---
id: CVE-2022-23593
aliases:
  - GHSA-gwcx-jrx4-92w2
  - BIT-tensorflow-2022-23593
  - PYSEC-2022-102
  - PYSEC-2022-157
  - PYSEC-2026-3193
title: Segfault in `simplifyBroadcast` in Tensorflow
summary: Segfault in `simplifyBroadcast` in Tensorflow
severity: medium
cvss: 5.9
cvssVector: 'CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H'
vendor: tensorflow
product: tensorflow
ecosystem: pip
affected:
  - 'tensorflow >= 2.8.0-rc0, < 2.8.0'
  - 'tensorflow-cpu >= 2.8.0-rc0, < 2.8.0'
  - 'tensorflow-gpu >= 2.8.0-rc0, < 2.8.0'
patched:
  - tensorflow 2.8.0
  - tensorflow-cpu 2.8.0
  - tensorflow-gpu 2.8.0
published: '2022-02-09'
updated: '2026-07-13'
source: OSV
sourceUrl: 'https://osv.dev/vulnerability/GHSA-gwcx-jrx4-92w2'
references:
  - url: >-
      https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gwcx-jrx4-92w2
  - url: 'https://nvd.nist.gov/vuln/detail/CVE-2022-23593'
  - url: >-
      https://github.com/tensorflow/tensorflow/commit/35f0fabb4c178253a964d7aabdbb15c6a398b69a
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2022-102.yaml
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2022-157.yaml
  - url: 'https://github.com/tensorflow/tensorflow'
  - url: >-
      https://github.com/tensorflow/tensorflow/blob/274df9b02330b790aa8de1cee164b70f72b9b244/tensorflow/compiler/mlir/tfrt/jit/transforms/tf_cpurt_symbolic_shape_optimization.cc#L149-L205
tags:
  - osv
  - pip
epss: 0.0087
epssPercentile: 0.57323
ingestedAt: '2026-07-13T18:57:58.633Z'
---

## Overview

### Impact
The [`simplifyBroadcast` function in the MLIR-TFRT infrastructure in TensorFlow](https://github.com/tensorflow/tensorflow/blob/274df9b02330b790aa8de1cee164b70f72b9b244/tensorflow/compiler/mlir/tfrt/jit/transforms/tf_cpurt_symbolic_shape_optimization.cc#L149-L205) is vulnerable to a segfault (hence, denial of service), if called with scalar shapes.

```cc 
  size_t maxRank = 0;
  for (auto shape : llvm::enumerate(shapes)) {
    auto found_shape = analysis.dimensionsForShapeTensor(shape.value());
    if (!found_shape) return {};
    shapes_found.push_back(*found_shape);
    maxRank = std::max(maxRank, found_shape->size());
  }   

  SmallVector<const ShapeComponentAnalysis::SymbolicDimension*>
      joined_dimensions(maxRank);
```

If all shapes are scalar, then `maxRank` is 0, so we build an empty `SmallVector`.

### Patches
We have patched the issue in GitHub commit [35f0fabb4c178253a964d7aabdbb15c6a398b69a](https://github.com/tensorflow/tensorflow/commit/35f0fabb4c178253a964d7aabdbb15c6a398b69a).

The fix will be included in TensorFlow 2.8.0. This is the only affected version.

### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.

## Affected packages

- `tensorflow >= 2.8.0-rc0, < 2.8.0`
- `tensorflow-cpu >= 2.8.0-rc0, < 2.8.0`
- `tensorflow-gpu >= 2.8.0-rc0, < 2.8.0`

## Remediation

Upgrade to a patched release:

- `tensorflow 2.8.0`
- `tensorflow-cpu 2.8.0`
- `tensorflow-gpu 2.8.0`