GHSA-5hx2-qx8j-qjqm
MEDIUMOverflow/crash in `tf.image.resize` when size is large
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🐍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 tf.image.resize is called with a large input argument then the TensorFlow process will crash due to a CHECK-failure caused by an overflow.
import tensorflow as tf
import numpy as np
tf.keras.layers.UpSampling2D(
size=1610637938,
data_format='channels_first',
interpolation='bilinear')(np.ones((5,1,1,1)))
The number of elements in the output tensor is too much for the int64_t type and the overflow is detected via a CHECK statement. This aborts the process.
Patches
We have patched the issue in GitHub commit e5272d4204ff5b46136a1ef1204fc00597e21837 (merging #51497).
The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, 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 externally via a GitHub issue.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | tensorflow | ≥ 2.6.0&&< 2.6.1 | 2.6.1 |
| 🐍PyPI | tensorflow | ≥ 2.5.0&&< 2.5.2 | 2.5.2 |
| 🐍PyPI | tensorflow | all versions | 2.4.4 |
| 🐍PyPI | tensorflow-cpu | ≥ 2.6.0&&< 2.6.1 | 2.6.1 |
| 🐍PyPI | tensorflow-cpu | ≥ 2.5.0&&< 2.5.2 | 2.5.2 |
| 🐍PyPI | tensorflow-cpu | all versions | 2.4.4 |
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-5hx2-qx8j-qjqm 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-5hx2-qx8j-qjqm 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-5hx2-qx8j-qjqm. 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-5hx2-qx8j-qjqm in your dependencies?
O3 detects GHSA-5hx2-qx8j-qjqm across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.