GHSA-6445-fm66-fvq2
MEDIUMInteger overflows in Tensorflow
EPSS Exploitation Probability
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.
Blast Radius
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Description
Impact
The implementation of AddManySparseToTensorsMap is vulnerable to an integer overflow which results in a CHECK-fail when building new TensorShape objects (so, an assert failure based denial of service):
import tensorflow as tf
import numpy as np
tf.raw_ops.AddManySparseToTensorsMap(
sparse_indices=[(0,0),(0,1),(0,2),(4,3),(5,0),(5,1)],
sparse_values=[1,1,1,1,1,1],
sparse_shape=[2**32,2**32],
container='',
shared_name='',
name=None)
We are missing some validation on the shapes of the input tensors as well as directly constructing a large TensorShape with user-provided dimensions. The latter is an instance of TFSA-2021-198 (CVE-2021-41197) and is easily fixed by replacing a call to TensorShape constructor with a call to BuildTensorShape static helper factory.
Patches
We have patched the issue in GitHub commits b51b82fe65ebace4475e3c54eb089c18a4403f1c and a68f68061e263a88321c104a6c911fe5598050a8.
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 Faysal Hossain Shezan from University of Virginia.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | tensorflow | all versions | 2.5.3 |
| 🐍PyPI | tensorflow | ≥ 2.6.0&&< 2.6.3 | 2.6.3 |
| 🐍PyPI | tensorflow | ≥ 2.7.0&&< 2.7.1 | 2.7.1 |
| 🐍PyPI | tensorflow-cpu | all versions | 2.5.3 |
| 🐍PyPI | tensorflow-cpu | ≥ 2.6.0&&< 2.6.3 | 2.6.3 |
| 🐍PyPI | tensorflow-cpu | ≥ 2.7.0&&< 2.7.1 | 2.7.1 |
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 dependencyDetect
Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for tensorflow. O3's reachability analysis confirms whether the vulnerable code path is actually invoked in your application, so you act on real exposure instead of every transitive match.
Fix
Update tensorflow to 2.5.3 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-6445-fm66-fvq2 is resolved across your whole dependency graph.
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.
How O3 protects you
O3 pinpoints whether GHSA-6445-fm66-fvq2 is reachable in your code and exactly where to fix it, then blocks exploitation in production at runtime until the patched version is deployed.
Tailored to GHSA-6445-fm66-fvq2. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.
Frequently Asked Questions
Is GHSA-6445-fm66-fvq2 in your dependencies?
O3 detects GHSA-6445-fm66-fvq2 across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.