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MEDIUM severity

CVE-2022-29201 — tensorflow

MEDIUMFix: tensorflow/tensorflow@0f0b080

CVE-2022-29201 is a medium-severity (CVSS 5.5) Improper Input Validation vulnerability in tensorflow. 1 public exploit or proof-of-concept reference is indexed, so weaponization risk is real. A fix is available for tensorflow — see the affected versions and patch details below.

Missing validation in `QuantizedConv2D` results in undefined behavior in TensorFlow

Also known asBIT-tensorflow-2022-29201GHSA-pqhm-4wvf-2jg8PYSEC-2026-1026PYSEC-2026-3224PYSEC-2026-3358
Published
Updated
Affected
9 pkgs
Patched
9 / 9
Exploits
1 known
Exploitation data as of Oct 26, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

Exploitation Status

Proof-of-concept exploit code exists

  • CISA’s SSVC triage found public proof-of-concept exploit code for this CVE, though no confirmed active exploitation.

Exploitation and automatability from CISA’s SSVC triage for CVE-2022-29201.

EPSS Exploitation Probability

via FIRST.org ↗
0.3%probability of exploitation in next 30 days
Lower Risk+0.01%
Lower risk than most CVEs27th percentile — riskier than 27% of all scored CVEsHighest risk
0.00%0.28%0.56%0.84%0.0%0.3%Apr 26Aug 26Oct 26

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

How urgent is this, really

Where this sits among everything scored

Real-World Exposure

9 pkgs affected
🐍tensorflow🐍tensorflow🐍tensorflow🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-gpu🐍tensorflow-gpu+1 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

The implementation of tf.raw_ops.QuantizedConv2D does not fully validate the input arguments:

import tensorflow as tf

input = tf.constant(1, shape=[1, 2, 3, 3], dtype=tf.quint8)
filter = tf.constant(1, shape=[1, 2, 3, 3], dtype=tf.quint8)

# bad args
min_input = tf.constant([], shape=[0], dtype=tf.float32)
max_input = tf.constant(0, shape=[], dtype=tf.float32)
min_filter = tf.constant(0, shape=[], dtype=tf.float32)
max_filter = tf.constant(0, shape=[], dtype=tf.float32)

tf.raw_ops.QuantizedConv2D(
  input=input,
  filter=filter,
  min_input=min_input,
  max_input=max_input,
  min_filter=min_filter,
  max_filter=max_filter, 
  strides=[1, 1, 1, 1],
  padding="SAME")

In this case, references get bound to nullptr for each argument that is empty (in the example, all arguments in the bad args section).

Patches

We have patched the issue in GitHub commit 0f0b080ecde4d3dfec158d6f60da34d5e31693c4.

The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, 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 Neophytos Christou from Secure Systems Lab at Brown University.

Affected Packages

9 total 9 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPItensorflowall versions2.6.4pip install --upgrade 'tensorflow==2.6.4'
🐍PyPItensorflow≥ 2.7.0&&< 2.7.22.7.2pip install --upgrade 'tensorflow==2.7.2'
🐍PyPItensorflow≥ 2.8.0&&< 2.8.12.8.1pip install --upgrade 'tensorflow==2.8.1'
🐍PyPItensorflow-cpuall versions2.6.4pip install --upgrade 'tensorflow-cpu==2.6.4'
🐍PyPItensorflow-cpu≥ 2.7.0&&< 2.7.22.7.2pip install --upgrade 'tensorflow-cpu==2.7.2'
🐍PyPItensorflow-cpu≥ 2.8.0&&< 2.8.12.8.1pip install --upgrade 'tensorflow-cpu==2.8.1'

Affected Products

1 product · 9 configurations
Application
tensorflowgoogle
≥ 2.7.0 && < 2.7.2
3 versions
2.7.02.8.02.9.0
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.

TensorFlow is an open source platform for machine learning. Prior to ver…

github.comNVDJun 2022

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 2.6.4 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2022-29201 is resolved across your whole dependency graph.

  3. 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.

Frequently Asked Questions

### Impact The implementation of [`tf.raw_ops.QuantizedConv2D`](https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/quantized_conv_ops.cc) does not fully validate the input arguments: ```python import tensorflow as tf input = tf.constant(1, shape=[1, 2, 3, 3], dtype=tf.quint8) filter = tf.constant(1, shape=[1, 2, 3, 3], dtype=tf.quint8) # bad args min_input = tf.constant([], shape=[0], dtype=tf.float32) max_input = tf.constant(0, shape=[], dtype=tf.float32) min_filter = tf.constant(0, shape=[], dtype=tf.float32) max_filter = tf.cons
O3 Security · Impact-Aware SCA

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CVE-2022-29201: tensorflow — Fixed in 2.6.4