GHSA-9w2p-5mgw-p94c
MEDIUMInteger overflow due to conversion to unsigned
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
tensorflow🐍tensorflow🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-gpu🐍tensorflow-gpuReal-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 implementation of tf.raw_ops.QuantizeAndDequantizeV4Grad is vulnerable to an integer overflow issue caused by converting a signed integer value to an unsigned one and then allocating memory based on this value.
import tensorflow as tf
tf.raw_ops.QuantizeAndDequantizeV4Grad(
gradients=[1.0,2.0],
input=[1.0,1.0],
input_min=[0.0],
input_max=[10.0],
axis=-100)
The implementation uses the axis value as the size argument to absl::InlinedVector constructor. But, the constructor uses an unsigned type for the argument, so the implicit conversion transforms the negative value to a large integer.
Patches
We have patched the issue in GitHub commit 96f364a1ca3009f98980021c4b32be5fdcca33a1.
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, and TensorFlow 2.4.3, 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 members of the Aivul Team from Qihoo 360.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | tensorflow | all versions | 2.4.3 |
| 🐍PyPI | tensorflow | ≥ 2.5.0&&< 2.5.1 | 2.5.1 |
| 🐍PyPI | tensorflow-cpu | all versions | 2.4.3 |
| 🐍PyPI | tensorflow-cpu | ≥ 2.5.0&&< 2.5.1 | 2.5.1 |
| 🐍PyPI | tensorflow-gpu | all versions | 2.4.3 |
| 🐍PyPI | tensorflow-gpu | ≥ 2.5.0&&< 2.5.1 | 2.5.1 |
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 2.4.3 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-9w2p-5mgw-p94c 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-9w2p-5mgw-p94c 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-9w2p-5mgw-p94c. 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-9w2p-5mgw-p94c in your dependencies?
O3 detects GHSA-9w2p-5mgw-p94c across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.