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.base.Base(*args, **kwargs)

An AST tracks a tree of operations on arguments. It has the following methods:

op: the operation that is being done on the arguments args: the arguments that are being used length: the length (in bits)

AST objects have hash identity. This means that an AST that has the same hash as another AST will be the same object. For example, the following is true:

a, b = two different ASTs c = b + a d = b + a assert c is d

This is done to better support serialization and better manage memory.

This is called when you create a new Base object, whether directly or through an operation. It finalizes the arguments (see the _finalize function, above) and then computes a hash. If an AST of this hash already exists, it returns that AST. Otherwise, it creates, initializes, and returns the AST.

Parameters:
  • op – The AST operation (‘__add__’, ‘Or’, etc)
  • args – The arguments to the AST operation (i.e., the objects to add)
  • variables – The symbolic variables present in the AST (default: empty set)
  • symbolic – A flag saying whether or not the AST is symbolic (default: False)
  • length – An integer specifying the length of this AST (default: None)
  • collapsible – A flag of whether or not Claripy can feel free to collapse this AST. This is mostly used to keep Claripy from collapsing Reverse operations, so that they can be undone with another Reverse.
  • simplified – A measure of how simplified this AST is. 0 means unsimplified, 1 means fast-simplified (basically, just undoing the Reverse op), and 2 means simplified through z3.
  • errored – A set of backends that are known to be unable to handle this AST.
  • eager_backends – A list of backends with which to attempt eager evaluation
  • annotations – A frozenset of annotations applied onto this AST.
make_uuid(uuid=None)

This overrides the default ANA uuid with the hash of the AST. UUID is slow, and we’ll soon replace it from ANA itself, and this will go away.

Returns:a string representation of the AST hash.
append_annotation(a)

Appends an annotation to this AST.

Parameters:a – the annotation to append
Returns:a new AST, with the annotation added
append_annotations(new_tuple)

Appends several annotations to this AST.

Parameters:new_tuple – the tuple of annotations to append
Returns:a new AST, with the annotations added
annotate(*args)

Appends annotations to this AST.

Parameters:args – the tuple of annotations to append (variadic positional args)
Returns:a new AST, with the annotations added
insert_annotation(a)

Inserts an annotation to this AST.

Parameters:a – the annotation to insert
Returns:a new AST, with the annotation added
insert_annotations(new_tuple)

Inserts several annotations to this AST.

Parameters:new_tuple – the tuple of annotations to insert
Returns:a new AST, with the annotations added
replace_annotations(new_tuple)

Replaces annotations on this AST.

Parameters:new_tuple – the tuple of annotations to replace the old annotations with
Returns:a new AST, with the annotations added
remove_annotation(a)

Removes an annotation from this AST.

Parameters:a – the annotation to remove
Returns:a new AST, with the annotation removed
remove_annotations(remove_sequence)

Removes several annotations from this AST.

Parameters:remove_sequence – a sequence/set of the annotations to remove
Returns:a new AST, with the annotations removed
depth

The depth of this AST. For example, an AST representing (a+(b+c)) would have a depth of 2.

swap_args(new_args, new_length=None)

This returns the same AST, with the arguments swapped out for new_args.

split(split_on)

Splits the AST if its operation is split_on (i.e., return all the arguments). Otherwise, return a list with just the AST.

structurally_match(o)

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 – the other claripy A object
Returns:True/False
replace(old, new)

Returns an AST with all instances of the AST ‘old’ replaced with AST ‘new’.

replace_dict(replacements)
Parameters:replacements – A dictionary of asts to replace and their replacements.
Returns:An AST with all instances of ast’s in replacements.
ite_burrowed

Returns an equivalent AST that “burrows” the ITE expressions as deep as possible into the ast, for simpler printing.

ite_excavated

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.

uninitialized

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).
uc_alloc_depth

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.
claripy.ast.bool.BoolS(name, explicit_name=None)

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.
Returns:

A Bool object representing this symbol.

claripy.ast.bool.constraint_to_si(expr)

Convert a constraint to SI if possible.

Parameters:expr
Returns:
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)

Creates a bit-vector symbol (i.e., a variable).

Parameters:
  • name – The name of the symbol.
  • size – The size (in bits) of the bit-vector.
  • min – The minimum value of the symbol.
  • max – The maximum value of the symbol.
  • stride – The stride of the symbol.
  • 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.
Returns:

a BV object representing this symbol.

claripy.ast.bv.BVV(value, size=None, **kwargs)

Creates a bit-vector value (i.e., a concrete value).

Parameters:
  • value – The value.
  • size – The size (in bits) of the bit-vector.
Returns:

A BV object representing this value.

claripy.ast.fp.FPS(name, sort, explicit_name=None)

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)

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.

Backends

class claripy.backends.Backend(solver_required=None)

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.A 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.A 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.

downsize()

Clears all caches associated with this backend.

handles(expr)

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)

Resolves a claripy.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)

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)

Should return True if e can be easily found to be True.

