GHSA-h246-cgh4-7475
MEDIUM`CHECK` fail in `BCast` overflow
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-gpu🐍tensorflow-cpu🐍tensorflow-gpu🐍tensorflow-cpu+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 BCast::ToShape is given input larger than an int32, it will crash, despite being supposed to handle up to an int64. An example can be seen in tf.experimental.numpy.outer by passing in large input to the input b.
import tensorflow as tf
value = tf.constant(shape=[2, 1024, 1024, 1024], value=False)
tf.experimental.numpy.outer(a=6,b=value)
Patches
We have patched the issue in GitHub commit 8310bf8dd188ff780e7fc53245058215a05bdbe5.
The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, 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 Pattarakrit Rattankul.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | tensorflow | all versions | 2.8.4 |
| 🐍PyPI | tensorflow | ≥ 2.9.0&&< 2.9.3 | 2.9.3 |
| 🐍PyPI | tensorflow | ≥ 2.10.0&&< 2.10.1 | 2.10.1 |
| 🐍PyPI | tensorflow-cpu | all versions | 2.8.4 |
| 🐍PyPI | tensorflow-gpu | all versions | 2.8.4 |
| 🐍PyPI | tensorflow-cpu | ≥ 2.9.0&&< 2.9.3 | 2.9.3 |
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 2.8.4 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-h246-cgh4-7475 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-h246-cgh4-7475 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-h246-cgh4-7475. 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-h246-cgh4-7475 in your dependencies?
O3 detects GHSA-h246-cgh4-7475 across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.