claripy
— Solver Engine#
Realistically, you should never have to work with in-depth claripy APIs unless you’re doing some hard-core analysis. Most of the time, you’ll be using claripy as a simple frontend to z3:
import claripy
a = claripy.BVS("sym_val", 32)
b = claripy.RotateLeft(a, 8)
c = b + 4
s = claripy.Solver()
s.add(c == 0x41424344)
assert s.eval(c, 1)[0] == 0x41424344
assert s.eval(a, 1)[0] == 0x40414243
Or using its components in angr:
import angr, claripy
b = angr.Project('/bin/true')
path = b.factory.path()
rax_start = claripy.BVS('rax_start', 64)
path.state.regs.rax = rax_start
path_new = path.step()[0]
rax_new = path_new.state.regs.rax
path_new.state.se.add(rax_new == 1337)
print(path_new.state.se.eval(rax_start, 1)[0])
AST#
- class claripy.ast.Bits(op, args, add_variables=None, hash=None, **kwargs)[source]#
Bases:
Base
A base class for AST types that can be stored as a series of bits. Currently, this is bitvectors and IEEE floats.
- Variables:
length – The length of this value in bits.
- op#
- args#
- variables#
- symbolic#
- annotations#
- simplifiable#
- depth#
- class claripy.ast.BV(op, args, add_variables=None, hash=None, **kwargs)[source]#
Bases:
Bits
A class representing an AST of operations culminating in a bitvector. Do not instantiate this class directly, instead use BVS or BVV to construct a symbol or value, and then use operations to construct more complicated expressions.
Individual sub-bits and bit-ranges can be extracted from a bitvector using index and slice notation. Bits are indexed weirdly. For a 32-bit AST:
a[31] is the LEFT most bit, so it’d be the 0 in
01111111111111111111111111111111
a[0] is the RIGHT most bit, so it’d be the 0 in
11111111111111111111111111111110
a[31:30] are the two leftmost bits, so they’d be the 0s in:
00111111111111111111111111111111
a[1:0] are the two rightmost bits, so they’d be the 0s in:
11111111111111111111111111111100
- chop(bits=1)[source]#
Chops a BV into consecutive sub-slices. Obviously, the length of this BV must be a multiple of bits.
- Returns:
A list of smaller bitvectors, each
bits
in length. The first one will be the left-most (i.e. most significant) bits.
- get_byte(index)[source]#
Extracts a byte from a BV, where the index refers to the byte in a big-endian order
- Parameters:
index – the byte to extract
- Returns:
An 8-bit BV
- get_bytes(index, size)[source]#
Extracts several bytes from a bitvector, where the index refers to the byte in a big-endian order
- Parameters:
index – the byte index at which to start extracting
size – the number of bytes to extract
- Returns:
A BV of size
size * 8
- zero_extend(n)[source]#
Zero-extends the bitvector by n bits. So:
a = BVV(0b1111, 4) b = a.zero_extend(4) b is BVV(0b00001111)
- sign_extend(n)[source]#
Sign-extends the bitvector by n bits. So:
a = BVV(0b1111, 4) b = a.sign_extend(4) b is BVV(0b11111111)
- concat(*args)[source]#
Concatenates this bitvector with the bitvectors provided. This bitvector will be on the far-left, i.e. the most significant bits.
- val_to_fp(sort, signed=True, rm=None)[source]#
Interpret this bitvector as an integer, and return the floating-point representation of that integer.
- Parameters:
sort – The sort of floating point value to return
signed – Optional: whether this value is a signed integer
rm – Optional: the rounding mode to use
- Returns:
An FP AST whose value is the same as this BV
- raw_to_fp()[source]#
Interpret the bits of this bitvector as an IEEE754 floating point number. The inverse of this function is raw_to_bv.
- Returns:
An FP AST whose bit-pattern is the same as this BV
- static Concat(*args)#
- static Extract(*args)#
- LShR()#
- SDiv()#
- SGE()#
- SGT()#
- SLE()#
- SLT()#
- SMod()#
- UGE()#
- UGT()#
- ULE()#
- ULT()#
- intersection()#
- property reversed#
- union()#
- widen()#
- class claripy.ast.FP(op, args, add_variables=None, hash=None, **kwargs)[source]#
Bases:
Bits
An AST representing a set of operations culminating in an IEEE754 floating point number.
Do not instantiate this class directly, instead use FPV or FPS to construct a value or symbol, and then use operations to construct more complicated expressions.
- Variables:
length – The length of this value
sort – The sort of this value, usually either FSORT_FLOAT or FSORT_DOUBLE
- to_fp(sort, rm=None)[source]#
Convert this float to a different sort
- Parameters:
sort – The sort to convert to
rm – Optional: The rounding mode to use
- Returns:
An FP AST
- raw_to_bv()[source]#
Interpret the bit-pattern of this IEEE754 floating point number as a bitvector. The inverse of this function is to_bv.
- Returns:
A BV AST whose bit-pattern is the same as this FP
- val_to_bv(size, signed=True, rm=None)[source]#
Convert this floating point value to an integer.
- Parameters:
size – The size of the bitvector to return
signed – Optional: Whether the target integer is signed
rm – Optional: The rounding mode to use
- Returns:
A bitvector whose value is the rounded version of this FP’s value
- property sort#
- Sqrt()#
- isInf()#
- isNaN()#
- class claripy.ast.Bool(op, args, add_variables=None, hash=None, **kwargs)[source]#
Bases:
Base
- is_true()[source]#
Returns True if ‘self’ can be easily determined to be True. Otherwise, return False. Note that the AST might still be True (i.e., if it were simplified via Z3), but it’s hard to quickly tell that.
- is_false()[source]#
Returns True if ‘self’ can be easily determined to be False. Otherwise, return False. Note that the AST might still be False (i.e., if it were simplified via Z3), but it’s hard to quickly tell that.
- intersection()#
- class claripy.ast.Base(op, args, add_variables=None, hash=None, **kwargs)[source]#
Bases:
object
This is the base class of all claripy ASTs. An AST tracks a tree of operations on arguments.
This class should not be instanciated directly - instead, use one of the constructor functions (BVS, BVV, FPS, FPV…) to construct a leaf node and then build more complicated expressions using operations.
AST objects have hash identity. This means that an AST that has the same hash as another AST will be the same object. This is critical for efficient memory usage. As an example, the following is true:
a, b = two different ASTs c = b + a d = b + a assert c is d
- Variables:
op – The operation that is being done on the arguments
args – The arguments that are being used
- FULL_SIMPLIFY = 1#
- LITE_SIMPLIFY = 2#
- UNSIMPLIFIED = 0#
- LITE_REPR = 0#
- MID_REPR = 1#
- FULL_REPR = 2#
- property cache_key: ASTCacheKey[T]#
A key that refers to this AST - this value is appropriate for usage as a key in dictionaries.
- make_like(op, args, **kwargs)[source]#
- Return type:
TypeVar
(T
, bound= Base)- Parameters:
self (T) –
op (str) –
args (Iterable) –
- append_annotation(a)[source]#
Appends an annotation to this AST.
- Parameters:
a (
Annotation
) – the annotation to appendself (T) –
- Return type:
TypeVar
(T
, bound= Base)- Returns:
a new AST, with the annotation added
- append_annotations(new_tuple)[source]#
Appends several annotations to this AST.
- Parameters:
new_tuple (
Tuple
[Annotation
,...
]) – the tuple of annotations to appendself (T) –
- Return type:
TypeVar
(T
, bound= Base)- Returns:
a new AST, with the annotations added
- annotate(*args, remove_annotations=None)[source]#
Appends annotations to this AST.
- Parameters:
args (
Annotation
) – the tuple of annotations to append (variadic positional args)remove_annotations (
Optional
[Iterable
[Annotation
]]) – annotations to removeself (T) –
- Return type:
TypeVar
(T
, bound= Base)- Returns:
a new AST, with the annotations added
- insert_annotation(a)[source]#
Inserts an annotation to this AST.
- Parameters:
a (
Annotation
) – the annotation to insertself (T) –
- Return type:
TypeVar
(T
, bound= Base)- Returns:
a new AST, with the annotation added
- insert_annotations(new_tuple)[source]#
Inserts several annotations to this AST.
- Parameters:
new_tuple (
Tuple
[Annotation
,...
]) – the tuple of annotations to insertself (T) –
- Return type:
TypeVar
(T
, bound= Base)- Returns:
a new AST, with the annotations added
- replace_annotations(new_tuple)[source]#
Replaces annotations on this AST.
- Parameters:
new_tuple (
Tuple
[Annotation
,...
]) – the tuple of annotations to replace the old annotations withself (T) –
- Return type:
TypeVar
(T
, bound= Base)- Returns:
a new AST, with the annotations added
- remove_annotation(a)[source]#
Removes an annotation from this AST.
- Parameters:
a (
Annotation
) – the annotation to removeself (T) –
- Return type:
TypeVar
(T
, bound= Base)- Returns:
a new AST, with the annotation removed
- remove_annotations(remove_sequence)[source]#
Removes several annotations from this AST.
- Parameters:
remove_sequence (
Iterable
[Annotation
]) – a sequence/set of the annotations to removeself (T) –
- Return type:
TypeVar
(T
, bound= Base)- Returns:
a new AST, with the annotations removed
- shallow_repr(max_depth=8, explicit_length=False, details=0, inner=False, parent_prec=15, left=True)[source]#
Returns a string representation of this AST, but with a maximum depth to prevent floods of text being printed.
- Parameters:
max_depth – The maximum depth to print.
explicit_length – Print lengths of BVV arguments.
details – An integer value specifying how detailed the output should be: LITE_REPR - print short repr for both operations and BVs, MID_REPR - print full repr for operations and short for BVs, FULL_REPR - print full repr of both operations and BVs.
inner – whether or not it is an inner AST
parent_prec – parent operation precedence level
left – whether or not it is a left AST
- Return type:
str
- Returns:
A string representing the AST
- children_asts()[source]#
Return an iterator over the nested children ASTs.
- Return type:
Iterator
[Base
]
- property recursive_children_asts#
Use children_asts() instead.
- Type:
DEPRECATED
- property recursive_leaf_asts#
Use leaf_asts() instead.
- Type:
DEPRECATED
- swap_args(new_args, new_length=None, **kwargs)[source]#
This returns the same AST, with the arguments swapped out for new_args.
- Return type:
TypeVar
(T
, bound= Base)- Parameters:
self (T) –
- split(split_on)[source]#
Splits the AST if its operation is split_on (i.e., return all the arguments). Otherwise, return a list with just the AST.
- Return type:
List
- Parameters:
split_on (Iterable[str]) –
- structurally_match(o)[source]#
Structurally compares two A objects, and check if their corresponding leaves are definitely the same A object (name-wise or hash-identity wise).
- Parameters:
o (
TypeVar
(T
, bound= Base)) – the other claripy A objectself (T) –
- Return type:
- Returns:
True/False
- replace_dict(replacements, variable_set=None, leaf_operation=None)[source]#
Returns this AST with subexpressions replaced by those that can be found in replacements dict.
- Parameters:
variable_set – For optimization, ast’s without these variables are not checked for replacing.
replacements – A dictionary of hashes to their replacements.
leaf_operation – An operation that should be applied to the leaf nodes.
self (T) –
- Return type:
TypeVar
(T
, bound= Base)- Returns:
An AST with all instances of ast’s in replacements.
- replace(old, new, variable_set=None, leaf_operation=None)[source]#
Returns this AST but with the AST ‘old’ replaced with AST ‘new’ in its subexpressions.
- Return type:
TypeVar
(T
, bound= Base)- Parameters:
self (T) –
- canonicalize(var_map=None, counter=None)[source]#
- Return type:
TypeVar
(T
, bound= Base)- Parameters:
self (T) –
- property ite_burrowed: T#
Returns an equivalent AST that “burrows” the ITE expressions as deep as possible into the ast, for simpler printing.
- property ite_excavated: T#
Returns an equivalent AST that “excavates” the ITE expressions out as far as possible toward the root of the AST, for processing in static analyses.
- property concrete_value#
- property cv#
- property v#
- property singlevalued: bool#
- property multivalued: bool#
- property cardinality: int#
- property concrete: bool#
- property uninitialized: bool#
Whether this AST comes from an uninitialized dereference or not. It’s only used in under-constrained symbolic execution mode.
