| name | contrib-op-shape-inference-memory-safety |
| description | Audit and fix out-of-range output writes in ONNX Runtime operator shape-inference functions. Use when reviewing or fixing a contrib (or standard) op TypeAndShapeInference where a getNumOutputs() guard precedes a write to a higher output index - optional trailing outputs make a smaller output count schema-valid, so getOutputType(index) can run one past the declared outputs at Graph::Resolve. |
Contrib-Op Shape-Inference Output-Index Safety
Reusable method for finding and fixing the bug class where an operator's
TypeAndShapeInference function guards an output write with getNumOutputs() > N but then
writes an output index greater than N. For a node that declares fewer outputs, the
written index is past the end of the inference context's output vector.
Scope: schema-level shape inference in onnxruntime/core/graph/contrib_ops/*.cc and
shape_inference_functions.cc. This runs once during Graph::Resolve (model-load time),
EP-agnostic - there is no per-EP (CPU/CUDA/ROCm) kernel duplicate of this code to
chase. Op kernels allocate outputs via the bounds-safe OpKernelContext::Output(index)
and are a separate concern.
1. The pattern
propagateElemTypeFromInputToOutput(ctx, 0, 0);
if (ctx.getNumOutputs() > 1) {
propagateElemTypeFromInputToOutput(ctx, 0, 1);
propagateElemTypeFromInputToOutput(ctx, 0, 2);
}
The guard getNumOutputs() > 1 admits a node with exactly 2 outputs (indices 0, 1), yet
the body writes index 2. The implication "> 1 ⇒ index 2 exists" is false: > 1 only
guarantees indices 0 and 1.
Why a smaller output count is valid
Trailing outputs declared OpSchema::Optional lower min_output. ONNX derives
min_output = number of required outputs, max_output = total declared. The model checker
(checker::check_node) only enforces min_output <= N <= max_output.
| Op | Output decls | min / max | A 2-output node? |
|---|
DecoderAttention | out (req), new_key_cache (Opt), new_value_cache (Opt) | 1 / 3 | passes checker |
MultiHeadAttention | out (req), present_key (Opt), present_value (Opt), qk (Opt) | 1 / 4 | passes checker |
DecoderMaskedMultiHeadAttention | out (req) + 3 Optional | 1 / 4 | passes checker |
So a node with output=['out','present_key'] is schema-valid, passes the checker, and then
reaches the index-2 write. A passing checker is not a guarantee the index is in range.
2. The sink (why the write is not caught)
const TypeProto* getInputType(size_t index) const override {
return node_.InputDefs().at(index)->TypeAsProto();
}
TypeProto* getOutputType(size_t index) override {
return &node_output_types_[index];
}
node_output_types_ is sized to node.OutputDefs().size() in the InferenceContextImpl
ctor, so for a 2-output node it has 2 elements; getOutputType(2) returns one past the end.
getInputType uses .at() (would throw on a bad index); getOutputType uses raw
operator[] (no check) - the asymmetry is the root cause.
- The call runs at
Graph::Resolve → InferAndVerifyTypeMatch → RunInferencing. The
surrounding ORT_TRY/ORT_CATCH(const std::exception&) only catches thrown
fail_shape_inference; a raw out-of-range operator[] does not throw, so the catch does
not help.
- Because this is schema-level inference, it is EP-independent - no CUDA/ROCm copy.
3. Audit technique — always sweep siblings
Do not stop at the reported function. Grep every shape-inference guard and compare its
threshold against the highest output index written before the next guard.
git grep -n 'getNumOutputs' -- \
onnxruntime/core/graph/contrib_ops/*.cc \
onnxruntime/core/graph/contrib_ops/shape_inference_functions.cc
For each if (ctx.getNumOutputs() > N) block, find the largest index passed to
propagateElemTypeFromInputToOutput(ctx, _, index) / updateOutputShape(ctx, index, _) /
getOutputType(index) inside it. Rule: the guard must require strictly more outputs than
the highest index written (write index k ⇒ guard must ensure getNumOutputs() > k).
Correct exemplars already in the tree to copy:
| Exemplar | Pattern | Why it is safe |
|---|
BaseGroupQueryAttention... | if (getNumOutputs() >= 3) then writes idx 2 | guard covers highest index |
PagedAttention... | nested > 1 + inner if (getNumOutputs() != 3) fail_shape_inference | fails before any write |
EmbedLayerNormalizationShapeInference | > 2 then writes idx 2 | fixed by PR #28176 (precedent) |
SkipLayerNormalizationShapeInference | each idx k guarded by > k | per-index guard |
Gotcha — conditional writes can hide a vacuous audit. A write may sit behind an inner
condition (e.g. hasInputShape(past_key_index) before writing index 2). The site is still
a bug, but you can only observe it when that inner condition is also satisfied. Keep this
in mind both for the audit and for tests (§5).
4. Fix patterns
Point fix (required): raise the guard to cover the highest index written.
if (ctx.getNumOutputs() > 1) { ... writes idx 2 ... }
if (ctx.getNumOutputs() > 2) {
...
