GHSA-v82p-hv3v-p6qp is a high-severity (CVSS 7.8) Improper Input Validation vulnerability in tensorflow. O3 Security confirms whether GHSA-v82p-hv3v-p6qp is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.
Incomplete validation in MKL requantization
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
Due to incomplete validation in MKL implementation of requantization, an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays:
import tensorflow as tf
tf.raw_ops.RequantizationRangePerChannel(
input=[],
input_min=[0,0,0,0,0],
input_max=[1,1,1,1,1],
clip_value_max=1)
The implementation does not validate the dimensions of the input tensor.
A similar issue occurs in MklRequantizePerChannelOp:
import tensorflow as tf
from tensorflow.python.ops import gen_math_ops
gen_math_ops.requantize_per_channel(
input=[],
input_min=[-100,-100,-100,-100,-100],
input_max=[-100,-100,-100],
requested_output_min=[-100,-100,-100,-100,-100],
requested_output_max=[],
out_type=tf.int)
The implementation does not perform full validation for all the input arguments.
Patches
We have patched the issue in GitHub commit 9e62869465573cb2d9b5053f1fa02a81fce21d69 and in the Github commit 203214568f5bc237603dbab6e1fd389f1572f5c9.
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, 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 members of the Aivul Team from Qihoo 360.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | tensorflow | all versions | 2.3.4 |
| 🐍PyPI | tensorflow | ≥ 2.4.0&&< 2.4.3 | 2.4.3 |
| 🐍PyPI | tensorflow | ≥ 2.5.0&&< 2.5.1 | 2.5.1 |
| 🐍PyPI | tensorflow-cpu | all versions | 2.3.4 |
| 🐍PyPI | tensorflow-cpu | ≥ 2.4.0&&< 2.4.3 | 2.4.3 |
| 🐍PyPI | tensorflow-cpu | ≥ 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 2.3.4 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-v82p-hv3v-p6qp 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-v82p-hv3v-p6qp 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-v82p-hv3v-p6qp. 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-v82p-hv3v-p6qp in your dependencies?
O3 detects GHSA-v82p-hv3v-p6qp across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.