- Returns:
True/False/None (unspecified).
- property uc_alloc_depth: int#
The depth of allocation by lazy-initialization. It’s only used in under-constrained symbolic execution mode.
- Returns:
An integer indicating the allocation depth, or None if it’s not from lazy-initialization.
- to_claripy()[source]#
Returns itself. Provides compatibility with other classes (such as SimActionObject) which provide a similar method to unwrap to an AST.
- Return type:
TypeVar
(T
, bound= Base)- Parameters:
self (T) –
- op#
- args#
- variables#
- symbolic#
- length#
- annotations#
- simplifiable#
- depth#
- class claripy.ast.String(*args, length, **kwargs)[source]#
Bases:
Bits
Base class that represent the AST of a String object and implements all the operation useful to create and modify the AST.
Do not instantiate this class directly, instead use StringS or StringV to construct a symbol or value, and then use operations to construct more complicated expressions.
- STRING_TYPE_IDENTIFIER = 'STRING_'#
- GENERATED_BVS_IDENTIFIER = 'BVS_'#
- MAX_LENGTH = 10000#
- strReplace(str_to_replace, replacement)[source]#
Replace the first occurence of str_to_replace with replacement
- Parameters:
str_to_replace (claripy.ast.String) – pattern that has to be replaced
replacement (claripy.ast.String) – replacement pattern
- toInt(bitlength)[source]#
Convert the string to a bitvector holding the integer representation of the string
- Parameters:
bitlength – size of the biitvector holding the result
- indexOf(pattern, start_idx, bitlength)[source]#
Return the start index of the pattern inside the input string in a Bitvector representation, otherwise it returns -1 (always using a BitVector)
- Parameters:
bitlength – size of the biitvector holding the result
- static IntToStr(*args)#
- static StrConcat(*args)#
- static StrContains(*args)#
- static StrIndexOf(*args)#
- static StrIsDigit(*args)#
- static StrLen(*args)#
- static StrPrefixOf(*args)#
- static StrReplace(*args)#
- static StrSubstr(*args)#
- static StrSuffixOf(*args)#
- static StrToInt(*args)#
- claripy.ast.base.from_iterable(iterable, /)#
Alternative chain() constructor taking a single iterable argument that evaluates lazily.
- class claripy.ast.base.Base(op, args, add_variables=None, hash=None, **kwargs)[source]#
Bases:
object
This is the base class of all claripy ASTs. An AST tracks a tree of operations on arguments.
This class should not be instanciated directly - instead, use one of the constructor functions (BVS, BVV, FPS, FPV…) to construct a leaf node and then build more complicated expressions using operations.
AST objects have hash identity. This means that an AST that has the same hash as another AST will be the same object. This is critical for efficient memory usage. As an example, the following is true:
a, b = two different ASTs c = b + a d = b + a assert c is d
- Variables:
op – The operation that is being done on the arguments
args – The arguments that are being used
- FULL_SIMPLIFY = 1#
- LITE_SIMPLIFY = 2#
- UNSIMPLIFIED = 0#
- LITE_REPR = 0#
- MID_REPR = 1#
- FULL_REPR = 2#
- property cache_key: ASTCacheKey[T]#
A key that refers to this AST - this value is appropriate for usage as a key in dictionaries.
- make_like(op, args, **kwargs)[source]#
- Return type:
TypeVar
(T
, bound= Base)- Parameters:
self (T) –
op (str) –
args (Iterable) –
- append_annotation(a)[source]#
Appends an annotation to this AST.
- Parameters:
a (
Annotation
) – the annotation to appendself (T) –
- Return type:
TypeVar
(T
, bound= Base)- Returns:
a new AST, with the annotation added
- append_annotations(new_tuple)[source]#
Appends several annotations to this AST.
- Parameters:
new_tuple (
Tuple
[Annotation
,...
]) – the tuple of annotations to appendself (T) –
- Return type:
TypeVar
(T
, bound= Base)- Returns:
a new AST, with the annotations added
- annotate(*args, remove_annotations=None)[source]#
Appends annotations to this AST.
- Parameters:
args (
Annotation
) – the tuple of annotations to append (variadic positional args)remove_annotations (
Optional
[Iterable
[Annotation
]]) – annotations to removeself (T) –
- Return type:
TypeVar
(T
, bound= Base)- Returns:
a new AST, with the annotations added
- insert_annotation(a)[source]#
Inserts an annotation to this AST.
- Parameters:
a (
Annotation
) – the annotation to insertself (T) –
- Return type:
TypeVar
(T
, bound= Base)- Returns:
a new AST, with the annotation added
- insert_annotations(new_tuple)[source]#
Inserts several annotations to this AST.
- Parameters:
new_tuple (
Tuple
[Annotation
,...
]) – the tuple of annotations to insertself (T) –
- Return type:
TypeVar
(T
, bound= Base)- Returns:
a new AST, with the annotations added
- replace_annotations(new_tuple)[source]#
Replaces annotations on this AST.
- Parameters:
new_tuple (
Tuple
[Annotation
,...
]) – the tuple of annotations to replace the old annotations withself (T) –
- Return type:
TypeVar
(T
, bound= Base)- Returns:
a new AST, with the annotations added
- remove_annotation(a)[source]#
Removes an annotation from this AST.
- Parameters:
a (
Annotation
) – the annotation to removeself (T) –
- Return type:
TypeVar
(T
, bound= Base)- Returns:
a new AST, with the annotation removed
- remove_annotations(remove_sequence)[source]#
Removes several annotations from this AST.
- Parameters:
remove_sequence (
Iterable
[Annotation
]) – a sequence/set of the annotations to removeself (T) –
- Return type:
TypeVar
(T
, bound= Base)- Returns:
a new AST, with the annotations removed
- shallow_repr(max_depth=8, explicit_length=False, details=0, inner=False, parent_prec=15, left=True)[source]#
Returns a string representation of this AST, but with a maximum depth to prevent floods of text being printed.
- Parameters:
max_depth – The maximum depth to print.
explicit_length – Print lengths of BVV arguments.
details – An integer value specifying how detailed the output should be: LITE_REPR - print short repr for both operations and BVs, MID_REPR - print full repr for operations and short for BVs, FULL_REPR - print full repr of both operations and BVs.
inner – whether or not it is an inner AST
parent_prec – parent operation precedence level
left – whether or not it is a left AST
- Return type:
str
- Returns:
A string representing the AST
- children_asts()[source]#
Return an iterator over the nested children ASTs.
- Return type:
Iterator
[Base
]
- property recursive_children_asts#
Use children_asts() instead.
- Type:
DEPRECATED
- property recursive_leaf_asts#
Use leaf_asts() instead.
- Type:
DEPRECATED
- swap_args(new_args, new_length=None, **kwargs)[source]#
This returns the same AST, with the arguments swapped out for new_args.
- Return type:
TypeVar
(T
, bound= Base)- Parameters:
self (T) –
- split(split_on)[source]#
Splits the AST if its operation is split_on (i.e., return all the arguments). Otherwise, return a list with just the AST.
- Return type:
List
- Parameters:
split_on (Iterable[str]) –
- structurally_match(o)[source]#
Structurally compares two A objects, and check if their corresponding leaves are definitely the same A object (name-wise or hash-identity wise).
- Parameters:
o (
TypeVar
(T
, bound= Base)) – the other claripy A objectself (T) –
- Return type:
bool
- Returns:
True/False
- replace_dict(replacements, variable_set=None, leaf_operation=None)[source]#
Returns this AST with subexpressions replaced by those that can be found in replacements dict.
- Parameters:
variable_set – For optimization, ast’s without these variables are not checked for replacing.
replacements – A dictionary of hashes to their replacements.
leaf_operation – An operation that should be applied to the leaf nodes.
self (T) –
- Return type:
TypeVar
(T
, bound= Base)- Returns:
An AST with all instances of ast’s in replacements.
- replace(old, new, variable_set=None, leaf_operation=None)[source]#
Returns this AST but with the AST ‘old’ replaced with AST ‘new’ in its subexpressions.
- Return type:
TypeVar
(T
, bound= Base)- Parameters:
self (T) –
- canonicalize(var_map=None, counter=None)[source]#
- Return type:
TypeVar
(T
, bound= Base)- Parameters:
self (T) –
- property ite_burrowed: T#
Returns an equivalent AST that “burrows” the ITE expressions as deep as possible into the ast, for simpler printing.
- property ite_excavated: T#
Returns an equivalent AST that “excavates” the ITE expressions out as far as possible toward the root of the AST, for processing in static analyses.
- property concrete_value#
- property cv#
- property v#
- property singlevalued: bool#
- property multivalued: bool#
- property cardinality: int#
- property concrete: bool#
- property uninitialized: bool#
Whether this AST comes from an uninitialized dereference or not. It’s only used in under-constrained symbolic execution mode.
- Returns:
True/False/None (unspecified).
- property uc_alloc_depth: int#
The depth of allocation by lazy-initialization. It’s only used in under-constrained symbolic execution mode.
- Returns:
An integer indicating the allocation depth, or None if it’s not from lazy-initialization.
- to_claripy()[source]#
Returns itself. Provides compatibility with other classes (such as SimActionObject) which provide a similar method to unwrap to an AST.
- Return type:
TypeVar
(T
, bound= Base)- Parameters:
self (T) –
- op#
- args#
- variables#
- symbolic#
- length#
- annotations#
- simplifiable#
- depth#
- class claripy.ast.bits.Bits(op, args, add_variables=None, hash=None, **kwargs)[source]#
Bases:
Base
A base class for AST types that can be stored as a series of bits. Currently, this is bitvectors and IEEE floats.
- Variables:
length – The length of this value in bits.
-
length:
int
#
- op#
- args#
- variables#
- symbolic#
- annotations#
- simplifiable#
- depth#
- class claripy.ast.bool.Bool(op, args, add_variables=None, hash=None, **kwargs)[source]#
Bases:
Base
- is_true()[source]#
Returns True if ‘self’ can be easily determined to be True. Otherwise, return False. Note that the AST might still be True (i.e., if it were simplified via Z3), but it’s hard to quickly tell that.
- is_false()[source]#
Returns True if ‘self’ can be easily determined to be False. Otherwise, return False. Note that the AST might still be False (i.e., if it were simplified via Z3), but it’s hard to quickly tell that.
- intersection()#
- claripy.ast.bool.BoolS(name, explicit_name=None)[source]#
Creates a boolean symbol (i.e., a variable).
- Parameters:
name – The name of the symbol
explicit_name – If False, an identifier is appended to the name to ensure uniqueness.
- Return type:
- Returns:
A Bool object representing this symbol.
- claripy.ast.bool.ite_dict(i, d, default)[source]#
Return an expression of if-then-else trees which expresses a switch tree :type i: :param i: The variable which may take on multiple values affecting the final result :type d: :param d: A dict mapping possible values for i to values which the result could be :type default: :param default: A default value that the expression should take on if i matches none of the keys of d :return: An expression encoding the result of the above
- claripy.ast.bool.ite_cases(cases, default)[source]#
Return an expression of if-then-else trees which expresses a series of alternatives
- Parameters:
cases – A list of tuples (c, v). c is the condition under which v should be the result of the expression
default – A default value that the expression should take on if none of the c conditions are satisfied
- Returns:
An expression encoding the result of the above
- claripy.ast.bool.reverse_ite_cases(ast)[source]#
Given an expression created by ite_cases, produce the cases that generated it :type ast: :param ast: :return:
- claripy.ast.bool.constraint_to_si(expr)[source]#
Convert a constraint to SI if possible.
- Parameters:
expr –
- Returns:
- class claripy.ast.bv.BV(op, args, add_variables=None, hash=None, **kwargs)[source]#
Bases:
Bits
A class representing an AST of operations culminating in a bitvector. Do not instantiate this class directly, instead use BVS or BVV to construct a symbol or value, and then use operations to construct more complicated expressions.
Individual sub-bits and bit-ranges can be extracted from a bitvector using index and slice notation. Bits are indexed weirdly. For a 32-bit AST:
a[31] is the LEFT most bit, so it’d be the 0 in
01111111111111111111111111111111
a[0] is the RIGHT most bit, so it’d be the 0 in
11111111111111111111111111111110
a[31:30] are the two leftmost bits, so they’d be the 0s in:
00111111111111111111111111111111
a[1:0] are the two rightmost bits, so they’d be the 0s in:
11111111111111111111111111111100
- chop(bits=1)[source]#
Chops a BV into consecutive sub-slices. Obviously, the length of this BV must be a multiple of bits.
