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MEDIUM severity

`CHECK`-failures in binary ops in TensorflowGHSA-gjqc-q9g6-q2j3

MEDIUMFix: tensorflow/tensorflow@a7c02f1

GHSA-gjqc-q9g6-q2j3 is a medium-severity (CVSS 6.5) Reachable Assertion vulnerability in tensorflow. 1 public exploit reference exists, so weaponization risk is real. A fix is available for tensorflow — see the affected versions and patch details below.

Also known asBIT-tensorflow-2022-23583CVE-2022-23583PYSEC-2022-147PYSEC-2022-92PYSEC-2026-3191
Published
Updated
Affected
9 pkgs
Patched
9 / 9
Exploits
1 known
Exploitation data as of Oct 12, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

Exploitation Status

Proof-of-concept exploit code exists

  • CISA’s SSVC triage found public proof-of-concept exploit code for this CVE, though no confirmed active exploitation.

Exploitation and automatability from CISA’s SSVC triage for GHSA-gjqc-q9g6-q2j3.

EPSS Exploitation Probability

via FIRST.org ↗
0.9%probability of exploitation in next 30 days
Lower Risk0.00%
Lower risk than most CVEs58th percentile — riskier than 58% of all scored CVEsHighest risk
0.00%0.46%0.92%1.38%0.3%0.9%Apr 26Jul 26Oct 26

Probability of exploitation in the next 30 days, from FIRST.org EPSS.

How urgent is this, really

GHSA-gjqc-q9g6-q2j3 by exploitation likelihood (EPSS) against impact (CVSS). Outside the shaded patch-first corner.

Where this sits among everything scored

Of 385,987 CVEs with a current EPSS score, this one falls in the < 10% band (highlighted). Counts from FIRST.org, log-scaled.

Real-World Exposure

9 pkgs affected
🐍tensorflow🐍tensorflow🐍tensorflow🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-gpu🐍tensorflow-gpu+1 more

Real-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.

Description

Impact

A malicious user can cause a denial of service by altering a SavedModel such that any binary op would trigger CHECK failures. This occurs when the protobuf part corresponding to the tensor arguments is modified such that the dtype no longer matches the dtype expected by the op. In that case, calling the templated binary operator for the binary op would receive corrupted data, due to the type confusion involved:

functor::BinaryFunctor<Device, Functor, 1>()(
    eigen_device, out->template flat<Tout>(),
    input_0.template flat<Tin>(), input_1.template flat<Tin>(),
    error_ptr);

If Tin and Tout don't match the type of data in out and input_* tensors then flat<*> would interpret it wrongly. In most cases, this would be a silent failure, but we have noticed scenarios where this results in a CHECK crash, hence a denial of service.

Patches

We have patched the issue in GitHub commit a7c02f1a9bbc35473969618a09ee5f9f5d3e52d9. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Affected Packages

9 total 9 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPItensorflowall versions2.5.3pip install --upgrade 'tensorflow==2.5.3'
🐍PyPItensorflow≥ 2.6.0&&< 2.6.32.6.3pip install --upgrade 'tensorflow==2.6.3'
🐍PyPItensorflow≥ 2.7.0&&< 2.7.12.7.1pip install --upgrade 'tensorflow==2.7.1'
🐍PyPItensorflow-cpuall versions2.5.3pip install --upgrade 'tensorflow-cpu==2.5.3'
🐍PyPItensorflow-cpu≥ 2.6.0&&< 2.6.32.6.3pip install --upgrade 'tensorflow-cpu==2.6.3'
🐍PyPItensorflow-cpu≥ 2.7.0&&< 2.7.12.7.1pip install --upgrade 'tensorflow-cpu==2.7.1'

Affected Products

1 product · 3 configurations
Application
tensorflowgoogle
≥ 2.6.0 && ≤ 2.6.2
1 version
2.7.0
Exploits & PoCs
1

Research use only. For defensive security, authorized penetration testing, and academic research only. Never execute exploit code against systems without explicit written authorization.

Tensorflow is an Open Source Machine Learning Framework. A malicious use…

github.comNVDFeb 2022

Detection & mitigation playbook

Open-source dependency

  1. Detect

    Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for tensorflow, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.

  2. Fix

    Update tensorflow to 2.5.3 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-gjqc-q9g6-q2j3 is resolved across your whole dependency graph.

  3. Workarounds

    Cap what an attacker can consume: apply request size, rate and timeout limits in front of the affected component, and run it with memory and CPU limits so exhaustion degrades one worker rather than the whole service.

Frequently Asked Questions

### Impact A malicious user can cause a denial of service by altering a `SavedModel` such that [any binary op](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/kernels/cwise_ops_common.h#L88-L137) would trigger `CHECK` failures. This occurs when the protobuf part corresponding to the tensor arguments is modified such that the `dtype` no longer matches the `dtype` expected by the op. In that case, calling the templated binary operator for the binary op would receive corrupted data, due to the type confusion involved: ```cc functor::BinaryFu
O3 Security · Impact-Aware SCA

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`CHECK`-failures in binary ops in Tensorflow (Medium 6.5)