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MEDIUM severity

Crash in `tf.math.segment_*` operationsGHSA-cq76-mxrc-vchh

MEDIUMFix: tensorflow/tensorflow#51733

GHSA-cq76-mxrc-vchh is a medium-severity (CVSS 5.5) CWE-190 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-2021-41195CVE-2021-41195PYSEC-2021-842PYSEC-2021-844PYSEC-2021-846
Published
Updated
Affected
9 pkgs
Patched
9 / 9
Exploits
1 known
Exploitation data as of Oct 11, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

EPSS Exploitation Probability

via FIRST.org ↗
0.2%probability of exploitation in next 30 days
Lower Risk+0.17%
Lower risk than most CVEs11th percentile — riskier than 11% of all scored CVEsHighest risk
0.00%0.24%0.47%0.71%0.0%0.2%Apr 26Oct 26

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

How urgent is this, really

GHSA-cq76-mxrc-vchh 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

The implementation of tf.math.segment_* operations results in a CHECK-fail related abort (and denial of service) if a segment id in segment_ids is large.

import tensorflow as tf

tf.math.segment_max(data=np.ones((1,10,1)), segment_ids=[1676240524292489355])
tf.math.segment_min(data=np.ones((1,10,1)), segment_ids=[1676240524292489355])
tf.math.segment_mean(data=np.ones((1,10,1)), segment_ids=[1676240524292489355])    
tf.math.segment_sum(data=np.ones((1,10,1)), segment_ids=[1676240524292489355])
tf.math.segment_prod(data=np.ones((1,10,1)), segment_ids=[1676240524292489355])

This is similar to CVE-2021-29584 (and similar other reported vulnerabilities in TensorFlow, localized to specific APIs): the implementation (both on CPU and GPU) computes the output shape using AddDim. However, if the number of elements in the tensor overflows an int64_t value, AddDim results in a CHECK failure which provokes a std::abort. Instead, code should use AddDimWithStatus.

Patches

We have patched the issue in GitHub commit e9c81c1e1a9cd8dd31f4e83676cab61b60658429 (merging #51733).

The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, 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.

Attribution

This vulnerability has been reported externally via a GitHub issue.

Affected Packages

9 total 9 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPItensorflow≥ 2.6.0&&< 2.6.12.6.1pip install --upgrade 'tensorflow==2.6.1'
🐍PyPItensorflow≥ 2.5.0&&< 2.5.22.5.2pip install --upgrade 'tensorflow==2.5.2'
🐍PyPItensorflowall versions2.4.4pip install --upgrade 'tensorflow==2.4.4'
🐍PyPItensorflow-cpu≥ 2.6.0&&< 2.6.12.6.1pip install --upgrade 'tensorflow-cpu==2.6.1'
🐍PyPItensorflow-cpu≥ 2.5.0&&< 2.5.22.5.2pip install --upgrade 'tensorflow-cpu==2.5.2'
🐍PyPItensorflow-cpuall versions2.4.4pip install --upgrade 'tensorflow-cpu==2.4.4'

Affected Products

1 product · 3 configurations
Application
tensorflowgoogle
≥ 2.6.0 && < 2.6.1
range
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 platform for machine learning. In affected …

github.comNVDNov 2021

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 2.6.1 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-cq76-mxrc-vchh is resolved across your whole dependency graph.

  3. Workarounds

    Constrain what reaches the vulnerable code: limit the size and shape of untrusted input, isolate the affected component in a sandboxed or least-privileged process, and enable the platform's memory-safety mitigations (ASLR, stack protector, hardened allocator) so an out-of-bounds access is more likely to fail closed than to be exploitable.

Frequently Asked Questions

### Impact The implementation of `tf.math.segment_*` operations results in a `CHECK`-fail related abort (and denial of service) if a segment id in `segment_ids` is large. ```python import tensorflow as tf tf.math.segment_max(data=np.ones((1,10,1)), segment_ids=[1676240524292489355]) tf.math.segment_min(data=np.ones((1,10,1)), segment_ids=[1676240524292489355]) tf.math.segment_mean(data=np.ones((1,10,1)), segment_ids=[1676240524292489355]) tf.math.segment_sum(data=np.ones((1,10,1)), segment_ids=[1676240524292489355]) tf.math.segment_prod(data=np.ones((1,10,1)), segment_ids=[16762405242924
O3 Security · Impact-Aware SCA

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Crash in `tf.math.segment_*` operations (Medium 5.5)