- Returns:
A list of smaller bitvectors, each
bits
in length. The first one will be the left-most (i.e. most significant) bits.
- get_byte(index)[source]#
Extracts a byte from a BV, where the index refers to the byte in a big-endian order
- Parameters:
index – the byte to extract
- Returns:
An 8-bit BV
- get_bytes(index, size)[source]#
Extracts several bytes from a bitvector, where the index refers to the byte in a big-endian order
- Parameters:
index – the byte index at which to start extracting
size – the number of bytes to extract
- Returns:
A BV of size
size * 8
- zero_extend(n)[source]#
Zero-extends the bitvector by n bits. So:
a = BVV(0b1111, 4) b = a.zero_extend(4) b is BVV(0b00001111)
- sign_extend(n)[source]#
Sign-extends the bitvector by n bits. So:
a = BVV(0b1111, 4) b = a.sign_extend(4) b is BVV(0b11111111)
- concat(*args)[source]#
Concatenates this bitvector with the bitvectors provided. This bitvector will be on the far-left, i.e. the most significant bits.
- val_to_fp(sort, signed=True, rm=None)[source]#
Interpret this bitvector as an integer, and return the floating-point representation of that integer.
- Parameters:
sort – The sort of floating point value to return
signed – Optional: whether this value is a signed integer
rm – Optional: the rounding mode to use
- Returns:
An FP AST whose value is the same as this BV
- raw_to_fp()[source]#
Interpret the bits of this bitvector as an IEEE754 floating point number. The inverse of this function is raw_to_bv.
- Returns:
An FP AST whose bit-pattern is the same as this BV
- static Concat(*args)#
- static Extract(*args)#
- LShR()#
- SDiv()#
- SGE()#
- SGT()#
- SLE()#
- SLT()#
- SMod()#
- UGE()#
- UGT()#
- ULE()#
- ULT()#
- intersection()#
- property reversed#
- union()#
- widen()#
- claripy.ast.bv.BVS(name, size, min=None, max=None, stride=None, uninitialized=False, explicit_name=None, discrete_set=False, discrete_set_max_card=None, **kwargs)[source]#
Creates a bit-vector symbol (i.e., a variable).
If you want to specify the maximum or minimum value of a normal symbol that is not part of value-set analysis, you should manually add constraints to that effect. Do not use ``min`` and ``max`` for symbolic execution.
- Parameters:
name – The name of the symbol.
size – The size (in bits) of the bit-vector.
min – The minimum value of the symbol, used only for value-set analysis
max – The maximum value of the symbol, used only for value-set analysis
stride – The stride of the symbol, used only for value-set analysis
uninitialized – Whether this value should be counted as an “uninitialized” value in the course of an analysis.
explicit_name (bool) – If False, an identifier is appended to the name to ensure uniqueness.
discrete_set (bool) – If True, a DiscreteStridedIntervalSet will be used instead of a normal StridedInterval.
discrete_set_max_card (int) – The maximum cardinality of the discrete set. It is ignored if discrete_set is set to False or None.
- Return type:
- Returns:
a BV object representing this symbol.
- claripy.ast.bv.BVV(value, size=None, **kwargs)[source]#
Creates a bit-vector value (i.e., a concrete value).
- Parameters:
value – The value. Either an integer or a bytestring. If it’s the latter, it will be interpreted as the bytes of a big-endian constant.
size – The size (in bits) of the bit-vector. Optional if you provide a string, required for an integer.
- Return type:
- Returns:
A BV object representing this value.
- claripy.ast.bv.SI(name=None, bits=0, lower_bound=None, upper_bound=None, stride=None, to_conv=None, explicit_name=None, discrete_set=False, discrete_set_max_card=None)[source]#
- claripy.ast.bv.ValueSet(bits, region=None, region_base_addr=None, value=None, name=None, val=None)[source]#
- claripy.ast.bv.VS(bits, region=None, region_base_addr=None, value=None, name=None, val=None)#
- claripy.ast.bv.DSIS(name=None, bits=0, lower_bound=None, upper_bound=None, stride=None, explicit_name=None, to_conv=None, max_card=None)[source]#
- class claripy.ast.fp.FP(op, args, add_variables=None, hash=None, **kwargs)[source]#
Bases:
Bits
An AST representing a set of operations culminating in an IEEE754 floating point number.
Do not instantiate this class directly, instead use FPV or FPS to construct a value or symbol, and then use operations to construct more complicated expressions.
- Variables:
length – The length of this value
sort – The sort of this value, usually either FSORT_FLOAT or FSORT_DOUBLE
- to_fp(sort, rm=None)[source]#
Convert this float to a different sort
- Parameters:
sort – The sort to convert to
rm – Optional: The rounding mode to use
- Returns:
An FP AST
- raw_to_bv()[source]#
Interpret the bit-pattern of this IEEE754 floating point number as a bitvector. The inverse of this function is to_bv.
- Returns:
A BV AST whose bit-pattern is the same as this FP
- val_to_bv(size, signed=True, rm=None)[source]#
Convert this floating point value to an integer.
- Parameters:
size – The size of the bitvector to return
signed – Optional: Whether the target integer is signed
rm – Optional: The rounding mode to use
- Returns:
A bitvector whose value is the rounded version of this FP’s value
- property sort#
- Sqrt()#
- isInf()#
- isNaN()#
- claripy.ast.fp.FPS(name, sort, explicit_name=None)[source]#
Creates a floating-point symbol.
- Parameters:
name – The name of the symbol
sort – The sort of the floating point
explicit_name – If False, an identifier is appended to the name to ensure uniqueness.
- Returns:
An FP AST.
- claripy.ast.fp.FPV(value, sort)[source]#
Creates a concrete floating-point value.
- Parameters:
value – The value of the floating point.
sort – The sort of the floating point.
- Returns:
An FP AST.
- class claripy.ast.strings.String(*args, length, **kwargs)[source]#
Bases:
Bits
Base class that represent the AST of a String object and implements all the operation useful to create and modify the AST.
Do not instantiate this class directly, instead use StringS or StringV to construct a symbol or value, and then use operations to construct more complicated expressions.
- STRING_TYPE_IDENTIFIER = 'STRING_'#
- GENERATED_BVS_IDENTIFIER = 'BVS_'#
- MAX_LENGTH = 10000#
-
string_length:
int
#
- strReplace(str_to_replace, replacement)[source]#
Replace the first occurence of str_to_replace with replacement
- Parameters:
str_to_replace (claripy.ast.String) – pattern that has to be replaced
replacement (claripy.ast.String) – replacement pattern
- toInt(bitlength)[source]#
Convert the string to a bitvector holding the integer representation of the string
- Parameters:
bitlength – size of the biitvector holding the result
- indexOf(pattern, start_idx, bitlength)[source]#
Return the start index of the pattern inside the input string in a Bitvector representation, otherwise it returns -1 (always using a BitVector)
- Parameters:
bitlength – size of the biitvector holding the result
- static IntToStr(*args)#
- static StrConcat(*args)#
- static StrContains(*args)#
- static StrIndexOf(*args)#
- static StrIsDigit(*args)#
- static StrLen(*args)#
- static StrPrefixOf(*args)#
- static StrReplace(*args)#
- static StrSubstr(*args)#
- static StrSuffixOf(*args)#
- static StrToInt(*args)#
- claripy.ast.strings.StringS(name, size, uninitialized=False, explicit_name=False, **kwargs)[source]#
Create a new symbolic string (analogous to z3.String())
- Parameters:
name – The name of the symbolic string (i. e. the name of the variable)
size – The size in bytes of the string (i. e. the length of the string)
uninitialized – Whether this value should be counted as an “uninitialized” value in the course of an analysis.
explicit_name (bool) – If False, an identifier is appended to the name to ensure uniqueness.
- Returns:
The String object representing the symbolic string
- claripy.ast.strings.StringV(value, length=None, **kwargs)[source]#
Create a new Concrete string (analogous to z3.StringVal())
- Parameters:
value – The constant value of the concrete string
length (
Optional
[int
]) – The byte length of the string
- Returns:
The String object representing the concrete string
Backends#
- class claripy.backends.Backend(solver_required=None)[source]#
Bases:
object
Backends are Claripy’s workhorses. Claripy exposes ASTs (claripy.ast.Base objects) to the world, but when actual computation has to be done, it pushes those ASTs into objects that can be handled by the backends themselves. This provides a unified interface to the outside world while allowing Claripy to support different types of computation. For example, BackendConcrete provides computation support for concrete bitvectors and booleans, BackendVSA introduces VSA constructs such as StridedIntervals (and details what happens when operations are performed on them), and BackendZ3 provides support for symbolic variables and constraint solving.
There are a set of functions that a backend is expected to implement. For all of these functions, the “public” version is expected to be able to deal with claripy.ast.Base objects, while the “private” version should only deal with objects specific to the backend itself. This is distinguished with Python idioms: a public function will be named func() while a private function will be _func(). All functions should return objects that are usable by the backend in its private methods. If this can’t be done (i.e., some functionality is being attempted that the backend can’t handle), the backend should raise a BackendError. In this case, Claripy will move on to the next backend in its list.
All backends must implement a convert() function. This function receives a claripy.ast.Base and should return an object that the backend can handle in its private methods. Backends should also implement a _convert() method, which will receive anything that is not a claripy.ast.Base object (i.e., an integer or an object from a different backend). If convert() or _convert() receives something that the backend can’t translate to a format that is usable internally, the backend should raise BackendError, and thus won’t be used for that object.
Claripy contract with its backends is as follows: backends should be able to can handle, in their private functions, any object that they return from their private or public functions. Likewise, Claripy will never pass an object to any backend private function that did not originate as a return value from a private or public function of that backend. One exception to this is _convert(), as Claripy can try to stuff anything it feels like into _convert() to see if the backend can handle that type of object.
- property is_smt_backend#
- handles(expr)[source]#
Checks whether this backend can handle the expression.
- Parameters:
expr – The expression.
- Returns:
True if the backend can handle this expression, False if not.
- convert(expr)[source]#
Resolves a claripy.ast.Base into something usable by the backend.
- Parameters:
expr – The expression.
save – Save the result in the expression’s object cache
- Returns:
A backend object.
- call(op, args)[source]#
Calls operation op on args args with this backend.
- Returns:
A backend object representing the result.
- is_true(e, extra_constraints=(), solver=None, model_callback=None)[source]#
Should return True if e can be easily found to be True.
- Parameters:
e – The AST.
extra_constraints – Extra constraints (as ASTs) to add to the solver for this solve.
solver – A solver, for backends that require it.
model_callback – a function that will be executed with recovered models (if any)
- Returns:
A boolean.
- is_false(e, extra_constraints=(), solver=None, model_callback=None)[source]#
Should return True if e can be easily found to be False.
- Parameters:
e – The AST
extra_constraints – Extra constraints (as ASTs) to add to the solver for this solve.
solver – A solver, for backends that require it
model_callback – a function that will be executed with recovered models (if any)
- Returns:
A boolean.
- has_true(e, extra_constraints=(), solver=None, model_callback=None)[source]#
Should return True if e can possible be True.
- Parameters:
e – The AST.
extra_constraints – Extra constraints (as ASTs) to add to the solver for this solve.
solver – A solver, for backends that require it.
model_callback – a function that will be executed with recovered models (if any)
- Returns:
A boolean
- has_false(e, extra_constraints=(), solver=None, model_callback=None)[source]#
Should return False if e can possibly be False.
- Parameters:
e – The AST.
extra_constraints – Extra constraints (as ASTs) to add to the solver for this solve.
solver – A solver, for backends that require it.
model_callback – a function that will be executed with recovered models (if any)
- Returns:
A boolean.
- solver(timeout=None)[source]#
This function should return an instance of whatever object handles solving for this backend. For example, in Z3, this would be z3.Solver().
- add(s, c, track=False)[source]#
This function adds constraints to the backend solver.
