Files
codeql/python/ql/lib/semmle/python/frameworks/Pydantic.qll
Copilot 3f9c603115 Python: switch dataflow library to new (shared) CFG + SSA
Flips the Python dataflow trunk from the legacy CFG (semmle/python/Flow.qll)
and legacy ESSA SSA (semmle/python/essa/*) to the new shared CFG facade
(semmle.python.controlflow.internal.Cfg) and the new SSA adapter
(semmle.python.dataflow.new.internal.SsaImpl), both introduced
additively in the preceding PRs in this stack.

This is the trunk-flip equivalent of the original draft PR #21894 (kept
around as documentation), rebased on top of the four preparatory PRs:

  P1: Remove AstNode.getAFlowNode() and rewrite callers (#21919).
  P2: Qualify Flow.qll's AST references with Py:: prefix (#21920).
  P3: Add new shared-CFG-backed control flow graph (#21921).
  P4: Add new shared-SSA-backed SSA adapter (#21923).

The Python dataflow library (semmle/python/dataflow/new/) now imports
the new CFG facade and SSA adapter. All CFG-typed predicates
(ControlFlowNode, CallNode, BasicBlock, NameNode, AttrNode, ...) are
qualified with the Cfg:: prefix; SSA references switch from
EssaVariable/EssaDefinition to SsaImpl::Definition/SourceVariable.

GuardNode is redesigned to use the new CFG's outcome-node model
(isAfterTrue / isAfterFalse) instead of the legacy ConditionBlock +
flipped indirection. Only BarrierGuard<...> is preserved as public
API.

Framework files (Bottle, FastApi, Django, Tornado, Pyramid, Stdlib,
...) are updated to take CFG nodes from the new facade.

A handful of dataflow consistency tweaks for the new CFG:
- Augmented-assignment targets are treated as both load and store.
- 'from X import *' produces uncertain SSA writes for unknown names.
- CFG nodes are canonicalised so dataflow does not see equivalent
  pre/post-order pairs as distinct nodes.

Two AST tweaks for the new CFG:
- AstNodeImpl: omit PEP 695 type-parameter names from
  FunctionDefExpr / ClassDefExpr children.
- ImportResolution: drop the legacy essa import.

Test churn (~175 files): reblessed library- and query-test .expected
files reflect slightly different CFG granularity, different toString
output, and a handful of true alert deltas in security queries.

Verification: all 367 lib + src + consistency-queries compile clean.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-06-02 20:01:30 +00:00

113 lines
4.3 KiB
Plaintext

/**
* Provides classes modeling security-relevant aspects of the `pydantic` PyPI package.
*
* See
* - https://pypi.org/project/pydantic/
* - https://pydantic-docs.helpmanual.io/
*/
private import python
private import semmle.python.controlflow.internal.Cfg as Cfg
private import semmle.python.dataflow.new.DataFlow
private import semmle.python.dataflow.new.TaintTracking
private import semmle.python.Concepts
private import semmle.python.ApiGraphs
private import semmle.python.frameworks.data.ModelsAsData
/**
* INTERNAL: Do not use.
*
* Provides models for `pydantic` PyPI package.
*
* See
* - https://pypi.org/project/pydantic/
* - https://pydantic-docs.helpmanual.io/
*/
module Pydantic {
/**
* Provides models for `pydantic.BaseModel` subclasses (a pydantic model).
*
* See https://pydantic-docs.helpmanual.io/usage/models/.
*/
module BaseModel {
/** Gets a reference to a `pydantic.BaseModel` subclass (a pydantic model). */
API::Node subclassRef() {
result = API::moduleImport("pydantic").getMember("BaseModel").getASubclass+()
or
result = ModelOutput::getATypeNode("pydantic.BaseModel~Subclass").getASubclass*()
}
/**
* A source of instances of `pydantic.BaseModel` subclasses, extend this class to model new instances.
*
* This can include instantiations of the class, return values from function
* calls, or a special parameter that will be set when functions are called by an external
* library.
*
* Use the predicate `BaseModel::instance()` to get references to instances of `pydantic.BaseModel`.
*/
abstract class InstanceSource extends DataFlow::LocalSourceNode { }
/** Gets a reference to an instance of a `pydantic.BaseModel` subclass. */
private DataFlow::TypeTrackingNode instance(DataFlow::TypeTracker t) {
t.start() and
result instanceof InstanceSource
or
t.start() and
instanceStepToPydanticModel(_, result)
or
exists(DataFlow::TypeTracker t2 | result = instance(t2).track(t2, t))
}
/** Gets a reference to an instance of a `pydantic.BaseModel` subclass. */
DataFlow::Node instance() { instance(DataFlow::TypeTracker::end()).flowsTo(result) }
/**
* A step from an instance of a `pydantic.BaseModel` subclass, that might result in
* an instance of a `pydantic.BaseModel` subclass.
*
* NOTE: We currently overapproximate, and treat all attributes as containing
* another pydantic model. For the code below, we _could_ limit this to `main_foo`
* and members of `other_foos`. IF THIS IS CHANGED, YOU MUST CHANGE THE ADDITIONAL
* TAINT STEPS BELOW, SUCH THAT SIMPLE ACCESS OF SOMETHING LIKE `str` IS STILL
* TAINTED.
*
*
* ```py
* class MyComplexModel(BaseModel):
* field: str
* main_foo: Foo
* other_foos: List[Foo]
* ```
*/
private predicate instanceStepToPydanticModel(DataFlow::Node nodeFrom, DataFlow::Node nodeTo) {
// attributes (such as `model.foo`)
nodeFrom = instance() and
nodeTo.(DataFlow::AttrRead).getObject() = nodeFrom
or
// subscripts on attributes (such as `model.foo[0]`). This needs to handle nested
// lists (such as `model.foo[0][0]`), and access being split into multiple
// statements (such as `xs = model.foo; xs[0]`).
//
// To handle this we overapproximate which things are a Pydantic model, by
// treating any subscript on anything that originates on a Pydantic model to also
// be a Pydantic model. So `model[0]` will be an overapproximation, but should not
// really cause problems (since we don't expect real code to contain such accesses)
nodeFrom = instance() and
nodeTo.asCfgNode().(Cfg::SubscriptNode).getObject() = nodeFrom.asCfgNode()
}
/**
* Extra taint propagation for `pydantic.BaseModel` subclasses. (note that these could also be `pydantic.BaseModel` subclasses)
*/
private class AdditionalTaintStep extends TaintTracking::AdditionalTaintStep {
override predicate step(DataFlow::Node nodeFrom, DataFlow::Node nodeTo) {
// NOTE: if `instanceStepToPydanticModel` is changed to be more precise, these
// taint steps should be expanded, such that a field that has type `str` is
// still tainted.
instanceStepToPydanticModel(nodeFrom, nodeTo)
}
}
}
}