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MEDIUM severity

CVE-2022-29209 tensorflow

MEDIUMFix: tensorflow/tensorflow@b917181

CVE-2022-29209 is a medium-severity (CVSS 5.5) CWE-843 vulnerability in tensorflow. 3 public exploit references exist, so weaponization risk is real. A fix is available for tensorflow — see the affected versions and patch details below.

Type confusion leading to `CHECK`-failure based denial of service in TensorFlow

Also known asBIT-tensorflow-2022-29209GHSA-f4rr-5m7v-wxcwPYSEC-2026-3167PYSEC-2026-3315PYSEC-2026-986
Published
May 20, 2022
Updated
Aug 12, 2026
Affected
9 pkgs
Patched
9 / 9
Exploits
3 known
Exploitation data as of Sep 21, 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 CVE-2022-29209.

EPSS Exploitation Probability

via FIRST.org ↗
0.4%probability of exploitation in next 30 days
Lower Risk0.00%
Lower risk than most CVEs33th percentile — riskier than 33% 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

CVE-2022-29209 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 377,636 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-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 macros that TensorFlow uses for writing assertions (e.g., CHECK_LT, CHECK_GT, etc.) have an incorrect logic when comparing size_t and int values. Due to type conversion rules, several of the macros would trigger incorrectly.

Patches

We have patched the issue in GitHub commit b917181c29b50cb83399ba41f4d938dc369109a1 (merging GitHub PR #55730).

The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, 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
🐍PyPItensorflowall versions2.6.4pip install --upgrade 'tensorflow==2.6.4'
🐍PyPItensorflow2.7.0&&< 2.7.22.7.2pip install --upgrade 'tensorflow==2.7.2'
🐍PyPItensorflow2.8.0&&< 2.8.12.8.1pip install --upgrade 'tensorflow==2.8.1'
🐍PyPItensorflow-cpuall versions2.6.4pip install --upgrade 'tensorflow-cpu==2.6.4'
🐍PyPItensorflow-cpu2.7.0&&< 2.7.22.7.2pip install --upgrade 'tensorflow-cpu==2.7.2'
🐍PyPItensorflow-cpu2.8.0&&< 2.8.12.8.1pip install --upgrade 'tensorflow-cpu==2.8.1'
Exploits & PoCs
3

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 2.6.4 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2022-29209 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 CVE-2022-29209 can be triaged on real exposure rather than presence alone.

Tailored to CVE-2022-29209. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.

Frequently Asked Questions

### Impact The [macros that TensorFlow uses for writing assertions (e.g., `CHECK_LT`, `CHECK_GT`, etc.)](https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/platform/default/logging.h) have an incorrect logic when comparing `size_t` and `int` values. Due to type conversion rules, several of the macros would trigger incorrectly. ### Patches We have patched the issue in GitHub commit [b917181c29b50cb83399ba41f4d938dc369109a1](https://github.com/tensorflow/tensorflow/commit/b917181c29b50cb83399ba41f4d938dc369109a1) (merging GitHub PR [#55730](ht
O3 Security · Impact-Aware SCA

Is CVE-2022-29209 in your dependencies?

O3 Security finds CVE-2022-29209 across PyPI dependencies, including transitive ones, and its impact-aware SCA ranks findings by whether your code actually calls the vulnerable path.

CVE-2022-29209: tensorflow (Medium 5.5) | O3 Security