- Parameters:
c – A sequence of ASTs
s – A backend solver object
track (bool) – True to enable constraint tracking, which is used in unsat_core()
- unsat_core(s)[source]#
This function returns the unsat core from the backend solver.
- Parameters:
s – A backend solver object.
- Returns:
The unsat core.
- eval(expr, n, extra_constraints=(), solver=None, model_callback=None)[source]#
This function returns up to n possible solutions for expression expr.
- Parameters:
expr – expression (an AST) to evaluate
n – number of results to return
solver – a solver object, native to the backend, to assist in the evaluation (for example, a z3.Solver)
extra_constraints – extra constraints (as ASTs) to add to the solver for this solve
model_callback – a function that will be executed with recovered models (if any)
- Returns:
A sequence of up to n results (backend objects)
- batch_eval(exprs, n, extra_constraints=(), solver=None, model_callback=None)[source]#
Evaluate one or multiple expressions.
- Parameters:
exprs – A list of expressions to evaluate.
n – Number of different solutions to return.
extra_constraints – Extra constraints (as ASTs) to add to the solver for this solve.
solver – A solver object, native to the backend, to assist in the evaluation.
model_callback – a function that will be executed with recovered models (if any)
- Returns:
A list of up to n tuples, where each tuple is a solution for all expressions.
- min(expr, extra_constraints=(), signed=False, solver=None, model_callback=None)[source]#
Return the minimum value of expr.
- Parameters:
expr – expression (an AST) to evaluate
solver – a solver object, native to the backend, to assist in the evaluation (for example, a z3.Solver)
extra_constraints – extra constraints (as ASTs) to add to the solver for this solve
signed – Whether to solve for the minimum signed integer instead of the unsigned min
model_callback – a function that will be executed with recovered models (if any)
- Returns:
the minimum possible value of expr (backend object)
- max(expr, extra_constraints=(), signed=False, solver=None, model_callback=None)[source]#
Return the maximum value of expr.
- Parameters:
expr – expression (an AST) to evaluate
solver – a solver object, native to the backend, to assist in the evaluation (for example, a z3.Solver)
extra_constraints – extra constraints (as ASTs) to add to the solver for this solve
signed – Whether to solve for the maximum signed integer instead of the unsigned max
model_callback – a function that will be executed with recovered models (if any)
- Returns:
the maximum possible value of expr (backend object)
- check_satisfiability(extra_constraints=(), solver=None, model_callback=None)[source]#
This function does a constraint check and returns the solvers state
- Parameters:
solver – The backend solver object.
extra_constraints – Extra constraints (as ASTs) to add to s for this solve
model_callback – a function that will be executed with recovered models (if any)
- Returns:
‘SAT’, ‘UNSAT’, or ‘UNKNOWN’
- satisfiable(extra_constraints=(), solver=None, model_callback=None)[source]#
This function does a constraint check and checks if the solver is in a sat state.
- Parameters:
solver – The backend solver object.
extra_constraints – Extra constraints (as ASTs) to add to s for this solve
model_callback – a function that will be executed with recovered models (if any)
- Returns:
True if sat, otherwise false
- solution(expr, v, extra_constraints=(), solver=None, model_callback=None)[source]#
Return True if v is a solution of expr with the extra constraints, False otherwise.
- Parameters:
expr – An expression (an AST) to evaluate
v – The proposed solution (an AST)
solver – A solver object, native to the backend, to assist in the evaluation (for example, a z3.Solver).
extra_constraints – Extra constraints (as ASTs) to add to the solver for this solve.
model_callback – a function that will be executed with recovered models (if any)
- Returns:
True if v is a solution of expr, False otherwise
- identical(a, b)[source]#
This should return whether a is identical to b. Of course, this isn’t always clear. True should mean that it is definitely identical. False eans that, conservatively, it might not be.
- Parameters:
a – an AST
b – another AST
- cardinality(a)[source]#
This should return the maximum number of values that an expression can take on. This should be a strict over approximation.
- Parameters:
a – The AST to evaluate
- Returns:
An integer
- class claripy.backend_object.BackendObject[source]#
Bases:
object
This is a base class for custom backend objects to implement.
It lets Claripy know that how to deal with those objects, in case they’re directly used in operations.
Backend objects that don’t derive from this class need to be wrapped in a type-I claripy.ast.Base.
- class claripy.backends.backend_concrete.BackendConcrete[source]#
Bases:
Backend
- is_true(e, extra_constraints=(), solver=None, model_callback=None)[source]#
Should return True if e can be easily found to be True.
- Parameters:
e – The AST.
extra_constraints – Extra constraints (as ASTs) to add to the solver for this solve.
solver – A solver, for backends that require it.
model_callback – a function that will be executed with recovered models (if any)
- Returns:
A boolean.
- is_false(e, extra_constraints=(), solver=None, model_callback=None)[source]#
Should return True if e can be easily found to be False.
- Parameters:
e – The AST
extra_constraints – Extra constraints (as ASTs) to add to the solver for this solve.
solver – A solver, for backends that require it
model_callback – a function that will be executed with recovered models (if any)
- Returns:
A boolean.
- claripy.backends.backend_z3.z3_expr_to_smt2(f, status='unknown', name='benchmark', logic='')[source]#
- class claripy.backends.backend_z3.SmartLRUCache(maxsize, getsizeof=None, evict=None)[source]#
Bases:
LRUCache
- class claripy.backends.backend_z3.BackendZ3(reuse_z3_solver=None, ast_cache_size=10000)[source]#
Bases:
Backend
- property extra_bvs_data#
- StringV(**kwargs)[source]#
The Z3 condom intercepts Z3Exceptions and throws a ClaripyZ3Error instead.
- StringS(**kwargs)[source]#
The Z3 condom intercepts Z3Exceptions and throws a ClaripyZ3Error instead.
- call(*args, **kwargs)[source]#
Calls operation op on args args with this backend.
- Returns:
A backend object representing the result.
- solver(timeout=None, max_memory=None)[source]#
This function should return an instance of whatever object handles solving for this backend. For example, in Z3, this would be z3.Solver().
- class claripy.backends.backend_z3_parallel.BackendZ3Parallel[source]#
Bases:
BackendZ3
- solver(*args, **kwargs)[source]#
This function should return an instance of whatever object handles solving for this backend. For example, in Z3, this would be z3.Solver().
- class claripy.backends.backend_vsa.BackendVSA[source]#
Bases:
Backend
- convert(expr)[source]#
Resolves a claripy.ast.Base into something usable by the backend.
- Parameters:
expr – The expression.
save – Save the result in the expression’s object cache
- Returns:
A backend object.
- apply_annotation(bo, annotation)[source]#
Apply an annotation on the backend object.
- Parameters:
bo (BackendObject) – The backend object.
annotation (Annotation) – The annotation to be applied
- Returns:
A new BackendObject
- Return type:
- static CreateStridedInterval(name=None, bits=0, stride=None, lower_bound=None, upper_bound=None, uninitialized=False, to_conv=None, discrete_set=False, discrete_set_max_cardinality=None)#
- Parameters:
name –
bits –
stride –
lower_bound –
upper_bound –
to_conv –
discrete_set (bool) –
discrete_set_max_cardinality (int) –
- Returns:
- class claripy.backends.backend_smtlib_solvers.z3str_popen.Z3StrProxy(timeout=None, max_memory=None)[source]#
Bases:
PopenSolverProxy
- class claripy.backends.backend_smtlib_solvers.z3str_popen.SolverBackendZ3Str(*args, **kwargs)[source]#
Bases:
SMTLibSolverBackend
- class claripy.backends.backend_smtlib_solvers.cvc4_popen.CVC4Proxy(timeout=None, max_memory=None)[source]#
Bases:
PopenSolverProxy
- class claripy.backends.backend_smtlib_solvers.cvc4_popen.SolverBackendCVC4(*args, **kwargs)[source]#
Bases:
SMTLibSolverBackend
- class claripy.backends.backend_smtlib_solvers.z3_popen.Z3Proxy(timeout=None, max_memory=None)[source]#
Bases:
PopenSolverProxy
- class claripy.backends.backend_smtlib_solvers.z3_popen.SolverBackendZ3(*args, **kwargs)[source]#
Bases:
SMTLibSolverBackend
- class claripy.backends.backend_smtlib_solvers.abc_popen.ABCProxy[source]#
Bases:
PopenSolverProxy
- class claripy.backends.backend_smtlib_solvers.abc_popen.SolverBackendABC(*args, **kwargs)[source]#
Bases:
SMTLibSolverBackend
- class claripy.backends.backend_smtlib_solvers.PopenSolverProxy(p)[source]#
Bases:
AbstractSMTLibSolverProxy
- class claripy.backends.backend_smtlib_solvers.SMTLibSolverBackend(*args, **kwargs)[source]#
Bases:
BackendSMTLibBase
- solver(timeout=None, max_memory=None)[source]#
This function should return an instance of whatever object handles solving for this backend. For example, in Z3, this would be z3.Solver().
- eval(expr, n, extra_constraints=(), solver=None, model_callback=None)[source]#
This function returns up to n possible solutions for expression expr.
- Parameters:
expr – expression (an AST) to evaluate
n – number of results to return
solver – a solver object, native to the backend, to assist in the evaluation (for example, a z3.Solver)
extra_constraints – extra constraints (as ASTs) to add to the solver for this solve
model_callback – a function that will be executed with recovered models (if any)
- Returns:
A sequence of up to n results (backend objects)
Frontends#
- class claripy.frontend.Frontend[source]#
Bases:
object
- eval_to_ast(e, n, extra_constraints=(), exact=None)[source]#
Evaluates expression e, returning a list of n concrete ASTs.
- Parameters:
e – the expression
n – the number of ASTs to return
extra_constraints – extra constraints to consider when performing the evaluation
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
list of concrete ASTs
- add(constraints)[source]#
Adds constraint(s) to constraints list.
- Parameters:
constraints – constraint(s) to add
- Returns:
- check_satisfiability(extra_constraints=(), exact=None)[source]#
Checks the satisfiability of stored constraints conjunction.
- Parameters:
extra_constraints – extra constraints to consider when checking satisfiability
exact – whether or not to perform exact checking. Ignored by non-approximating backends.
- Returns:
‘SAT’ if the conjunction is satisfiable otherwise ‘UNSAT’
- satisfiable(extra_constraints=(), exact=None)[source]#
Checks if stored constraints conjunction is satisfiable.
- Parameters:
extra_constraints – extra constraints to consider when checking satisfiability
exact – whether or not to perform exact checking. Ignored by non-approximating backends.
- Returns:
True if the conjunction is satisfiable otherwise False
- eval(e, n, extra_constraints=(), exact=None)[source]#
Evaluates expression e, returning a tuple of n solutions.
- Parameters:
e – the expression
n – the number of solutions to return
extra_constraints – extra constraints to consider when performing the evaluation
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
tuple of python primitives representing results
- batch_eval(exprs, n, extra_constraints=(), exact=None)[source]#
Evaluates exprs, returning a list of tuples (one tuple of n solutions for expression).
- Parameters:
exprs – expressions
n – the number of solutions to return
extra_constraints – extra constraints to consider when performing the evaluation
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
list of tuples of python primitives representing results
- max(e, extra_constraints=(), signed=False, exact=None)[source]#
Evaluates e, returning its max possible value.
- Parameters:
e – the expression
extra_constraints – extra constraints to consider when performing the evaluation
signed – whether the value should be treated as a signed integer
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
max possible value
- min(e, extra_constraints=(), signed=False, exact=None)[source]#
Evaluates e, returning its min possible value.
- Parameters:
e – the expression
extra_constraints – extra constraints to consider when performing the evaluation
signed – whether the value should be treated as a signed integer
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
min possible value
- solution(e, v, extra_constraints=(), exact=None)[source]#
Checks if v is a possible solution to e.
- Parameters:
e – the expression
v – the value
extra_constraints – extra constraints to consider when performing the evaluation
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
True if it is a possible solution otherwise False
- is_true(e, extra_constraints=(), exact=None)[source]#
Checks if e can only (and TRIVIALLY) evaluate to True. If this function returns True, then the expression cannot ever be False, regardless of constraints or anything else. If the expression returns False, then the expression might STILL not ever be False; it’s just that we can’t trivially prove it. In other words, a return value of False gives you no information whatsoever.
