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LOW severity

GHSA-vqw6-72r7-fgw7

LOWFix: tensorflow/tensorflow@480641e

GHSA-vqw6-72r7-fgw7 is a low-severity (CVSS 2.5) Out-of-bounds Read vulnerability in tensorflow. 1 public exploit reference exists, so weaponization risk is real. O3 Security confirms whether GHSA-vqw6-72r7-fgw7 is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.

OOB read in `MatrixTriangularSolve`

Also known asBIT-tensorflow-2021-29551CVE-2021-29551PYSEC-2021-188PYSEC-2021-479PYSEC-2021-677
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

The implementation of MatrixTriangularSolve fails to terminate kernel execution if one validation condition fails:

void ValidateInputTensors(OpKernelContext* ctx, const Tensor& in0,
                            const Tensor& in1) override {
  OP_REQUIRES(
      ctx, in0.dims() >= 2,
      errors::InvalidArgument("In[0] ndims must be >= 2: ", in0.dims()));

  OP_REQUIRES(
      ctx, in1.dims() >= 2,
      errors::InvalidArgument("In[0] ndims must be >= 2: ", in1.dims()));
}
  
void Compute(OpKernelContext* ctx) override {
  const Tensor& in0 = ctx->input(0);
  const Tensor& in1 = ctx->input(1);

  ValidateInputTensors(ctx, in0, in1);

  MatMulBCast bcast(in0.shape().dim_sizes(), in1.shape().dim_sizes());
  ...
}

Since OP_REQUIRES only sets ctx->status() to a non-OK value and calls return, this allows malicious attackers to trigger an out of bounds read:

import tensorflow as tf
import numpy as np

matrix_array = np.array([])
matrix_tensor = tf.convert_to_tensor(np.reshape(matrix_array,(1,0)),dtype=tf.float32)
rhs_array = np.array([])
rhs_tensor = tf.convert_to_tensor(np.reshape(rhs_array,(0,1)),dtype=tf.float32)

tf.raw_ops.MatrixTriangularSolve(matrix=matrix_tensor,rhs=rhs_tensor,lower=False,adjoint=False)

As the two input tensors are empty, the OP_REQUIRES in ValidateInputTensors should fire and interrupt execution. However, given the implementation of OP_REQUIRES, after the in0.dims() >= 2 fails, execution moves to the initialization of the bcast object. This initialization is done with invalid data and results in heap OOB read.

Patches

We have patched the issue in GitHub commit 480641e3599775a8895254ffbc0fc45621334f68.

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 Ye Zhang 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-vqw6-72r7-fgw7 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-vqw6-72r7-fgw7 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-vqw6-72r7-fgw7. 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 [`MatrixTriangularSolve`](https://github.com/tensorflow/tensorflow/blob/8cae746d8449c7dda5298327353d68613f16e798/tensorflow/core/kernels/linalg/matrix_triangular_solve_op_impl.h#L160-L240) fails to terminate kernel execution if one validation condition fails: ```cc void ValidateInputTensors(OpKernelContext* ctx, const Tensor& in0, const Tensor& in1) override { OP_REQUIRES( ctx, in0.dims() >= 2, errors::InvalidArgument("In[0] ndims must be >= 2: ", in0.dims())); OP_REQUIRES( ctx, in1.dims() >= 2, errors::
O3 Security · Impact-Aware SCA

Is GHSA-vqw6-72r7-fgw7 in your dependencies?

O3 detects GHSA-vqw6-72r7-fgw7 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-vqw6-72r7-fgw7: OOB read in… | O3 Security