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MEDIUM severity

CVE-2021-29563 — tensorflow

MEDIUMFix: tensorflow/tensorflow@31bd502

CVE-2021-29563 is a medium-severity (CVSS 5.5) 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.

CHECK-fail in `tf.raw_ops.RFFT`

Also known asBIT-tensorflow-2021-29563GHSA-ph87-fvjr-v33wPYSEC-2021-200PYSEC-2021-491PYSEC-2021-689
Published
Updated
Affected
12 pkgs
Patched
12 / 12
Exploits
1 known
Exploitation data as of Oct 10, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

EPSS Exploitation Probability

via FIRST.org ↗
0.2%probability of exploitation in next 30 days
Lower Risk0.00%
Lower risk than most CVEs8th percentile — riskier than 8% of all scored CVEsHighest risk
0.00%0.23%0.46%0.69%0.0%0.2%Apr 26Aug 26Oct 26

Probability of exploitation in the next 30 days, from FIRST.org EPSS.

How urgent is this, really

CVE-2021-29563 by exploitation likelihood (EPSS) against impact (CVSS). Outside the shaded patch-first corner.

Where this sits among everything scored

Of 385,738 CVEs with a current EPSS score, this one falls in the < 10% band (highlighted). Counts from FIRST.org, log-scaled.

Real-World Exposure

12 pkgs affected
🐍tensorflow🐍tensorflow🐍tensorflow🐍tensorflow🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-cpu+4 more

Real-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.RFFT:

import tensorflow as tf

inputs = tf.constant([1], shape=[1], dtype=tf.float32)
fft_length = tf.constant([0], shape=[1], dtype=tf.int32)

tf.raw_ops.RFFT(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 31bd5026304677faa8a0b77602c6154171b9aec1.

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

12 total 12 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPItensorflowall versions2.1.4pip install --upgrade 'tensorflow==2.1.4'
🐍PyPItensorflow≥ 2.2.0&&< 2.2.32.2.3pip install --upgrade 'tensorflow==2.2.3'
🐍PyPItensorflow≥ 2.3.0&&< 2.3.32.3.3pip install --upgrade 'tensorflow==2.3.3'
🐍PyPItensorflow≥ 2.4.0&&< 2.4.22.4.2pip install --upgrade 'tensorflow==2.4.2'
🐍PyPItensorflow-cpuall versions2.1.4pip install --upgrade 'tensorflow-cpu==2.1.4'
🐍PyPItensorflow-cpu≥ 2.2.0&&< 2.2.32.2.3pip install --upgrade 'tensorflow-cpu==2.2.3'

Affected Products

1 product · 4 configurations
Application
tensorflowgoogle
≥ 2.4.0 && < 2.4.2
range
Exploits & PoCs
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 dependency
  1. Detect

    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.

  2. Fix

    Update tensorflow to 2.1.4 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2021-29563 is resolved across your whole dependency graph.

  3. 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

### Impact An attacker can cause a denial of service by exploiting a `CHECK`-failure coming from the implementation of `tf.raw_ops.RFFT`: ```python import tensorflow as tf inputs = tf.constant([1], shape=[1], dtype=tf.float32) fft_length = tf.constant([0], shape=[1], dtype=tf.int32) tf.raw_ops.RFFT(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 [31bd5026304677faa8a0b77602c6154171b9aec1](https://github.com/
O3 Security · Impact-Aware SCA

Is CVE-2021-29563 in your dependencies?

Find it across PyPI, including transitive dependencies.

CVE-2021-29563: tensorflow DoS — Fixed in 2.1.4