GHSA-cvgx-3v3q-m36c
HIGHHeap OOB in shape inference for `QuantizeV2`
EPSS Exploitation Probability
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Blast Radius
tensorflow🐍tensorflow-cpu🐍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 shape inference code for QuantizeV2 can trigger a read outside of bounds of heap allocated array:
import tensorflow as tf
@tf.function
def test():
data=tf.raw_ops.QuantizeV2(
input=[1.0,1.0],
min_range=[1.0,10.0],
max_range=[1.0,10.0],
T=tf.qint32,
mode='MIN_COMBINED',
round_mode='HALF_TO_EVEN',
narrow_range=False,
axis=-100,
ensure_minimum_range=10)
return data
test()
This occurs whenever axis is a negative value less than -1. In this case, we are accessing data before the start of a heap buffer:
int axis = -1;
Status s = c->GetAttr("axis", &axis);
if (!s.ok() && s.code() != error::NOT_FOUND) {
return s;
}
...
if (axis != -1) {
...
TF_RETURN_IF_ERROR(
c->Merge(c->Dim(minmax, 0), c->Dim(input, axis), &depth));
}
The code allows axis to be an optional argument (s would contain an error::NOT_FOUND error code). Otherwise, it assumes that axis is a valid index into the dimensions of the input tensor. If axis is less than -1 then this results in a heap OOB read.
Patches
We have patched the issue in GitHub commit a0d64445116c43cf46a5666bd4eee28e7a82f244.
The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, as this version is the only one that is also affected.
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 | ≥ 2.6.0&&< 2.6.1 | 2.6.1 |
| 🐍PyPI | tensorflow-cpu | ≥ 2.6.0&&< 2.6.1 | 2.6.1 |
| 🐍PyPI | tensorflow-gpu | ≥ 2.6.0&&< 2.6.1 | 2.6.1 |
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 2.6.1 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-cvgx-3v3q-m36c 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-cvgx-3v3q-m36c 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-cvgx-3v3q-m36c. 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-cvgx-3v3q-m36c in your dependencies?
O3 detects GHSA-cvgx-3v3q-m36c across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.