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GHSA-h246-cgh4-7475 tensorflow

MEDIUMFix: tensorflow/tensorflow@8310bf8

GHSA-h246-cgh4-7475 is a medium-severity (CVSS 4.8) CWE-704 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.

`CHECK` fail in `BCast` overflow

Also known asBIT-tensorflow-2022-41890CVE-2022-41890PYSEC-2026-1001PYSEC-2026-3194PYSEC-2026-3333
Published
Nov 21, 2022
Updated
Jul 13, 2026
Affected
9 pkgs
Patched
9 / 9
Exploits
1 known
Exploitation data as of Sep 20, 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-h246-cgh4-7475.

EPSS Exploitation Probability

via FIRST.org ↗
0.5%probability of exploitation in next 30 days
Lower Risk0.00%
Lower risk than most CVEs40th percentile — riskier than 40% of all scored CVEsHighest risk

EPSS (Exploit Prediction Scoring System) is a daily probability model maintained by FIRST.org. It estimates the likelihood a CVE will be exploited in production environments within the next 30 days, derived from real-world threat intelligence signals.

How urgent is this, really

GHSA-h246-cgh4-7475 plotted by exploitation likelihood (EPSS) against impact (CVSS). The shaded corner — EPSS 50%+ and CVSS 7.0+ — is where this CVE doesn't sit, though severity or exploitability alone can still warrant action.

Where this sits among everything scored

Of 377,333 CVEs with a current EPSS score, this one falls in the < 10% band (highlighted). Real counts from FIRST.org, not a sample — log-scaled since the landscape is heavily right-skewed.

Real-World Exposure

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

If BCast::ToShape is given input larger than an int32, it will crash, despite being supposed to handle up to an int64. An example can be seen in tf.experimental.numpy.outer by passing in large input to the input b.

import tensorflow as tf
value = tf.constant(shape=[2, 1024, 1024, 1024], value=False)
tf.experimental.numpy.outer(a=6,b=value)

Patches

We have patched the issue in GitHub commit 8310bf8dd188ff780e7fc53245058215a05bdbe5.

The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, 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 by Pattarakrit Rattankul.

Affected Packages

9 total 9 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPItensorflowall versions2.8.4pip install --upgrade 'tensorflow==2.8.4'
🐍PyPItensorflow2.9.0&&< 2.9.32.9.3pip install --upgrade 'tensorflow==2.9.3'
🐍PyPItensorflow2.10.0&&< 2.10.12.10.1pip install --upgrade 'tensorflow==2.10.1'
🐍PyPItensorflow-cpuall versions2.8.4pip install --upgrade 'tensorflow-cpu==2.8.4'
🐍PyPItensorflow-gpuall versions2.8.4pip install --upgrade 'tensorflow-gpu==2.8.4'
🐍PyPItensorflow-cpu2.9.0&&< 2.9.32.9.3pip install --upgrade 'tensorflow-cpu==2.9.3'
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.

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 2.8.4 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-h246-cgh4-7475 is resolved across your whole dependency graph.

  3. Workarounds

    If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.

  4. How O3 protects you

    O3 Security's impact-aware SCA analyses which vulnerable code paths your application actually calls, so a match like GHSA-h246-cgh4-7475 can be triaged on real exposure rather than presence alone.

Tailored to GHSA-h246-cgh4-7475. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.

Frequently Asked Questions

### Impact If [`BCast::ToShape`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/util/bcast.h) is given input larger than an `int32`, it will crash, despite being supposed to handle up to an `int64`. An example can be seen in [`tf.experimental.numpy.outer`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/util/bcast.h) by passing in large input to the input `b`. ```python import tensorflow as tf value = tf.constant(shape=[2, 1024, 1024, 1024], value=False) tf.experimental.numpy.outer(a=6,b=value) ``` ### Patches We have patched the issue in GitHub comm
O3 Security · Impact-Aware SCA

Is GHSA-h246-cgh4-7475 in your dependencies?

O3 Security finds GHSA-h246-cgh4-7475 across PyPI dependencies, including transitive ones, and its impact-aware SCA ranks findings by whether your code actually calls the vulnerable path.

GHSA-h246-cgh4-7475: tensorflow | O3 Security