GHSA-36vm-xw34-x4pj is a low-severity (CVSS 2.5) Reachable Assertion vulnerability in tensorflow. 1 public exploit reference exists, so weaponization risk is real. O3 Security confirms whether GHSA-36vm-xw34-x4pj is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.
CHECK-fail in `tf.raw_ops.IRFFT`
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
An attacker can cause a denial of service by exploiting a CHECK-failure coming from the implementation of tf.raw_ops.IRFFT:
import tensorflow as tf
values = [-10.0] * 130
values[0] = -9.999999999999995
inputs = tf.constant(values, shape=[10, 13], dtype=tf.float32)
inputs = tf.cast(inputs, dtype=tf.complex64)
fft_length = tf.constant([0], shape=[1], dtype=tf.int32)
tf.raw_ops.IRFFT(input=inputs, fft_length=fft_length)
The above example causes Eigen code to operate on an empty matrix. This triggers on an assertion and causes program termination.
Patches
We have patched the issue in GitHub commit 1c56f53be0b722ca657cbc7df461ed676c8642a2.
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-36vm-xw34-x4pj 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-36vm-xw34-x4pj 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-36vm-xw34-x4pj. 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-36vm-xw34-x4pj in your dependencies?
O3 detects GHSA-36vm-xw34-x4pj across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.