- Parameters:
e – the expression
extra_constraints – extra constraints to consider when performing the evaluation
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
True if it can only evaluate to True otherwise False
- is_false(e, extra_constraints=(), exact=None)[source]#
Checks if e can only (and TRIVIALLY) evaluate to False. If this function returns True, then the expression cannot ever be True, regardless of constraints or anything else. If the expression returns False, then the expression might STILL not ever be True; it’s just that we can’t trivially prove it. In other words, a return value of False gives you no information whatsoever.
- Parameters:
e – the expression
extra_constraints – extra constraints to consider when performing the evaluation
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
True if it can only evaluate to False otherwise False
- class claripy.frontends.composite_frontend.CompositeFrontend(template_frontend, template_frontend_string, track=False, **kwargs)[source]#
Bases:
ConstrainedFrontend
- property variables#
- add(constraints, **kwargs)[source]#
Adds constraint(s) to constraints list.
- Parameters:
constraints – constraint(s) to add
- Returns:
- check_satisfiability(extra_constraints=(), exact=None)[source]#
Checks the satisfiability of stored constraints conjunction.
- Parameters:
extra_constraints – extra constraints to consider when checking satisfiability
exact – whether or not to perform exact checking. Ignored by non-approximating backends.
- Returns:
‘SAT’ if the conjunction is satisfiable otherwise ‘UNSAT’
- satisfiable(extra_constraints=(), exact=None)[source]#
Checks if stored constraints conjunction is satisfiable.
- Parameters:
extra_constraints – extra constraints to consider when checking satisfiability
exact – whether or not to perform exact checking. Ignored by non-approximating backends.
- Returns:
True if the conjunction is satisfiable otherwise False
- eval(e, n, extra_constraints=(), exact=None)[source]#
Evaluates expression e, returning a tuple of n solutions.
- Parameters:
e – the expression
n – the number of solutions to return
extra_constraints – extra constraints to consider when performing the evaluation
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
tuple of python primitives representing results
- batch_eval(exprs, n, extra_constraints=(), exact=None)[source]#
Evaluates exprs, returning a list of tuples (one tuple of n solutions for expression).
- Parameters:
exprs – expressions
n – the number of solutions to return
extra_constraints – extra constraints to consider when performing the evaluation
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
list of tuples of python primitives representing results
- max(e, extra_constraints=(), signed=False, exact=None)[source]#
Evaluates e, returning its max possible value.
- Parameters:
e – the expression
extra_constraints – extra constraints to consider when performing the evaluation
signed – whether the value should be treated as a signed integer
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
max possible value
- min(e, extra_constraints=(), signed=False, exact=None)[source]#
Evaluates e, returning its min possible value.
- Parameters:
e – the expression
extra_constraints – extra constraints to consider when performing the evaluation
signed – whether the value should be treated as a signed integer
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
min possible value
- solution(e, v, extra_constraints=(), exact=None)[source]#
Checks if v is a possible solution to e.
- Parameters:
e – the expression
v – the value
extra_constraints – extra constraints to consider when performing the evaluation
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
True if it is a possible solution otherwise False
- is_true(e, extra_constraints=(), exact=None)[source]#
Checks if e can only (and TRIVIALLY) evaluate to True. If this function returns True, then the expression cannot ever be False, regardless of constraints or anything else. If the expression returns False, then the expression might STILL not ever be False; it’s just that we can’t trivially prove it. In other words, a return value of False gives you no information whatsoever.
- Parameters:
e – the expression
extra_constraints – extra constraints to consider when performing the evaluation
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
True if it can only evaluate to True otherwise False
- is_false(e, extra_constraints=(), exact=None)[source]#
Checks if e can only (and TRIVIALLY) evaluate to False. If this function returns True, then the expression cannot ever be True, regardless of constraints or anything else. If the expression returns False, then the expression might STILL not ever be True; it’s just that we can’t trivially prove it. In other words, a return value of False gives you no information whatsoever.
- Parameters:
e – the expression
extra_constraints – extra constraints to consider when performing the evaluation
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
True if it can only evaluate to False otherwise False
- property timeout#
- property max_memory#
- class claripy.frontends.constrained_frontend.ConstrainedFrontend[source]#
Bases:
Frontend
- add(constraints)[source]#
Adds constraint(s) to constraints list.
- Parameters:
constraints – constraint(s) to add
- Returns:
- check_satisfiability(extra_constraints=(), exact=None)[source]#
Checks the satisfiability of stored constraints conjunction.
- Parameters:
extra_constraints – extra constraints to consider when checking satisfiability
exact – whether or not to perform exact checking. Ignored by non-approximating backends.
- Returns:
‘SAT’ if the conjunction is satisfiable otherwise ‘UNSAT’
- satisfiable(extra_constraints=(), exact=None)[source]#
Checks if stored constraints conjunction is satisfiable.
- Parameters:
extra_constraints – extra constraints to consider when checking satisfiability
exact – whether or not to perform exact checking. Ignored by non-approximating backends.
- Returns:
True if the conjunction is satisfiable otherwise False
- batch_eval(exprs, n, extra_constraints=(), exact=None)[source]#
Evaluates exprs, returning a list of tuples (one tuple of n solutions for expression).
- Parameters:
exprs – expressions
n – the number of solutions to return
extra_constraints – extra constraints to consider when performing the evaluation
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
list of tuples of python primitives representing results
- eval(e, n, extra_constraints=(), exact=None)[source]#
Evaluates expression e, returning a tuple of n solutions.
- Parameters:
e – the expression
n – the number of solutions to return
extra_constraints – extra constraints to consider when performing the evaluation
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
tuple of python primitives representing results
- min(e, extra_constraints=(), signed=False, exact=None)[source]#
Evaluates e, returning its min possible value.
- Parameters:
e – the expression
extra_constraints – extra constraints to consider when performing the evaluation
signed – whether the value should be treated as a signed integer
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
min possible value
- max(e, extra_constraints=(), signed=False, exact=None)[source]#
Evaluates e, returning its max possible value.
- Parameters:
e – the expression
extra_constraints – extra constraints to consider when performing the evaluation
signed – whether the value should be treated as a signed integer
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
max possible value
- solution(e, v, extra_constraints=(), exact=None)[source]#
Checks if v is a possible solution to e.
- Parameters:
e – the expression
v – the value
extra_constraints – extra constraints to consider when performing the evaluation
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
True if it is a possible solution otherwise False
- is_true(e, extra_constraints=(), exact=None)[source]#
Checks if e can only (and TRIVIALLY) evaluate to True. If this function returns True, then the expression cannot ever be False, regardless of constraints or anything else. If the expression returns False, then the expression might STILL not ever be False; it’s just that we can’t trivially prove it. In other words, a return value of False gives you no information whatsoever.
- Parameters:
e – the expression
extra_constraints – extra constraints to consider when performing the evaluation
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
True if it can only evaluate to True otherwise False
- is_false(e, extra_constraints=(), exact=None)[source]#
Checks if e can only (and TRIVIALLY) evaluate to False. If this function returns True, then the expression cannot ever be True, regardless of constraints or anything else. If the expression returns False, then the expression might STILL not ever be True; it’s just that we can’t trivially prove it. In other words, a return value of False gives you no information whatsoever.
- Parameters:
e – the expression
extra_constraints – extra constraints to consider when performing the evaluation
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
True if it can only evaluate to False otherwise False
- class claripy.frontends.full_frontend.FullFrontend(solver_backend, timeout=None, max_memory=None, track=False, **kwargs)[source]#
Bases:
ConstrainedFrontend
- check_satisfiability(extra_constraints=(), exact=None)[source]#
Checks the satisfiability of stored constraints conjunction.
- Parameters:
extra_constraints – extra constraints to consider when checking satisfiability
exact – whether or not to perform exact checking. Ignored by non-approximating backends.
- Return type:
bool
- Returns:
‘SAT’ if the conjunction is satisfiable otherwise ‘UNSAT’
- satisfiable(extra_constraints=(), exact=None)[source]#
Checks if stored constraints conjunction is satisfiable.
- Parameters:
extra_constraints (
Iterable
[Bool
]) – extra constraints to consider when checking satisfiabilityexact (
Optional
[bool
]) – whether or not to perform exact checking. Ignored by non-approximating backends.
- Return type:
bool
- Returns:
True if the conjunction is satisfiable otherwise False
- eval(e, n, extra_constraints=(), exact=None)[source]#
Evaluates expression e, returning a tuple of n solutions.
- Parameters:
e – the expression
n – the number of solutions to return
extra_constraints – extra constraints to consider when performing the evaluation
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Return type:
Tuple
[Any
,...
]- Returns:
tuple of python primitives representing results
- batch_eval(exprs, n, extra_constraints=(), exact=None)[source]#
Evaluates exprs, returning a list of tuples (one tuple of n solutions for expression).
- Parameters:
exprs – expressions
n – the number of solutions to return
extra_constraints – extra constraints to consider when performing the evaluation
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
list of tuples of python primitives representing results
- max(e, extra_constraints=(), signed=False, exact=None)[source]#
Evaluates e, returning its max possible value.
- Parameters:
e – the expression
extra_constraints – extra constraints to consider when performing the evaluation
signed – whether the value should be treated as a signed integer
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
max possible value
- min(e, extra_constraints=(), signed=False, exact=None)[source]#
Evaluates e, returning its min possible value.
- Parameters:
e – the expression
extra_constraints – extra constraints to consider when performing the evaluation
signed – whether the value should be treated as a signed integer
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
min possible value
- solution(e, v, extra_constraints=(), exact=None)[source]#
Checks if v is a possible solution to e.
- Parameters:
e – the expression
v – the value
extra_constraints – extra constraints to consider when performing the evaluation
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
True if it is a possible solution otherwise False
- is_true(e, extra_constraints=(), exact=None)[source]#
Checks if e can only (and TRIVIALLY) evaluate to True. If this function returns True, then the expression cannot ever be False, regardless of constraints or anything else. If the expression returns False, then the expression might STILL not ever be False; it’s just that we can’t trivially prove it. In other words, a return value of False gives you no information whatsoever.
- Parameters:
- Return type:
bool
- Returns:
True if it can only evaluate to True otherwise False
- is_false(e, extra_constraints=(), exact=None)[source]#
Checks if e can only (and TRIVIALLY) evaluate to False. If this function returns True, then the expression cannot ever be True, regardless of constraints or anything else. If the expression returns False, then the expression might STILL not ever be True; it’s just that we can’t trivially prove it. In other words, a return value of False gives you no information whatsoever.
- Parameters:
- Return type:
bool
- Returns:
True if it can only evaluate to False otherwise False
- class claripy.frontends.hybrid_frontend.HybridFrontend(exact_frontend, approximate_frontend, approximate_first=False, **kwargs)[source]#
Bases:
Frontend
- property constraints#
- property variables#
- satisfiable(extra_constraints=(), exact=None)[source]#
Checks if stored constraints conjunction is satisfiable.
- Parameters:
extra_constraints – extra constraints to consider when checking satisfiability
exact – whether or not to perform exact checking. Ignored by non-approximating backends.
- Returns:
True if the conjunction is satisfiable otherwise False
- eval_to_ast(e, n, extra_constraints=(), exact=None)[source]#
Evaluates expression e, returning a list of n concrete ASTs.
- Parameters:
e – the expression
n – the number of ASTs to return
extra_constraints – extra constraints to consider when performing the evaluation
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
list of concrete ASTs
- eval(e, n, extra_constraints=(), exact=None)[source]#
Evaluates expression e, returning a tuple of n solutions.
- Parameters:
e – the expression
n – the number of solutions to return
extra_constraints – extra constraints to consider when performing the evaluation
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
tuple of python primitives representing results
- batch_eval(e, n, extra_constraints=(), exact=None)[source]#
Evaluates exprs, returning a list of tuples (one tuple of n solutions for expression).
- Parameters:
exprs – expressions
n – the number of solutions to return
extra_constraints – extra constraints to consider when performing the evaluation
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
list of tuples of python primitives representing results
- max(e, extra_constraints=(), signed=False, exact=None)[source]#
Evaluates e, returning its max possible value.
- Parameters:
e – the expression
extra_constraints – extra constraints to consider when performing the evaluation
signed – whether the value should be treated as a signed integer
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
max possible value
- min(e, extra_constraints=(), signed=False, exact=None)[source]#
Evaluates e, returning its min possible value.
