GHSA-mq5c-prh3-3f3h is a low-severity (CVSS 3.6) CWE-665 vulnerability in tensorflow. 1 public exploit reference exists, so weaponization risk is real. O3 Security confirms whether GHSA-mq5c-prh3-3f3h is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.
Invalid validation in `QuantizeAndDequantizeV2`
Real-World Exposure
tensorflow🐍tensorflow🐍tensorflow🐍tensorflow🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-cpu+4 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
The validation in tf.raw_ops.QuantizeAndDequantizeV2 allows invalid values for axis argument:
import tensorflow as tf
input_tensor = tf.constant([0.0], shape=[1], dtype=float)
input_min = tf.constant(-10.0)
input_max = tf.constant(-10.0)
tf.raw_ops.QuantizeAndDequantizeV2(
input=input_tensor, input_min=input_min, input_max=input_max,
signed_input=False, num_bits=1, range_given=False, round_mode='HALF_TO_EVEN',
narrow_range=False, axis=-2)
The validation uses || to mix two different conditions:
OP_REQUIRES(ctx,
(axis_ == -1 || axis_ < input.shape().dims()),
errors::InvalidArgument(...));
If axis_ < -1 the condition in OP_REQUIRES will still be true, but this value of axis_ results in heap underflow. This allows attackers to read/write to other data on the heap.
Patches
We have patched the issue in GitHub commit c5b0d5f8ac19888e46ca14b0e27562e7fbbee9a9.
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, 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 Yakun Zhang and Ying Wang of Baidu X-Team.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | tensorflow | all versions | 2.1.4 |
| 🐍PyPI | tensorflow | ≥ 2.2.0&&< 2.2.3 | 2.2.3 |
| 🐍PyPI | tensorflow | ≥ 2.3.0&&< 2.3.3 | 2.3.3 |
| 🐍PyPI | tensorflow | ≥ 2.4.0&&< 2.4.2 | 2.4.2 |
| 🐍PyPI | tensorflow-cpu | all versions | 2.1.4 |
| 🐍PyPI | tensorflow-cpu | ≥ 2.2.0&&< 2.2.3 | 2.2.3 |
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 2.1.4 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-mq5c-prh3-3f3h 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-mq5c-prh3-3f3h 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-mq5c-prh3-3f3h. 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-mq5c-prh3-3f3h in your dependencies?
O3 detects GHSA-mq5c-prh3-3f3h across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.