Your RSA-2048 keys break in 2030. Find every one of them before attackers do.
🐍
🐍 PyPI
Not in CISA KEV
HIGH severity

GHSA-pfjj-m3jj-9jc9

HIGHFix: tensorflow/tensorflow@965b97e

GHSA-pfjj-m3jj-9jc9 is a high-severity (CVSS 7.6) NULL Pointer Dereference vulnerability in tensorflow. 1 public exploit reference exists, so weaponization risk is real. O3 Security confirms whether GHSA-pfjj-m3jj-9jc9 is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.

Undefined behavior in `SparseTensorSliceDataset`

Also known asBIT-tensorflow-2022-21736CVE-2022-21736PYSEC-2022-115PYSEC-2022-60PYSEC-2026-3223
Published
Feb 9, 2022
Updated
Jul 13, 2026
Affected
9 pkgs
Patched
9 / 9
Exploits
1 known
Exploitation data as of Aug 23, 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 GHSA-pfjj-m3jj-9jc9.

EPSS Exploitation Probability

via FIRST.org ↗
0.7%probability of exploitation in next 30 days
Lower Risk0.00%
Lower risk than most CVEs52th percentile — riskier than 52% of all scored CVEsHighest risk

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.

How urgent is this, really

GHSA-pfjj-m3jj-9jc9 plotted by exploitation likelihood (EPSS) against impact (CVSS). The shaded corner — EPSS 50%+ and CVSS 7.0+ — is where this CVE doesn't sit, though severity or exploitability alone can still warrant action.

Where this sits among everything scored

Of 0 CVEs with a current EPSS score, this one falls in the < 10% band (highlighted). Real counts from FIRST.org, not a sample — log-scaled since the landscape is heavily right-skewed.

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 SparseTensorSliceDataset has an undefined behavior: under certain condition it can be made to dereference a nullptr value:

import tensorflow as tf
import numpy as np

tf.raw_ops.SparseTensorSliceDataset(
  indices=[[]],
  values=[],
  dense_shape=[1,1])

The 3 input arguments represent a sparse tensor. However, there are some preconditions that these arguments must satisfy but these are not validated in the implementation.

Patches

We have patched the issue in GitHub commit 965b97e4a9650495cda5a8c210ef6684b4b9eceb.

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 Faysal Hossain Shezan from University of Virginia.

Affected Packages

9 total 9 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPItensorflowall versions2.5.3
🐍PyPItensorflow2.6.0&&< 2.6.32.6.3
🐍PyPItensorflow2.7.0&&< 2.7.12.7.1
🐍PyPItensorflow-cpuall versions2.5.3
🐍PyPItensorflow-cpu2.6.0&&< 2.6.32.6.3
🐍PyPItensorflow-cpu2.7.0&&< 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.

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-pfjj-m3jj-9jc9 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-pfjj-m3jj-9jc9 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-pfjj-m3jj-9jc9. 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 `SparseTensorSliceDataset`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/data/sparse_tensor_slice_dataset_op.cc#L227-L292) has an undefined behavior: under certain condition it can be made to dereference a `nullptr` value: ```python import tensorflow as tf import numpy as np tf.raw_ops.SparseTensorSliceDataset( indices=[[]], values=[], dense_shape=[1,1]) ``` The 3 input arguments represent a sparse tensor. However, there are some preconditions that these arguments must satisfy but thes
O3 Security · Impact-Aware SCA

Is GHSA-pfjj-m3jj-9jc9 in your dependencies?

O3 detects GHSA-pfjj-m3jj-9jc9 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-pfjj-m3jj-9jc9: Undefined behavior in… | O3 Security