Parameters:
  • e – The AST.
  • extra_constraints – Extra constraints (claripy.E objects) 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)

Should return True if e can be easily found to be False.

Parameters:
  • e – The AST
  • extra_constraints – Extra constraints (claripy.E objects) 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)

Should return True if e can possible be True.

Parameters:
  • e – The AST.
  • extra_constraints – Extra constraints (claripy.E objects) 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)

Should return False if e can possibly be False.

Parameters:
  • e – The AST.
  • extra_constraints – Extra constraints (claripy.E objects) 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)

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)

This function adds constraints to the backend solver.

Parameters:
  • c – A sequence of claripy.E objects.
  • s – A backend solver object.
  • track (bool) – True to enable constraint tracking, which is used in unsat_core().
unsat_core(s)

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)

This function returns up to n possible solutions for expression expr.

Parameters:
  • expr – expression (claripy.E object) 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 (claripy.E objects) 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)

Evaluate one or multiple expressions.

Parameters:
  • exprs – A list of expressions to evaluate.
  • n – Number of different solutions to return.
  • extra_constraints – Extra constraints (claripy.E objects) 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=(), solver=None, model_callback=None)

Return the minimum value of expr.

Parameters:
  • expr – expression (claripy.E object) to evaluate
  • solver – a solver object, native to the backend, to assist in the evaluation (for example, a z3.Solver)
  • extra_constraints – extra constraints (claripy.E objects) to add to the solver for this solve
  • 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=(), solver=None, model_callback=None)

Return the maximum value of expr.

Parameters:
  • expr – expression (claripy.E object) to evaluate
  • solver – a solver object, native to the backend, to assist in the evaluation (for example, a z3.Solver)
  • extra_constraints – extra constraints (claripy.E objects) to add to the solver for this solve
  • model_callback – a function that will be executed with recovered models (if any)
Returns:

the maximum possible value of expr (backend object)

satisfiable(extra_constraints=(), solver=None, model_callback=None)

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 (claripy.E objects) 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)

Return True if v is a solution of expr with the extra constraints, False otherwise.

Parameters:
  • expr – An expression (claripy.E) to evaluate
  • v – The proposed solution (claripy.E)
  • solver – A solver object, native to the backend, to assist in the evaluation (for example, a z3.Solver).
  • extra_constraints – Extra constraints (claripy.E objects) 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

size(a)

This should return the size of an expression.

Parameters:a – the claripy A object
name(a)

This should return the name of an expression.

Parameters:a – the claripy A object
identical(a, b)

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 – a claripy A object
  • b – a claripy A object
cardinality(a)

This should return the maximum number of values that an expression can take on. This should be a strict over approximation.

Parameters:a – A claripy A object.
Returns:An integer.
apply_annotation(o, a)

This should apply the annotation on the backend object, and return a new backend object.

Parameters:
  • o – A backend object.
  • a – An Annotation object.
Returns:

A backend object.

class claripy.backend_object.BackendObject

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.

to_claripy()

Claripy calls this to retrieve something that it can directly reason about.

Frontends

Frontend Mixins

VSA

class claripy.vsa.discrete_strided_interval_set.DiscreteStridedIntervalSet(name=None, bits=0, si_set=None, max_cardinality=None)

A DiscreteStridedIntervalSet represents one or more discrete StridedInterval instances.

cardinality

This is an over-approximation of the cardinality of this DSIS.

Returns:
collapse()

Collapse into a StridedInterval instance.

Returns:A new StridedInterval instance.
normalize()

Return the collapsed object if should_collapse() is True, otherwise return self.

Returns:A DiscreteStridedIntervalSet object.
concat(o)

Operation concat

Parameters:b – The other operand to concatenate with.
Returns:The concatenated value.
extract(high_bit, low_bit)

Operation extract

Parameters:
  • high_bit – The highest bit to begin extraction.
  • low_bit – The lowest bit to end extraction.
Returns:

Extracted bits.

eval(n, signed=False)
Parameters:
  • n
  • signed
Returns:

sign_extend(o)

Operation SignExt

Parameters:new_length – The length to extend to.
Returns:SignExtended value.
zero_extend(o)

Operation ZeroExt

Parameters:new_length – The length to extend to.
Returns:ZeroExtended value.
widen(o)

Widening operator.

Parameters:b – The other operand.
Returns:The widened result.
class claripy.vsa.strided_interval.WarrenMethods

Methods as suggested in book. Hackers Delight.

static min_or(a, b, c, d, w)

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)

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)

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)

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

static min_xor(a, b, c, d, w)

Lower bound of result of XORing 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 XORing 2-intervals

static max_xor(a, b, c, d, w)

Upper bound of result of XORing 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 XORing 2-intervals

class claripy.vsa.strided_interval.StridedInterval(name=None, bits=0, stride=None, lower_bound=None, upper_bound=None, uninitialized=False, bottom=False)

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.

eval(n, signed=False)

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)

Checks whether an integer is solution of the current strided Interval :param b: integer to check :return: True if b belongs to the current Strided Interval, False otherwhise

identical(o)

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)

Signed less than

Parameters:o – The other operand
Returns:TrueResult(), FalseResult(), or MaybeResult()
SLE(o)

Signed less than or equal to.

