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LOW severity

GHSA-jhq9-wm9m-cf89

LOWFix: tensorflow/tensorflow@704866e

GHSA-jhq9-wm9m-cf89 is a low-severity (CVSS 2.5) Reachable Assertion vulnerability in tensorflow. 1 public exploit reference exists, so weaponization risk is real. O3 Security confirms whether GHSA-jhq9-wm9m-cf89 is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.

CHECK-failure in `UnsortedSegmentJoin`

Also known asBIT-tensorflow-2021-29552CVE-2021-29552PYSEC-2021-189PYSEC-2021-480PYSEC-2021-678
Published
May 21, 2021
Updated
Jul 8, 2026
Affected
12 pkgs
Patched
12 / 12
Exploits
1 known
Exploitation data as of Jul 8, 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 cause a denial of service by controlling the values of num_segments tensor argument for UnsortedSegmentJoin:

import tensorflow as tf

inputs = tf.constant([], dtype=tf.string)
segment_ids = tf.constant([], dtype=tf.int32)
num_segments = tf.constant([], dtype=tf.int32)
separator = ''

tf.raw_ops.UnsortedSegmentJoin(
  inputs=inputs, segment_ids=segment_ids,
  num_segments=num_segments, separator=separator)

This is because the implementation assumes that the num_segments tensor is a valid scalar:

const Tensor& num_segments_tensor = context->input(2);
auto num_segments = num_segments_tensor.scalar<NUM_SEGMENTS_TYPE>()();

Since the tensor is empty the CHECK involved in .scalar<T>()() that checks that the number of elements is exactly 1 will be invalidated and this would result in process termination.

Patches

We have patched the issue in GitHub commit 704866eabe03a9aeda044ec91a8d0c83fc1ebdbe.

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 Ying Wang and Yakun Zhang 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-jhq9-wm9m-cf89 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-jhq9-wm9m-cf89 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-jhq9-wm9m-cf89. 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 cause a denial of service by controlling the values of `num_segments` tensor argument for `UnsortedSegmentJoin`: ```python import tensorflow as tf inputs = tf.constant([], dtype=tf.string) segment_ids = tf.constant([], dtype=tf.int32) num_segments = tf.constant([], dtype=tf.int32) separator = '' tf.raw_ops.UnsortedSegmentJoin( inputs=inputs, segment_ids=segment_ids, num_segments=num_segments, separator=separator) ``` This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/a2a607db15c7cd01d754d37e5448d72a13491bdb/tensorflow/core/kern
O3 Security · Impact-Aware SCA

Is GHSA-jhq9-wm9m-cf89 in your dependencies?

O3 detects GHSA-jhq9-wm9m-cf89 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-jhq9-wm9m-cf89: CHECK-failure in… | O3 Security