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LOW severity

GHSA-xm2v-8rrw-w9pm

LOWFix: tensorflow/tensorflow@2be2cdf

GHSA-xm2v-8rrw-w9pm is a low-severity (CVSS 2.5) CWE-369 vulnerability in tensorflow. 1 public exploit reference exists, so weaponization risk is real. O3 Security confirms whether GHSA-xm2v-8rrw-w9pm is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.

Division by 0 in `Conv2DBackpropInput`

Also known asBIT-tensorflow-2021-29525CVE-2021-29525PYSEC-2021-162PYSEC-2021-453PYSEC-2021-651
Published
May 21, 2021
Updated
Mar 13, 2026
Affected
12 pkgs
Patched
12 / 12
Exploits
1 known
Exploitation data as of Mar 13, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

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 trigger a division by 0 in tf.raw_ops.Conv2DBackpropInput:

import tensorflow as tf

input_tensor = tf.constant([52, 1, 1, 5], shape=[4], dtype=tf.int32)
filter_tensor = tf.constant([], shape=[0, 1, 5, 0], dtype=tf.float32)
out_backprop = tf.constant([], shape=[52, 1, 1, 0], dtype=tf.float32)

tf.raw_ops.Conv2DBackpropInput(input_sizes=input_tensor, filter=filter_tensor,
                               out_backprop=out_backprop, strides=[1, 1, 1, 1],
                               use_cudnn_on_gpu=True, padding='SAME',
                               explicit_paddings=[], data_format='NHWC',
                               dilations=[1, 1, 1, 1])

This is because the implementation does a division by a quantity that is controlled by the caller:

  const size_t size_A = output_image_size * dims.out_depth; 
  const size_t size_B = filter_total_size * dims.out_depth;
  const size_t size_C = output_image_size * filter_total_size;
  const size_t work_unit_size = size_A + size_B + size_C;
  ...
  const size_t shard_size =
      use_parallel_contraction ? 1 :
      (target_working_set_size + work_unit_size - 1) / work_unit_size;

Patches

We have patched the issue in GitHub commit 2be2cdf3a123e231b16f766aa0e27d56b4606535.

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 versions2.1.4
🐍PyPItensorflow2.2.0&&< 2.2.32.2.3
🐍PyPItensorflow2.3.0&&< 2.3.32.3.3
🐍PyPItensorflow2.4.0&&< 2.4.22.4.2
🐍PyPItensorflow-cpuall versions2.1.4
🐍PyPItensorflow-cpu2.2.0&&< 2.2.32.2.3
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. 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 2.1.4 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-xm2v-8rrw-w9pm 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-xm2v-8rrw-w9pm 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-xm2v-8rrw-w9pm. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.

Frequently Asked Questions

### Impact An attacker can trigger a division by 0 in `tf.raw_ops.Conv2DBackpropInput`: ```python import tensorflow as tf input_tensor = tf.constant([52, 1, 1, 5], shape=[4], dtype=tf.int32) filter_tensor = tf.constant([], shape=[0, 1, 5, 0], dtype=tf.float32) out_backprop = tf.constant([], shape=[52, 1, 1, 0], dtype=tf.float32) tf.raw_ops.Conv2DBackpropInput(input_sizes=input_tensor, filter=filter_tensor, out_backprop=out_backprop, strides=[1, 1, 1, 1], use_cudnn_on_gpu=True, padding='SAME', explic
O3 Security · Impact-Aware SCA

Is GHSA-xm2v-8rrw-w9pm in your dependencies?

O3 detects GHSA-xm2v-8rrw-w9pm across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.

GHSA-xm2v-8rrw-w9pm: Division by 0 in… | O3 Security