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GHSA-23hm-7w47-xw72

HIGH

Out of bounds read in Tensorflow

Also known asBIT-tensorflow-2022-21726CVE-2022-21726PYSEC-2022-105PYSEC-2022-50
Published
Feb 9, 2022
Updated
Nov 13, 2024
Affected
9 pkgs
Patched
9 / 9
Exploits
1 known

EPSS Exploitation Probability

via FIRST.org ↗
0.8%probability of exploitation in next 30 days
Lower Risk52th percentile+0.51%
0.00%0.43%0.87%1.30%0.3%0.8%Dec 25Apr 26Jun 26

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

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 Dequantize does not fully validate the value of axis and can result in heap OOB accesses:

import tensorflow as tf

@tf.function
def test():
  y = tf.raw_ops.Dequantize(
    input=tf.constant([1,1],dtype=tf.qint32),
    min_range=[1.0],
    max_range=[10.0],
    mode='MIN_COMBINED',
    narrow_range=False,
    axis=2**31-1,
    dtype=tf.bfloat16)
  return y

test()

The axis argument can be -1 (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked and this results in reading past the end of the array containing the dimensions of the input tensor:

  if (axis_ > -1) {
    num_slices = input.dim_size(axis_);
  }
  // ...
  int64_t pre_dim = 1, post_dim = 1;
  for (int i = 0; i < axis_; ++i) {
    pre_dim *= float_output.dim_size(i);
  }
  for (int i = axis_ + 1; i < float_output.dims(); ++i) {
    post_dim *= float_output.dim_size(i);
  }

Patches

We have patched the issue in GitHub commit 23968a8bf65b009120c43b5ebcceaf52dbc9e943.

The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 Yu Tian of Qihoo 360 AIVul Team.

Affected Packages

9 total 9 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPItensorflowall versions2.5.3
🐍PyPItensorflow≥ 2.6.0&&< 2.6.32.6.3
🐍PyPItensorflow≥ 2.7.0&&< 2.7.12.7.1
🐍PyPItensorflow-cpuall versions2.5.3
🐍PyPItensorflow-cpu≥ 2.6.0&&< 2.6.32.6.3
🐍PyPItensorflow-cpu≥ 2.7.0&&< 2.7.12.7.1
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 Machine Learning Framework. The implementat…

github.comNVDFeb 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. 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.

  2. Fix

    Update tensorflow to 2.5.3 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-23hm-7w47-xw72 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.

  4. How O3 protects you

    O3 pinpoints whether GHSA-23hm-7w47-xw72 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-23hm-7w47-xw72. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.

Frequently Asked Questions

### Impact The [implementation of `Dequantize`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/dequantize_op.cc#L92-L153) does not fully validate the value of `axis` and can result in heap OOB accesses: ```python import tensorflow as tf @tf.function def test(): y = tf.raw_ops.Dequantize( input=tf.constant([1,1],dtype=tf.qint32), min_range=[1.0], max_range=[10.0], mode='MIN_COMBINED', narrow_range=False, axis=2**31-1, dtype=tf.bfloat16) return y test() ``` The `axis` argument can be `-1` (the de
O3 Security · Impact-Aware SCA

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