Tensorflow vulnerable to Out-of-Bounds ReadGHSA-8w5g-3wcv-9g2j
MEDIUMFix: tensorflow/tensorflow@b389f5cGHSA-8w5g-3wcv-9g2j is a medium-severity (CVSS 6.8) Out-of-bounds Read vulnerability in tensorflow. 1 public exploit or proof-of-concept reference is indexed, so weaponization risk is real. A fix is available for tensorflow — see the affected versions and patch details below.
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-8w5g-3wcv-9g2j.
EPSS Exploitation Probability
Probability of exploitation in the next 30 days, from FIRST.org EPSS.
How urgent is this, really
Where this sits among everything scored
Real-World Exposure
tensorflow🐍tensorflow-cpu🐍tensorflow-gpu🐍tensorflow🐍tensorflow-cpu🐍tensorflow-gpu🐍tensorflow🐍tensorflow-cpu+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
When the BaseCandidateSamplerOp function receives a value in true_classes larger than range_max, a heap oob vuln occurs.
tf.raw_ops.ThreadUnsafeUnigramCandidateSampler(
true_classes=[[0x100000,1]],
num_true = 2,
num_sampled = 2,
unique = False,
range_max = 2,
seed = 2,
seed2 = 2)
Patches
We have patched the issue in GitHub commit b389f5c944cadfdfe599b3f1e4026e036f30d2d4.
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 Yu Tian of Qihoo 360 AIVul Team.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | tensorflow | ≥ 2.10.0&&< 2.10.1 | 2.10.1pip install --upgrade 'tensorflow==2.10.1' |
| 🐍PyPI | tensorflow-cpu | ≥ 2.10.0&&< 2.10.1 | 2.10.1pip install --upgrade 'tensorflow-cpu==2.10.1' |
| 🐍PyPI | tensorflow-gpu | ≥ 2.10.0&&< 2.10.1 | 2.10.1pip install --upgrade 'tensorflow-gpu==2.10.1' |
| 🐍PyPI | tensorflow | all versions | 2.8.4pip install --upgrade 'tensorflow==2.8.4' |
| 🐍PyPI | tensorflow-cpu | all versions | 2.8.4pip install --upgrade 'tensorflow-cpu==2.8.4' |
| 🐍PyPI | tensorflow-gpu | all versions | 2.8.4pip install --upgrade 'tensorflow-gpu==2.8.4' |
Affected Products
tensorflowgoogleResearch 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 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 2.10.1 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-8w5g-3wcv-9g2j 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-8w5g-3wcv-9g2j in your dependencies?
Find it across PyPI, including transitive dependencies.