Parameters:o – The other operand.
Returns:TrueResult(), FalseResult(), or MaybeResult()
SGT(o)

Signed greater than.

Parameters:o – The other operand
Returns:TrueResult(), FalseResult(), or MaybeResult()
SGE(o)

Signed greater than or equal to.

Parameters:o – The other operand
Returns:TrueResult(), FalseResult(), or MaybeResult()
ULT(o)

Unsigned less than.

Parameters:o – The other operand
Returns:TrueResult(), FalseResult(), or MaybeResult()
ULE(o)

Unsigned less than or equal to.

Parameters:o – The other operand
Returns:TrueResult(), FalseResult(), or MaybeResult()
UGT(o)

Signed greater than.

Parameters:o – The other operand
Returns:TrueResult(), FalseResult(), or MaybeResult()
UGE(o)

Unsigned greater than or equal to.

Parameters:o – The other operand
Returns:TrueResult(), FalseResult(), or MaybeResult()
eq(o)

Equal

Parameters:o – The ohter operand
Returns:TrueResult(), FalseResult(), or MaybeResult()
LShR(shift_amount)

Logical shift right. :param StridedInterval shift_amount: The amount of shifting :return: The shifted StridedInterval object :rtype: StridedInterval

complement

Return the complement of the interval Refer section 3.1 augmented for managing strides

Returns:
is_empty

The same as is_bottom :return: True/False

is_top

If this is a TOP value.

Returns:True if this is a TOP
is_bottom

Whether this StridedInterval is a BOTTOM, in other words, describes an empty set of integers.

Returns:True/False
is_integer

If this is an integer, i.e. self.lower_bound == self.upper_bound.

Returns:True if this is an integer, False otherwise
static lcm(a, b)

Get the least common multiple.

Parameters:
  • a – The first operand (integer)
  • b – The second operand (integer)
Returns:

Their LCM

static gcd(a, b)

Get the greatest common divisor.

Parameters:
  • a – The first operand (integer)
  • b – The second operand (integer)
Returns:

Their GCD

static upper(bits, i, stride)
Returns:
static lower(bits, i, stride)
Returns:
static top(bits, name=None, uninitialized=False)

Get a TOP StridedInterval.

Returns:
add(o)

Binary operation: add

Parameters:b – The other operand
Returns:self + b
sub(o)

Binary operation: sub

Parameters:b – The other operand
Returns:self - b
mul(o)

Binary operation: multiplication

Parameters:o – The other operand
Returns:self * o
sdiv(o)

Binary operation: signed division

Parameters:o – The divisor
Returns:(self / o) in signed arithmetic
udiv(o)

Binary operation: unsigned division

Parameters:o – The divisor
Returns:(self / o) in unsigned arithmetic
bitwise_or(o)

Binary operation: logical or

Parameters:b – The other operand
Returns:self | b
bitwise_and(o)

Binary operation: logical and

Parameters:b – The other operand
Returns:
bitwise_xor(o)

Operation xor

Parameters:t – The other operand.
union(o)

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)

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). :param intervals_to_join: Intervals to join :return: Interval that contains all intervals

static pseudo_join(s, b, smart_join=True)

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)

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)
Parameters:
  • a – First integer
  • b – Second integer
Returns:

the integer GCD between a and b

static diop_natural_solution_linear(c, a, b)

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

reverse()

This is a delayed reversing function. All it really does is to invert the _reversed property of this StridedInterval object.

Returns:None
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)
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)

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.

eliminatable

A Region annotation is not eliminatable in simplifications.

Returns:False
Return type:bool
relocatable

A Region annotation is not relocatable in simplifications.

Returns:False
Return type:bool
relocate(src, dst)

Override Annotation.relocate().

Parameters:
  • src – The old AST
  • dst – The new AST, as the result of a simplification
Returns:

The new annotation that should be applied on the new AST

class claripy.vsa.valueset.ValueSet(name=None, region=None, region_base_addr=None, bits=None, val=None)

ValueSet is a mapping between memory regions and corresponding offsets.

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
copy()

Make a copy of self and return.

Returns:A new ValueSet object.
Return type:ValueSet
apply_annotation(annotation)

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:ValueSet
extract(high_bit, low_bit)

Operation extract

  • A cheap hack is implemented: a copy of self is returned if (high_bit - low_bit + 1 == self.bits), which is a
    ValueSet instance. Otherwise a StridedInterval is returned.
Parameters:
  • high_bit
  • low_bit
Returns:

A ValueSet or a StridedInterval

identical(o)

Used to make exact comparisons between two ValueSets.

Parameters:o – The other ValueSet to compare with.
Returns:True if they are exactly same, False otherwise.

Misc. Things

class claripy.balancer.Balancer(helper, c, validation_frontend=None)

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.