- Parameters:
e – the expression
extra_constraints – extra constraints to consider when performing the evaluation
signed – whether the value should be treated as a signed integer
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
min possible value
- solution(e, v, extra_constraints=(), exact=None)[source]#
Checks if v is a possible solution to e.
- Parameters:
e – the expression
v – the value
extra_constraints – extra constraints to consider when performing the evaluation
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
True if it is a possible solution otherwise False
- is_true(e, extra_constraints=(), exact=None)[source]#
Checks if e can only (and TRIVIALLY) evaluate to True. If this function returns True, then the expression cannot ever be False, regardless of constraints or anything else. If the expression returns False, then the expression might STILL not ever be False; it’s just that we can’t trivially prove it. In other words, a return value of False gives you no information whatsoever.
- Parameters:
e – the expression
extra_constraints – extra constraints to consider when performing the evaluation
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
True if it can only evaluate to True otherwise False
- is_false(e, extra_constraints=(), exact=None)[source]#
Checks if e can only (and TRIVIALLY) evaluate to False. If this function returns True, then the expression cannot ever be True, regardless of constraints or anything else. If the expression returns False, then the expression might STILL not ever be True; it’s just that we can’t trivially prove it. In other words, a return value of False gives you no information whatsoever.
- Parameters:
e – the expression
extra_constraints – extra constraints to consider when performing the evaluation
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
True if it can only evaluate to False otherwise False
- class claripy.frontends.light_frontend.LightFrontend(solver_backend, **kwargs)[source]#
Bases:
ConstrainedFrontend
- eval(e, n, extra_constraints=(), exact=None)[source]#
Evaluates expression e, returning a tuple of n solutions.
- Parameters:
e – the expression
n – the number of solutions to return
extra_constraints – extra constraints to consider when performing the evaluation
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
tuple of python primitives representing results
- batch_eval(exprs, n, extra_constraints=(), exact=None)[source]#
Evaluates exprs, returning a list of tuples (one tuple of n solutions for expression).
- Parameters:
exprs – expressions
n – the number of solutions to return
extra_constraints – extra constraints to consider when performing the evaluation
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
list of tuples of python primitives representing results
- max(e, extra_constraints=(), signed=False, exact=None)[source]#
Evaluates e, returning its max possible value.
- Parameters:
e – the expression
extra_constraints – extra constraints to consider when performing the evaluation
signed – whether the value should be treated as a signed integer
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
max possible value
- min(e, extra_constraints=(), signed=False, exact=None)[source]#
Evaluates e, returning its min possible value.
- Parameters:
e – the expression
extra_constraints – extra constraints to consider when performing the evaluation
signed – whether the value should be treated as a signed integer
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
min possible value
- solution(e, v, extra_constraints=(), exact=None)[source]#
Checks if v is a possible solution to e.
- Parameters:
e – the expression
v – the value
extra_constraints – extra constraints to consider when performing the evaluation
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
True if it is a possible solution otherwise False
- is_true(e, extra_constraints=(), exact=None)[source]#
Checks if e can only (and TRIVIALLY) evaluate to True. If this function returns True, then the expression cannot ever be False, regardless of constraints or anything else. If the expression returns False, then the expression might STILL not ever be False; it’s just that we can’t trivially prove it. In other words, a return value of False gives you no information whatsoever.
- Parameters:
e – the expression
extra_constraints – extra constraints to consider when performing the evaluation
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
True if it can only evaluate to True otherwise False
- is_false(e, extra_constraints=(), exact=None)[source]#
Checks if e can only (and TRIVIALLY) evaluate to False. If this function returns True, then the expression cannot ever be True, regardless of constraints or anything else. If the expression returns False, then the expression might STILL not ever be True; it’s just that we can’t trivially prove it. In other words, a return value of False gives you no information whatsoever.
- Parameters:
e – the expression
extra_constraints – extra constraints to consider when performing the evaluation
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
True if it can only evaluate to False otherwise False
- satisfiable(extra_constraints=(), exact=None)[source]#
Checks if stored constraints conjunction is satisfiable.
- Parameters:
extra_constraints – extra constraints to consider when checking satisfiability
exact – whether or not to perform exact checking. Ignored by non-approximating backends.
- Returns:
True if the conjunction is satisfiable otherwise False
- class claripy.frontends.replacement_frontend.ReplacementFrontend(actual_frontend, allow_symbolic=None, replacements=None, replacement_cache=None, unsafe_replacement=None, complex_auto_replace=None, auto_replace=None, replace_constraints=None, **kwargs)[source]#
Bases:
ConstrainedFrontend
- __init__(actual_frontend, allow_symbolic=None, replacements=None, replacement_cache=None, unsafe_replacement=None, complex_auto_replace=None, auto_replace=None, replace_constraints=None, **kwargs)[source]#
- eval(e, n, extra_constraints=(), exact=None)[source]#
Evaluates expression e, returning a tuple of n solutions.
- Parameters:
e – the expression
n – the number of solutions to return
extra_constraints – extra constraints to consider when performing the evaluation
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
tuple of python primitives representing results
- batch_eval(exprs, n, extra_constraints=(), exact=None)[source]#
Evaluates exprs, returning a list of tuples (one tuple of n solutions for expression).
- Parameters:
exprs – expressions
n – the number of solutions to return
extra_constraints – extra constraints to consider when performing the evaluation
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
list of tuples of python primitives representing results
- max(e, extra_constraints=(), signed=False, exact=None)[source]#
Evaluates e, returning its max possible value.
- Parameters:
e – the expression
extra_constraints – extra constraints to consider when performing the evaluation
signed – whether the value should be treated as a signed integer
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
max possible value
- min(e, extra_constraints=(), signed=False, exact=None)[source]#
Evaluates e, returning its min possible value.
- Parameters:
e – the expression
extra_constraints – extra constraints to consider when performing the evaluation
signed – whether the value should be treated as a signed integer
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
min possible value
- solution(e, v, extra_constraints=(), exact=None)[source]#
Checks if v is a possible solution to e.
- Parameters:
e – the expression
v – the value
extra_constraints – extra constraints to consider when performing the evaluation
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
True if it is a possible solution otherwise False
- is_true(e, extra_constraints=(), exact=None)[source]#
Checks if e can only (and TRIVIALLY) evaluate to True. If this function returns True, then the expression cannot ever be False, regardless of constraints or anything else. If the expression returns False, then the expression might STILL not ever be False; it’s just that we can’t trivially prove it. In other words, a return value of False gives you no information whatsoever.
- Parameters:
e – the expression
extra_constraints – extra constraints to consider when performing the evaluation
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
True if it can only evaluate to True otherwise False
- is_false(e, extra_constraints=(), exact=None)[source]#
Checks if e can only (and TRIVIALLY) evaluate to False. If this function returns True, then the expression cannot ever be True, regardless of constraints or anything else. If the expression returns False, then the expression might STILL not ever be True; it’s just that we can’t trivially prove it. In other words, a return value of False gives you no information whatsoever.
- Parameters:
e – the expression
extra_constraints – extra constraints to consider when performing the evaluation
exact – whether or not to perform an exact evaluation. Ignored by non-approximating backends.
- Returns:
True if it can only evaluate to False otherwise False
- satisfiable(extra_constraints=(), exact=None)[source]#
Checks if stored constraints conjunction is satisfiable.
- Parameters:
extra_constraints – extra constraints to consider when checking satisfiability
exact – whether or not to perform exact checking. Ignored by non-approximating backends.
- Returns:
True if the conjunction is satisfiable otherwise False
- class claripy.solvers.Solver(backend=<claripy.backends.backend_z3.BackendZ3 object>, **kwargs)[source]#
Bases:
ConstraintFixerMixin
,ConcreteHandlerMixin
,EagerResolutionMixin
,ConstraintFilterMixin
,ConstraintDeduplicatorMixin
,SimplifySkipperMixin
,SatCacheMixin
,ModelCacheMixin
,ConstraintExpansionMixin
,SimplifyHelperMixin
,FullFrontend
- class claripy.solvers.SolverCacheless(backend=<claripy.backends.backend_z3.BackendZ3 object>, **kwargs)[source]#
Bases:
ConstraintFixerMixin
,ConcreteHandlerMixin
,EagerResolutionMixin
,ConstraintFilterMixin
,ConstraintDeduplicatorMixin
,SimplifySkipperMixin
,FullFrontend
- class claripy.solvers.SolverReplacement(actual_frontend=None, **kwargs)[source]#
Bases:
ConstraintFixerMixin
,ConcreteHandlerMixin
,ConstraintDeduplicatorMixin
,ReplacementFrontend
- class claripy.solvers.SolverHybrid(exact_frontend=None, approximate_frontend=None, complex_auto_replace=True, replace_constraints=True, track=False, approximate_first=False, **kwargs)[source]#
Bases:
ConstraintFixerMixin
,ConcreteHandlerMixin
,EagerResolutionMixin
,ConstraintFilterMixin
,ConstraintDeduplicatorMixin
,SimplifySkipperMixin
,HybridFrontend
- class claripy.solvers.SolverVSA(**kwargs)[source]#
Bases:
ConstraintFixerMixin
,ConcreteHandlerMixin
,ConstraintFilterMixin
,LightFrontend
- class claripy.solvers.SolverConcrete(**kwargs)[source]#
Bases:
ConstraintFixerMixin
,ConcreteHandlerMixin
,ConstraintFilterMixin
,LightFrontend
- class claripy.solvers.SolverStrings(backend, *args, **kwargs)[source]#
Bases:
ConstraintFixerMixin
,ConcreteHandlerMixin
,ConstraintFilterMixin
,ConstraintDeduplicatorMixin
,EagerResolutionMixin
,EvalStringsToASTsMixin
,SMTLibScriptDumperMixin
,FullFrontend
- class claripy.solvers.SolverCompositeChild(backend=<claripy.backends.backend_z3.BackendZ3 object>, **kwargs)[source]#
Bases:
ConstraintDeduplicatorMixin
,SatCacheMixin
,SimplifySkipperMixin
,ModelCacheMixin
,FullFrontend
- class claripy.solvers.SolverComposite(template_solver=None, track=False, template_solver_string=None, **kwargs)[source]#
Bases:
ConstraintFixerMixin
,ConcreteHandlerMixin
,EagerResolutionMixin
,ConstraintFilterMixin
,ConstraintDeduplicatorMixin
,SatCacheMixin
,SimplifySkipperMixin
,SimplifyHelperMixin
,ConstraintExpansionMixin
,CompositedCacheMixin
,CompositeFrontend
Frontend Mixins#
- class claripy.frontend_mixins.composited_cache_mixin.CompositedCacheMixin(*args, **kwargs)[source]#
Bases:
object
- class claripy.frontend_mixins.constraint_deduplicator_mixin.ConstraintDeduplicatorMixin(*args, **kwargs)[source]#
Bases:
object
- class claripy.frontend_mixins.constraint_expansion_mixin.ConstraintExpansionMixin[source]#
Bases:
object
- class claripy.frontend_mixins.model_cache_mixin.ModelCache(model)[source]#
Bases:
object
- eval_ast(ast, allow_unconstrained=True)[source]#
Eval the ast, replacing symbols by their last value in the model.
- Parameters:
ast – The AST to evaluate.
allow_unconstrained (
bool
) – When set to True, we will treat non-existent variables as unconstrained variables and will use arbitrary concrete values for them during evaluation. Otherwise, raise KeyErrors for non-existent variables.
- eval_constraints(constraints)[source]#
Returns whether the constraints is satisfied trivially by using the last model.
- eval_list(asts, allow_unconstrained=True)[source]#
Evaluate a list of ASTs.
- Parameters:
asts – A list of ASTs to evaluate.
allow_unconstrained (
bool
) – When set to True, we will treat non-existent variables as unconstrained variables and will use arbitrary concrete values for them during evaluation. Otherwise, raise KeyErrors for non-existent variables.
- Return type:
Tuple
- Returns:
A tuple of evaluated results, one element per AST.
