Is there a patch available for CVE-2026-11329?

Yes, a patch is available for CVE-2026-11329. Update the affected software to the latest version immediately.

onnx-mlir CVE-2026-11329

Q: What is the CVSS score of CVE-2026-11329?

CVE-2026-11329 has a CVSS 4.0 base score of 2.0 (Low). CVSS vector: CVSS:4.0/AV:L/AC:H/AT:N/PR:L/UI:N/VC:N/VI:L/VA:L/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X. EPSS exploitation probability: 0.0%.

EUVD-2026-34826 LOW

Use of Weak Hash (CWE-328)

2026-06-05 VulDB

GHSA-h7gg-9w38-vh5g

Information Disclosure Python

2.0

CVSS 4.0 · NVD

Severity by source

NVD PRIMARY

2.0 LOW

CVSS:4.0/AV:L/AC:H/AT:N/PR:L/UI:N/VC:N/VI:L/VA:L/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X

Primary rating from NVD · only source for this CVE.

CVSS VectorNVD

CVSS:4.0/AV:L/AC:H/AT:N/PR:L/UI:N/VC:N/VI:L/VA:L/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X

Attack Vector

Local

Attack Complexity

High

Privileges Required

Low

User Interaction

None

Scope

Lifecycle Timeline

CVSS changed

Jun 05, 2026 - 13:22 NVD

3.6 (LOW) 2.0 (LOW)

Source Code Evidence Fetched

Jun 05, 2026 - 13:16 vuln.today

Analysis Generated

Jun 05, 2026 - 13:16 vuln.today

DescriptionCVE.org

A vulnerability has been found in onnx onnx-mlir up to 0.5.0.0. Affected by this issue is the function generate_hash_key of the file src/Runtime/python/torch_onnxmlir/src/torch_onnxmlir/backend.py of the component Placeholder Node Cache Handler. Such manipulation leads to use of weak hash. An attack has to be approached locally. A high complexity level is associated with this attack. The exploitation is known to be difficult. The name of the patch is 72c5187ff6d13c2c2b3d3789b8f5faf99f08a5b4. Applying a patch is advised to resolve this issue.

AnalysisAI

Weak cache key construction in onnx-mlir's torch backend (versions up to 0.5.0.0) omits tensor data type (dtype) from placeholder node hash keys, enabling cache collisions between semantically distinct nodes. A locally authenticated attacker with high-complexity manipulation can cause the compiler to incorrectly reuse cached compilation results across mismatched dtypes, yielding low-integrity and low-availability impacts. No public exploit is identified at time of analysis; the upstream fix is confirmed via commit 72c5187 and PR #3427.

Technical ContextAI

onnx-mlir is a compiler toolchain that lowers ONNX model graphs to native machine code. The affected component is the torch_onnxmlir Python backend (src/Runtime/python/torch_onnxmlir/src/torch_onnxmlir/backend.py), where the generate_hash_key function constructs a cache key for placeholder nodes as f'om_placeholder_{placeholder_counter}_[{shape_str}]' - encoding only tensor shape, not dtype. CWE-328 (Use of Weak Hash) applies because this key omits a critical distinguishing attribute: two placeholder nodes with identical shapes but different data types (e.g., float32 vs. int64) hash to the same value. The fix in PR #3427 appends _{dtype} to produce f'om_placeholder_{placeholder_counter}_[{shape_str}]_{dtype}', making the key collision-resistant for differing dtypes. CPE cpe:2.3:a:onnx:onnx-mlir:*:*:*:*:*:*:*:* covers all versions through 0.5.0.0.

RemediationAI

Apply the upstream fix by incorporating commit 72c5187ff6d13c2c2b3d3789b8f5faf99f08a5b4, available via PR #3427 at https://github.com/onnx/onnx-mlir/pull/3427. The patch modifies generate_hash_key to append the tensor dtype to the placeholder cache key, eliminating collisions between same-shape, different-dtype nodes. An exact released tagged version incorporating this fix has not been independently confirmed from available intelligence - users should verify that their target release includes this specific commit before considering remediation complete. As a compensating control pending patch deployment, ML engineers compiling models with the torch_onnxmlir backend should avoid or disable placeholder node caching in workflows that involve multiple dtypes sharing identical tensor shapes; note this may degrade compilation performance. The official fix is the only fully effective remediation.