}
Justify the threshold with the op's output semantics. For these attention ops the two trailing
outputs - present_key (idx 1) and present_value (idx 2) for MultiHeadAttention,
new_key_cache / new_value_cache for DecoderAttention (see the §1 table for each op's
exact output names) - are a both-or-neither pair: there is no valid configuration that
emits one without the other, so requiring all three outputs before populating indices 1 and 2
is behavior-preserving. (PagedAttention encodes the same invariant via its nested != 3
check.)
Defense-in-depth (recommended): bound the sink so a future author cannot reintroduce the
class.
TypeProto* getOutputType(size_t index) override {
if (index >= node_output_types_.size()) {
fail_type_inference("output index ", index, " is out of range; node has ",
node_output_types_.size(), " outputs");
}
return &node_output_types_[index];
}
This mirrors getInputType's .at() and the existing bounds checks in the sibling
DataPropagationContextImpl. Placing it at the base layer transitively protects the NHWC and
quantization wrapper contexts. After the point fix this branch is unreachable through a normal
model (the guard already prevents the out-of-range index), so it is pure defense-in-depth. Its
failure mode is build-dependent: with exceptions enabled, fail_type_inference raises
InferenceError (a std::exception), caught by the existing ORT_CATCH(const std::exception&)
around RunInferencing and surfaced as a clean load-time error; under ORT_NO_EXCEPTIONS it is
not compiled out - ONNX's no-exceptions path prints the message to std::cerr and calls
abort(), a deterministic fail-fast (consistent with getInputType's .at(), which likewise
terminates under no-exceptions). Either way the result is a controlled failure rather than an
out-of-range write.
5. Test recipe
Tests live in onnxruntime/test/contrib_ops/*.cc and are auto-globbed into the
onnxruntime_provider_test target by cmake/onnxruntime_unittests.cmake
(test/contrib_ops/*.cc pattern) - no cmake edit needed for a new file. See the
ort-test skill for the executable taxonomy (onnxruntime_provider_test vs
onnxruntime_test_all).
Rules that make the regression test actually guard the fix:
- Drive through
Model + Graph::Resolve, not ONNX's standalone TestShapeInference.
Only the full resolve path constructs the real InferenceContextImpl and hits the
getOutputType sink described in §2. A standalone ONNX shape-inference helper uses a
different context and bypasses the sink, so it cannot reproduce the bug.
- Negative tests must be NON-VACUOUS - they must actually enter the write branch on
pre-fix source. If a write is gated by an inner condition (§3 gotcha), satisfy it: e.g. for
MultiHeadAttention/DecoderMaskedMultiHeadAttention, supply a shaped past_key
(and past_sequence_length / past_present_share_buffer as the op requires) so the
index-2 block runs. A negative test that only supplies query skips the block and passes
even on pre-fix source - regression-proof in name only.
- Add positive (all-outputs) cases: a node with every output present must still infer the
trailing output types - proves the tightened guard did not over-restrict.
- Keep tests throw-free post-fix so they are valid under
ORT_NO_EXCEPTIONS. Any case
that is expected to fail_shape_inference (throws) must be excluded with
#ifndef ORT_NO_EXCEPTIONS. The "2 outputs must not go out of range" case is throw-free
after the point fix and is safe in all builds.
Verify the negative test is non-vacuous (sanitizer A/B) - the most reliable way to prove a
negative test enters the previously-out-of-range branch: build the test at the pre-fix
commit with AddressSanitizer and confirm it flags the out-of-range output access; then
confirm it is clean after the fix.
cmake --build build/Linux/Debug --target onnxruntime_provider_test -j"$(nproc)"
./build/Linux/Debug/onnxruntime_provider_test \
--gtest_filter='AttentionOptionalOutputsShapeInferenceTest.*'
git worktree add --detach ../ort-prefix-check <fix_commit>~1
python3 tools/ci_build/build.py --build_dir build/asan --config Debug --parallel \
--skip_tests --enable_address_sanitizer --skip_submodule_sync \
--cmake_generator Ninja --target onnxruntime_provider_test
6. Process / wording conventions
- Run
lintrunner -a before pushing so the CLANGFORMAT / Python-format gate passes. See
the ort-lint skill.
- Use correctness/robustness framing in code, comments, commit messages, and the PR body
- describe the change as fixing an optional-output guard, not as a security fix. This
matches repo convention (compare
python-kwargs-setattr-security) and keeps the PR neutral.
7. Audit checklist (per-operator review)
When reviewing or hardening any operator implementation or its shape inference:
References
- PR #28176 - "Fix ... in EmbedLayerNormalizationShapeInference": the precedent that fixed
the identical
> 1 → > 2 primitive in one site; the sibling attention sites were missed,
motivating the sweep in §3.
- PR #29268 - this fix: guards corrected in
DecoderAttention / MultiHeadAttention /
DecoderMaskedMultiHeadAttention shape inference, plus the getOutputType bounds check and
non-vacuous regression tests.
- Sibling skill:
ort-test (test executables, --gtest_filter, contrib-op test layout);
ort-lint (lintrunner -a); ort-build (build flags, ASan).