- class claripy.frontend_mixins.model_cache_mixin.ModelCacheMixin(*args, **kwargs)[source]#
Bases:
object
- class claripy.frontend_mixins.simplify_skipper_mixin.SimplifySkipperMixin(*args, **kwargs)[source]#
Bases:
object
- class claripy.frontend_mixins.solve_block_mixin.SolveBlockMixin(*args, **kwargs)[source]#
Bases:
object
Annotations#
- class claripy.annotation.Annotation[source]#
Bases:
object
Annotations are used to achieve claripy’s goal of being an arithmetic instrumentation engine. They provide a means to pass extra information to the claripy backends.
- property eliminatable: bool#
Returns whether this annotation can be eliminated in a simplification.
- Returns:
True if eliminatable, False otherwise
- property relocatable: bool#
Returns whether this annotation can be relocated in a simplification.
- Returns:
True if it can be relocated, false otherwise.
- relocate(src, dst)[source]#
This is called when an annotation has to be relocated because of simplifications.
Consider the following case:
x = claripy.BVS(‘x’, 32) zero = claripy.BVV(0, 32).add_annotation(your_annotation) y = x + zero
Here, one of three things can happen:
if your_annotation.eliminatable is True, the simplifiers will simply eliminate your_annotation along with zero and y is x will hold
elif your_annotation.relocatable is False, the simplifier will abort and y will never be simplified
elif your_annotation.relocatable is True, the simplifier will run, determine that the simplified result of x + zero will be x. It will then call your_annotation.relocate(zero, x) to move the annotation away from the AST that is about to be eliminated.
- class claripy.annotation.SimplificationAvoidanceAnnotation[source]#
Bases:
Annotation
- property eliminatable#
Returns whether this annotation can be eliminated in a simplification.
- Returns:
True if eliminatable, False otherwise
- property relocatable#
Returns whether this annotation can be relocated in a simplification.
- Returns:
True if it can be relocated, false otherwise.
VSA#
- class claripy.vsa.abstract_location.AbstractLocation(bbl_key, stmt_id, region_id, segment_list=None, region_offset=None, size=None)[source]#
Bases:
BackendObject
- property basicblock_key#
- property statement_id#
- property region#
- property segments#
- class claripy.vsa.bool_result.BoolResult(op=None, args=None)[source]#
Bases:
BackendObject
- class claripy.vsa.bool_result.TrueResult(op=None, args=None)[source]#
Bases:
BoolResult
- cardinality = 1#
- property value#
- class claripy.vsa.bool_result.FalseResult(op=None, args=None)[source]#
Bases:
BoolResult
- cardinality = 1#
- property value#
- class claripy.vsa.bool_result.MaybeResult(op=None, args=None)[source]#
Bases:
BoolResult
- cardinality = 2#
- property value#
- class claripy.vsa.discrete_strided_interval_set.DiscreteStridedIntervalSet(name=None, bits=0, si_set=None, max_cardinality=None)[source]#
Bases:
StridedInterval
A DiscreteStridedIntervalSet represents one or more discrete StridedInterval instances.
- property cardinality#
This is an over-approximation of the cardinality of this DSIS.
- Returns:
- property number_of_values#
- property stride#
- collapse()[source]#
Collapse into a StridedInterval instance.
- Returns:
A new StridedInterval instance.
- normalize()[source]#
Return the collapsed object if
should_collapse()
is True, otherwise return self.- Returns:
A DiscreteStridedIntervalSet object.
- concat(b)[source]#
Operation concat
- Parameters:
b – The other operand to concatenate with.
- Returns:
The concatenated value.
- extract(high_bit, low_bit)[source]#
Operation extract
- Parameters:
high_bit – The highest bit to begin extraction.
low_bit – The lowest bit to end extraction.
- Returns:
Extracted bits.
- union(b)[source]#
The union operation. It might return a DiscreteStridedIntervalSet to allow for better precision in analysis.
- Parameters:
b – Operand
- Returns:
A new DiscreteStridedIntervalSet, or a new StridedInterval.
- sign_extend(new_length)[source]#
Operation SignExt
- Parameters:
new_length – The length to extend to.
- Returns:
SignExtended value.
- exception claripy.vsa.errors.ClaripyVSAError[source]#
Bases:
ClaripyError
- exception claripy.vsa.errors.ClaripyVSAOperationError[source]#
Bases:
ClaripyVSAError
- class claripy.vsa.strided_interval.WarrenMethods[source]#
Bases:
object
Methods as suggested in book. Hackers Delight.
- static min_or(a, b, c, d, w)[source]#
Lower bound of result of ORing 2-intervals.
- Parameters:
a – Lower bound of first interval
b – Upper bound of first interval
c – Lower bound of second interval
d – Upper bound of second interval
w – bit width
- Returns:
Lower bound of ORing 2-intervals
- static max_or(a, b, c, d, w)[source]#
Upper bound of result of ORing 2-intervals.
- Parameters:
a – Lower bound of first interval
b – Upper bound of first interval
c – Lower bound of second interval
d – Upper bound of second interval
w – bit width
- Returns:
Upper bound of ORing 2-intervals
- static min_and(a, b, c, d, w)[source]#
Lower bound of result of ANDing 2-intervals.
- Parameters:
a – Lower bound of first interval
b – Upper bound of first interval
c – Lower bound of second interval
d – Upper bound of second interval
w – bit width
- Returns:
Lower bound of ANDing 2-intervals
- static max_and(a, b, c, d, w)[source]#
Upper bound of result of ANDing 2-intervals.
- Parameters:
a – Lower bound of first interval
b – Upper bound of first interval
c – Lower bound of second interval
d – Upper bound of second interval
w – bit width
- Returns:
Upper bound of ANDing 2-intervals
- class claripy.vsa.strided_interval.StridedInterval(name=None, bits=0, stride=None, lower_bound=None, upper_bound=None, uninitialized=False, bottom=False)[source]#
Bases:
BackendObject
A Strided Interval is represented in the following form:
<bits> stride[lower_bound, upper_bound]
For more details, please refer to relevant papers like TIE and WYSINWYE.
This implementation is signedness-agostic, please refer to [1] Signedness-Agnostic Program Analysis: Precise Integer Bounds for Low-Level Code by Jorge A. Navas, etc. for more details. Note that this implementation only takes hint from [1]. Such a work has been improved to be more precise (and still sound) when dealing with strided intervals. DO NOT expect to see a 1-to-1 reproduction of [1].
Thanks all corresponding authors for their outstanding works.
- __init__(name=None, bits=0, stride=None, lower_bound=None, upper_bound=None, uninitialized=False, bottom=False)[source]#
- eval(n, signed=False)[source]#
Evaluate this StridedInterval to obtain a list of concrete integers.
- Parameters:
n – Upper bound for the number of concrete integers
signed – Treat this StridedInterval as signed or unsigned
- Returns:
A list of at most n concrete integers
- solution(b)[source]#
Checks whether an integer is solution of the current strided Interval :type b: :param b: integer to check :return: True if b belongs to the current Strided Interval, False otherwhise
- identical(o)[source]#
Used to make exact comparisons between two StridedIntervals. Usually it is only used in test cases.
- Parameters:
o – The other StridedInterval to compare with.
- Returns:
True if they are exactly same, False otherwise.
- SLT(o)[source]#
Signed less than
- Parameters:
o – The other operand
- Returns:
TrueResult(), FalseResult(), or MaybeResult()
- SLE(o)[source]#
Signed less than or equal to.
- Parameters:
o – The other operand.
- Returns:
TrueResult(), FalseResult(), or MaybeResult()
- SGT(o)[source]#
Signed greater than.
- Parameters:
o – The other operand
- Returns:
TrueResult(), FalseResult(), or MaybeResult()
- SGE(o)[source]#
Signed greater than or equal to.
- Parameters:
o – The other operand
- Returns:
TrueResult(), FalseResult(), or MaybeResult()
- ULT(o)[source]#
Unsigned less than.
- Parameters:
o – The other operand
- Returns:
TrueResult(), FalseResult(), or MaybeResult()
- ULE(o)[source]#
Unsigned less than or equal to.
- Parameters:
o – The other operand
- Returns:
TrueResult(), FalseResult(), or MaybeResult()
- UGT(o)[source]#
Signed greater than.
- Parameters:
o – The other operand
- Returns:
TrueResult(), FalseResult(), or MaybeResult()
- UGE(o)[source]#
Unsigned greater than or equal to.
- Parameters:
o – The other operand
- Returns:
TrueResult(), FalseResult(), or MaybeResult()
- eq(o)[source]#
Equal
- Parameters:
o – The ohter operand
- Returns:
TrueResult(), FalseResult(), or MaybeResult()
- LShR(shift_amount)[source]#
Logical shift right. :param StridedInterval shift_amount: The amount of shifting :return: The shifted StridedInterval object :rtype: StridedInterval
- property name#
- property reversed#
- property size#
- property cardinality#
- property complement#
Return the complement of the interval Refer section 3.1 augmented for managing strides
- Returns:
- property lower_bound#
- property upper_bound#
- property bits#
- property stride#
- property max#
- property min#
- property unique#
- property is_empty#
The same as is_bottom :return: True/False
- property is_top#
If this is a TOP value.
- Returns:
True if this is a TOP
- property is_bottom#
Whether this StridedInterval is a BOTTOM, in other words, describes an empty set of integers.
- Returns:
True/False
- property is_integer#
If this is an integer, i.e. self.lower_bound == self.upper_bound.
- Returns:
True if this is an integer, False otherwise
- property is_interval#
- property n_values#
- static lcm(a, b)[source]#
Get the least common multiple.
- Parameters:
a – The first operand (integer)
b – The second operand (integer)
- Returns:
Their LCM
- static gcd(a, b)[source]#
Get the greatest common divisor.
- Parameters:
a – The first operand (integer)
b – The second operand (integer)
- Returns:
Their GCD
- mul(o)[source]#
Binary operation: multiplication
- Parameters:
o – The other operand
- Returns:
self * o
- sdiv(o)[source]#
Binary operation: signed division
- Parameters:
o – The divisor
- Returns:
(self / o) in signed arithmetic
- udiv(o)[source]#
Binary operation: unsigned division
- Parameters:
o – The divisor
- Returns:
(self / o) in unsigned arithmetic
- bitwise_or(t)[source]#
Binary operation: logical or
- Parameters:
b – The other operand
- Returns:
self | b
- union(b)[source]#
The union operation. It might return a DiscreteStridedIntervalSet to allow for better precision in analysis.
- Parameters:
b – Operand
- Returns:
A new DiscreteStridedIntervalSet, or a new StridedInterval.
- static least_upper_bound(*intervals_to_join)[source]#
Pseudo least upper bound. Join the given set of intervals into a big interval. The resulting strided interval is the one which in all the possible joins of the presented SI, presented the least number of values.
The number of joins to compute is linear with the number of intervals to join.
Draft of proof: Considering three generic SI (a,b, and c) ordered from their lower bounds, such that a.lower_bund <= b.lower_bound <= c.lower_bound, where <= is the lexicographic less or equal. The only joins which have sense to compute are: * a U b U c * b U c U a * c U a U b
All the other combinations fall in either one of these cases. For example: b U a U c does not make make sense to be calculated. In fact, if one draws this union, the result is exactly either (b U c U a) or (a U b U c) or (c U a U b). :type intervals_to_join: :param intervals_to_join: Intervals to join :return: Interval that contains all intervals
- static pseudo_join(s, b, smart_join=True)[source]#
It two intervals in a way that the resulting SI is the one that has the least SI cardinality (i.e., which represents the least number of elements) possible if the smart_join flag is enabled, otherwise it just joins the SI according the order they are passed to the function.
The pseudo-join operation is not associative in wrapping intervals (please refer to section 3.1 paper ‘Signedness-Agnostic Program Analysis: Precise Integer Bounds for Low-Level Code’), Therefore the join of three WI may give us different results according on the order we join them. All of the results will be sound, though.
Please use the function least_upper_bound as a stub.
- Parameters:
s – The first SI
b – The other SI.
smart_join – Enable the smart join behavior. If this flag is set, this function joins the two SI in a way that the resulting Si has least number of elements (more precise). If it is unset, this function will join the two SI according on the order they are passed to the function.
- Returns:
A new StridedInterval
- static extended_euclid(a, b)[source]#
It calculates the GCD of a and b, and two values x and y such that: a*x + b*y = GCD(a,b). This code has been taken from the project sympy.
