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. A fix is available for tensorflow — see the affected versions and patch details below.
EPSS Exploitation Probability
Probability of exploitation in the next 30 days, from FIRST.org EPSS.
How urgent is this, really
GHSA-36vm-xw34-x4pj by exploitation likelihood (EPSS) against impact (CVSS). Outside the shaded patch-first corner.
Where this sits among everything scored
Of 385,987 CVEs with a current EPSS score, this one falls in the < 10% band (highlighted). Counts from FIRST.org, log-scaled.
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 | 2.1.4pip install --upgrade 'tensorflow==2.1.4' |
| 🐍PyPI | tensorflow | ≥ 2.2.0&&< 2.2.3 | 2.2.3pip install --upgrade 'tensorflow==2.2.3' |
| 🐍PyPI | tensorflow | ≥ 2.3.0&&< 2.3.3 | 2.3.3pip install --upgrade 'tensorflow==2.3.3' |
| 🐍PyPI | tensorflow | ≥ 2.4.0&&< 2.4.2 | 2.4.2pip install --upgrade 'tensorflow==2.4.2' |
| 🐍PyPI | tensorflow-cpu | all versions | 2.1.4pip install --upgrade 'tensorflow-cpu==2.1.4' |
| 🐍PyPI | tensorflow-cpu | ≥ 2.2.0&&< 2.2.3 | 2.2.3pip install --upgrade 'tensorflow-cpu==2.2.3' |
Affected Products
tensorflowgoogleResearch 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, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.
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
Cap what an attacker can consume: apply request size, rate and timeout limits in front of the affected component, and run it with memory and CPU limits so exhaustion degrades one worker rather than the whole service.
Frequently Asked Questions
Is GHSA-36vm-xw34-x4pj in your dependencies?
Find it across PyPI, including transitive dependencies.