Missing validation during checkpoint loadingGHSA-7pxj-m4jf-r6h2
HIGHFix: tensorflow/tensorflow@368af87GHSA-7pxj-m4jf-r6h2 is a high-severity (CVSS 7.8) CWE-345 vulnerability in tensorflow. A fix is available for tensorflow — see the affected versions and patch details below.
EPSS Exploitation Probability
Probability of exploitation in the next 30 days, from FIRST.org EPSS.
How urgent is this, really
GHSA-7pxj-m4jf-r6h2 by exploitation likelihood (EPSS) against impact (CVSS). Outside the shaded patch-first corner.
Where this sits among everything scored
Of 385,738 CVEs with a current EPSS score, this one falls in the < 10% band (highlighted). Counts from FIRST.org, log-scaled.
Real-World Exposure
tensorflow🐍tensorflow🐍tensorflow🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-gpu🐍tensorflow-gpu+1 moreReal-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
An attacker can trigger undefined behavior, integer overflows, segfaults and CHECK-fail crashes if they can change saved checkpoints from outside of TensorFlow.
This is because the checkpoints loading infrastructure is missing validation for invalid file formats.
Patches
We have patched the issue in GitHub commits b619c6f865715ca3b15ef1842b5b95edbaa710ad, e8dc63704c88007ee4713076605c90188d66f3d2, 368af875869a204b4ac552b9ddda59f6a46a56ec, and abcced051cb1bd8fb05046ac3b6023a7ebcc4578.
These fixes will be included in TensorFlow 2.7.0. We will also cherrypick these commits 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.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | tensorflow | ≥ 2.6.0&&< 2.6.1 | 2.6.1pip install --upgrade 'tensorflow==2.6.1' |
| 🐍PyPI | tensorflow | ≥ 2.5.0&&< 2.5.2 | 2.5.2pip install --upgrade 'tensorflow==2.5.2' |
| 🐍PyPI | tensorflow | all versions | 2.4.4pip install --upgrade 'tensorflow==2.4.4' |
| 🐍PyPI | tensorflow-cpu | ≥ 2.6.0&&< 2.6.1 | 2.6.1pip install --upgrade 'tensorflow-cpu==2.6.1' |
| 🐍PyPI | tensorflow-cpu | ≥ 2.5.0&&< 2.5.2 | 2.5.2pip install --upgrade 'tensorflow-cpu==2.5.2' |
| 🐍PyPI | tensorflow-cpu | all versions | 2.4.4pip install --upgrade 'tensorflow-cpu==2.4.4' |
Affected Products
tensorflowgoogleDetection & mitigation playbook
Open-source dependencyDetect
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.
Fix
Update tensorflow to 2.6.1 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-7pxj-m4jf-r6h2 is resolved across your whole dependency graph.
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
Is GHSA-7pxj-m4jf-r6h2 in your dependencies?
Find it across PyPI, including transitive dependencies.