- Parameters:
a – first integer
b – second integer
- Returns:
x,y and the GCD of a and b
- static igcd(a, b)[source]#
- Parameters:
a – First integer
b – Second integer
- Returns:
the integer GCD between a and b
- static diop_natural_solution_linear(c, a, b)[source]#
It finds the fist natural solution of the diophantine equation a*x + b*y = c. Some lines of this code are taken from the project sympy.
- Parameters:
c – constant
a – quotient of x
b – quotient of y
- Returns:
the first natural solution of the diophatine equation
- claripy.vsa.strided_interval.CreateStridedInterval(name=None, bits=0, stride=None, lower_bound=None, upper_bound=None, uninitialized=False, to_conv=None, discrete_set=False, discrete_set_max_cardinality=None)[source]#
- Parameters:
name –
bits –
stride –
lower_bound –
upper_bound –
to_conv –
discrete_set (bool) –
discrete_set_max_cardinality (int) –
- Returns:
- class claripy.vsa.valueset.RegionAnnotation(region_id, region_base_addr, offset)[source]#
Bases:
Annotation
Use RegionAnnotation to annotate ASTs. Normally, an AST annotated by RegionAnnotations is treated as a ValueSet.
Note that Annotation objects are immutable. Do not change properties of an Annotation object without creating a new one.
- property eliminatable#
A Region annotation is not eliminatable in simplifications.
- Returns:
False
- Return type:
bool
- property relocatable#
A Region annotation is not relocatable in simplifications.
- Returns:
False
- Return type:
bool
- class claripy.vsa.valueset.ValueSet(name=None, region=None, region_base_addr=None, bits=None, val=None)[source]#
Bases:
BackendObject
ValueSet is a mapping between memory regions and corresponding offsets.
- __init__(name=None, region=None, region_base_addr=None, bits=None, val=None)[source]#
Constructor.
- Parameters:
name (str) – Name of this ValueSet object. Only for debugging purposes.
region (str) – Region ID.
region_base_addr (int) – Base address of the region.
bits (int) – Size of the ValueSet.
val – an initial offset
- property name#
- property bits#
- property regions#
- property reversed#
- property unique#
- property cardinality#
- property is_empty#
- property valueset#
- apply_annotation(annotation)[source]#
Apply a new annotation onto self, and return a new ValueSet object.
- Parameters:
annotation (RegionAnnotation) – The annotation to apply.
- Returns:
A new ValueSet object
- Return type:
- property min#
The minimum integer value of a value-set. It is only defined when there is exactly one region.
- Returns:
A integer that represents the minimum integer value of this value-set.
- Return type:
int
- property max#
The maximum integer value of a value-set. It is only defined when there is exactly one region.
- Returns:
A integer that represents the maximum integer value of this value-set.
- Return type:
int
Misc. Things#
- claripy.from_iterable(iterable, /)#
Alternative chain() constructor taking a single iterable argument that evaluates lazily.
- class claripy.balancer.Balancer(helper, c, validation_frontend=None)[source]#
Bases:
object
The Balancer is an equation redistributor. The idea is to take an AST and rebalance it to, for example, isolate unknown terms on one side of an inequality.
- property compat_ret#
- property replacements#
- comparison_info = {'SGE': (False, True, False), 'SGT': (False, False, False), 'SLE': (True, True, False), 'SLT': (True, False, False), 'UGE': (False, True, True), 'UGT': (False, False, True), 'ULE': (True, True, True), 'ULT': (True, False, True), '__ge__': (False, True, True), '__gt__': (False, False, True), '__le__': (True, True, True), '__lt__': (True, False, True)}#
- class claripy.bv.BVV(value, bits)[source]#
Bases:
BackendObject
- bits#
- mod#
- property value#
- property signed#
- exception claripy.errors.UnsatError[source]#
Bases:
ClaripyError
- exception claripy.errors.ClaripyFrontendError[source]#
Bases:
ClaripyError
- exception claripy.errors.ClaripySerializationError[source]#
Bases:
ClaripyError
- exception claripy.errors.BackendError[source]#
Bases:
ClaripyError
- exception claripy.errors.BackendUnsupportedError[source]#
Bases:
BackendError
- exception claripy.errors.ClaripyZ3Error[source]#
Bases:
ClaripyError
- exception claripy.errors.ClaripyBackendVSAError[source]#
Bases:
BackendError
- exception claripy.errors.MissingSolverError[source]#
Bases:
ClaripyError
- exception claripy.errors.ClaripySolverInterruptError[source]#
Bases:
ClaripyError
- exception claripy.errors.ClaripyASTError[source]#
Bases:
ClaripyError
- exception claripy.errors.ClaripyBalancerError[source]#
Bases:
ClaripyASTError
- exception claripy.errors.ClaripyBalancerUnsatError[source]#
Bases:
ClaripyBalancerError
- exception claripy.errors.ClaripyTypeError[source]#
Bases:
ClaripyASTError
- exception claripy.errors.ClaripyValueError[source]#
Bases:
ClaripyASTError
- exception claripy.errors.ClaripySizeError[source]#
Bases:
ClaripyASTError
- exception claripy.errors.ClaripyOperationError[source]#
Bases:
ClaripyASTError
- exception claripy.errors.ClaripyReplacementError[source]#
Bases:
ClaripyASTError
- exception claripy.errors.ClaripyRecursionError[source]#
Bases:
ClaripyOperationError
- exception claripy.errors.ClaripyZeroDivisionError[source]#
Bases:
ClaripyOperationError
,ZeroDivisionError
- class claripy.fp.RM(value)[source]#
Bases:
Enum
An enumeration.
- RM_NearestTiesEven = 'RM_RNE'#
- RM_NearestTiesAwayFromZero = 'RM_RNA'#
- RM_TowardsZero = 'RM_RTZ'#
- RM_TowardsPositiveInf = 'RM_RTP'#
- RM_TowardsNegativeInf = 'RM_RTN'#
- class claripy.fp.FPV(value, sort)[source]#
Bases:
BackendObject
- value#
- sort#
- claripy.fp.fpToFP(a1, a2, a3=None)[source]#
Returns a FP AST and has three signatures:
- fpToFP(ubvv, sort)
Returns a FP AST whose value is the same as the unsigned BVV a1 and whose sort is a2.
- fpToFP(rm, fpv, sort)
Returns a FP AST whose value is the same as the floating point a2 and whose sort is a3.
- fpToTP(rm, sbvv, sort)
Returns a FP AST whose value is the same as the signed BVV a2 and whose sort is a3.
- claripy.fp.fpToFPUnsigned(_rm, thing, sort)[source]#
Returns a FP AST whose value is the same as the unsigned BVV thing and whose sort is sort.
- claripy.fp.fpToIEEEBV(fpv)[source]#
Interprets the bit-pattern of the IEEE754 floating point number fpv as a bitvector.
- Returns:
A BV AST whose bit-pattern is the same as fpv
- claripy.fp.fpFP(sgn, exp, mantissa)[source]#
Concatenates the bitvectors sgn, exp and mantissa and returns the corresponding IEEE754 floating point number.
- Returns:
A FP AST whose bit-pattern is the same as the concatenated bitvector
- claripy.fp.fpGEQ(a, b)[source]#
Checks if floating point a is greater than or equal to floating point b.
- claripy.fp.fpLEQ(a, b)[source]#
Checks if floating point a is less than or equal to floating point b.
- claripy.fp.fpAbs(x)[source]#
Returns the absolute value of the floating point x. So:
a = FPV(-3.2, FSORT_DOUBLE) b = fpAbs(a) b is FPV(3.2, FSORT_DOUBLE)
- claripy.fp.fpNeg(x)[source]#
Returns the additive inverse of the floating point x. So:
a = FPV(3.2, FSORT_DOUBLE) b = fpAbs(a) b is FPV(-3.2, FSORT_DOUBLE)
- claripy.fp.fpSub(_rm, a, b)[source]#
Returns the subtraction of the floating point a by the floating point b.
- claripy.fp.fpMul(_rm, a, b)[source]#
Returns the multiplication of two floating point numbers, a and b.
- claripy.fp.fpDiv(_rm, a, b)[source]#
Returns the division of the floating point a by the floating point b.
- claripy.operations.op(name, arg_types, return_type, extra_check=None, calc_length=None, do_coerce=True, bound=True)[source]#
- class claripy.simplifications.SimplificationManager[source]#
Bases:
object
- static rotate_shift_mask_simplifier(a, b)[source]#
- Handles the following case:
- ((A << a) | (A >> (_N - a))) & mask, where
A being a BVS, a being a integer that is less than _N, _N is either 32 or 64, and mask can be evaluated to 0xffffffff (64-bit) or 0xffff (32-bit) after reversing the rotate-shift operation.
- It will be simplified to:
(A & (mask >>> a)) <<< a
- static and_mask_comparing_against_constant_simplifier(op, a, b)[source]#
This simplifier handles the following case:
A & mask == b, and A & mask != b
If the high bits of A are 0, & mask can be eliminated.
- static zeroext_extract_comparing_against_constant_simplifier(op, a, b)[source]#
This simplifier handles the following cases:
Extract(hi, 0, Concat(0, A)) op b, and Extract(hi, 0, ZeroExt(n, A)) op b
Extract can be eliminated if the high bits of Concat(0, A) or ZeroExt(n, A) are all zeros.
- static zeroext_comparing_against_simplifier(op, a, b)[source]#
This simplifier handles the following cases:
ZeroExt(n, A) == b, ZeroExt(n, A) != b, and ZeroExt(n, A) >= b
If the high bits of b are all zeros (in case of ==, !=, and >=) or have at least one ones (in case of !=), ZeroExt can be eliminated.
- claripy.ops.from_iterable(iterable, /)#
Alternative chain() constructor taking a single iterable argument that evaluates lazily.
- class claripy.strings.StringV(value)[source]#
Bases:
BackendObject
- claripy.strings.StrConcat(*args)[source]#
Concatenate a sequence of strings.
- Parameters:
args – list of string that has to be concatenated
- Returns:
the concatenated string
- claripy.strings.StrSubstr(start_idx, count, initial_string)[source]#
Return the substring of length count starting at start_idx.
- Parameters:
start_idx – starting index of the substring
count – length of the substring in bytes
initial_string – original string
- Returns:
the substring
- claripy.strings.StrReplace(initial_string, pattern_to_be_replaced, replacement_pattern)[source]#
Return string where the first occurrence of pattern_to_be_replaced is replaced with replacement_pattern.
- Parameters:
initial_string – string in which the pattern needs to be replaced
pattern_to_be_replaced – substring that has to be replaced inside initial_string
replacement_pattern – pattern that has to be inserted in initial_string to replace pattern_to_be_replaced
- Returns:
string with replacement
- claripy.strings.StrLen(input_string, bitlength)[source]#
Return length of the input_string in bytes.
- Parameters:
input_string – the string we want to calculate the length
bitlength – length of the bitvector representing the length of the string
- Returns:
bitvector holding the size of the string in bytes
- claripy.strings.StrContains(input_string, substring)[source]#
Check if substring is contained in input_string.
- Parameters:
input_string – the string we want to check
substring – the string we want to check if it’s contained inside the input_string
- Returns:
True if substring is contained in input_string else False
- claripy.strings.StrPrefixOf(prefix, input_string)[source]#
Check if input_string starts with prefix.
- Parameters:
prefix – prefix we want to check
input_string – the string we want to check
- Returns:
True if the input_string starts with prefix else False
- claripy.strings.StrSuffixOf(suffix, input_string)[source]#
Check if input_string ends with suffix.
- Parameters:
suffix – suffix we want to check
input_string – the string we want to check
:return : True if the input_string ends with suffix else False
- claripy.strings.StrIndexOf(input_string, substring, startIndex, bitlength)[source]#
Return the index of the first occurrence of substring at or after the startIndex, or -1 if it is not found.
- Parameters:
input_string – the string we want to check
substring – the substring we want to find the index
startIndex – the index to start searching at
bitlength – length of the bitvector representing the index of the substring
- Return BV:
index of the substring or -1 in bitvector
- claripy.strings.StrToInt(input_string, bitlength)[source]#
Return the integer representation of input_string.
- Parameters:
input_string – the string we want to transform in an integer
bitlength – length of the bitvector representing the index of the substring
- Return BV:
bitvector of the integer resulting from the string or -1 in bitvector if the string cannot be transformed into an integer