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>
Preparatory refactor for the shared-CFG dataflow migration.
Deprecates the AstNode.getAFlowNode() cached predicate on the public
Python QL API and rewrites all ~140 internal callers across lib/, src/,
test/, and tools/ from `expr.getAFlowNode() = cfgNode` to
`cfgNode.getNode() = expr`, using ControlFlowNode.getNode() which
already exists in Flow.qll.
The predicate itself is preserved (with a deprecation note pointing at
the new pattern) so external users do not experience churn — they can
migrate at their own pace and the AST/CFG hierarchies still get the
intended untangling once the deprecation eventually elapses.
Semantic noop verified by:
- All 361 lib/ + src/ queries compile clean.
- All 122 ControlFlow + PointsTo library-tests pass.
- All 64 dataflow library-tests pass.
- All 113 Variables/Exceptions/Expressions/Statements/Functions/Imports/
Security/CWE-798/ModificationOfParameterWithDefault query-tests pass.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
The fix may look a bit obscure, so here's what's going on.
When we see `from . import helper`, we create an `ImportExpr` with level
equal to 1 (corresponding to the number of dots). To resolve such
imports, we compute the name of the enclosing package, as part of
`ImportExpr.qualifiedTopName()`. For this form of import expression, it
is equivalent to `this.getEnclosingModule().getPackageName()`. But
`qualifiedTopName` requires that `valid_module_name` holds for its
result, and this was _not_ the case for namespace packages.
To fix this, we extend `valid_module_name` to include the module names
of _any_ folder, not just regular package (which are the ones where
there's a `__init__.py` in the folder). Note that this doesn't simply
include all folders -- only the ones that result in valid module names
in Python.
With `ModuleVariableNode`s now appearing for _all_ global variables (not
just the ones that actually seem to be used), some of the tests changed
a bit. Mostly this was in the form of new flow (because of new nodes
that popped into existence). For some inline expectation tests, I opted
to instead exclude these results, as there was no suitable location to
annotate. For the normal tests, I just accepted the output (after having
vetted it carefully, of course).
This pull request introduces a new CodeQL query for detecting prompt injection vulnerabilities in Python code targeting AI prompting APIs such as agents and openai. The changes includes a new experimental query, new taint flow and type models, a customizable dataflow configuration, documentation, and comprehensive test coverage.
Also fixes an issue with the return type annotations that caused these
to not work properly.
Currently, annotated assignments don't work properly, due to the fact
that our flow relation doesn't consider flow going to the "type" part of
an annotated assignment. This means that in `x : Foo`, we do correctly
note that `x` is annotated with `Foo`, but we have no idea what `Foo`
is, since it has no incoming flow.
To fix this we should probably just extend the flow relation, but this
may need to be done with some care, so I have left it as future work.
Since using `.DictionaryElementAny` doesn't actually do a store on the
source, (so we can later follow any dict read-steps).
I added the ensure_tainted steps to highlight that the result of the
WHOLE expression ends up "tainted", and that we don't just mark
`os.environ` as the source without further flow.
