GHSA-v7vw-577f-vp8x is a medium-severity (CVSS 5.9) Improper Input Validation vulnerability in tensorflow. O3 Security confirms whether GHSA-v7vw-577f-vp8x is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.
TensorFlow vulnerable to segfault in `QuantizedRelu` and `QuantizedRelu6`
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
If QuantizedRelu or QuantizedRelu6 are given nonscalar inputs for min_features or max_features, it results in a segfault that can be used to trigger a denial of service attack.
import tensorflow as tf
out_type = tf.quint8
features = tf.constant(28, shape=[4,2], dtype=tf.quint8)
min_features = tf.constant([], shape=[0], dtype=tf.float32)
max_features = tf.constant(-128, shape=[1], dtype=tf.float32)
tf.raw_ops.QuantizedRelu(features=features, min_features=min_features, max_features=max_features, out_type=out_type)
tf.raw_ops.QuantizedRelu6(features=features, min_features=min_features, max_features=max_features, out_type=out_type)
Patches
We have patched the issue in GitHub commit 49b3824d83af706df0ad07e4e677d88659756d89.
The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, 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 Neophytos Christou, Secure Systems Labs, Brown University.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | tensorflow | all versions | 2.7.2 |
| 🐍PyPI | tensorflow | ≥ 2.8.0&&< 2.8.1 | 2.8.1 |
| 🐍PyPI | tensorflow | ≥ 2.9.0&&< 2.9.1 | 2.9.1 |
| 🐍PyPI | tensorflow-cpu | all versions | 2.7.2 |
| 🐍PyPI | tensorflow-cpu | ≥ 2.8.0&&< 2.8.1 | 2.8.1 |
| 🐍PyPI | tensorflow-cpu | ≥ 2.9.0&&< 2.9.1 | 2.9.1 |
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 2.7.2 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-v7vw-577f-vp8x 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-v7vw-577f-vp8x 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-v7vw-577f-vp8x. 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-v7vw-577f-vp8x in your dependencies?
O3 detects GHSA-v7vw-577f-vp8x across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.