---
id: CVE-2020-15210
aliases:
  - GHSA-x9j7-x98r-r4w2
  - BIT-tensorflow-2020-15210
  - PYSEC-2020-133
  - PYSEC-2020-290
  - PYSEC-2020-325
title: Segmentation fault in tensorflow-lite
summary: Segmentation fault in tensorflow-lite
severity: medium
cvss: 6.5
cvssVector: 'CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:L/A:H'
vendor: tensorflow
product: tensorflow
ecosystem: pip
affected:
  - tensorflow < 1.15.4
  - 'tensorflow >= 2.0.0, < 2.0.3'
  - 'tensorflow >= 2.1.0, < 2.1.2'
  - 'tensorflow >= 2.2.0, < 2.2.1'
  - 'tensorflow >= 2.3.0, < 2.3.1'
  - tensorflow-cpu < 1.15.4
  - 'tensorflow-cpu >= 2.0.0, < 2.0.3'
  - 'tensorflow-cpu >= 2.1.0, < 2.1.2'
  - 'tensorflow-cpu >= 2.2.0, < 2.2.1'
  - tensorflow-gpu < 1.15.4
  - 'tensorflow-gpu >= 2.0.0, < 2.0.3'
  - 'tensorflow-gpu >= 2.1.0, < 2.1.2'
  - 'tensorflow-gpu >= 2.2.0, < 2.2.1'
  - 'tensorflow-cpu >= 2.3.0, < 2.3.1'
  - 'tensorflow-gpu >= 2.3.0, < 2.3.1'
patched:
  - tensorflow 1.15.4
  - tensorflow 2.0.3
  - tensorflow 2.1.2
  - tensorflow 2.2.1
  - tensorflow 2.3.1
  - tensorflow-cpu 1.15.4
  - tensorflow-cpu 2.0.3
  - tensorflow-cpu 2.1.2
  - tensorflow-cpu 2.2.1
  - tensorflow-gpu 1.15.4
  - tensorflow-gpu 2.0.3
  - tensorflow-gpu 2.1.2
  - tensorflow-gpu 2.2.1
  - tensorflow-cpu 2.3.1
  - tensorflow-gpu 2.3.1
published: '2020-09-25'
updated: '2026-07-08'
source: OSV
sourceUrl: 'https://osv.dev/vulnerability/GHSA-x9j7-x98r-r4w2'
references:
  - url: >-
      https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x9j7-x98r-r4w2
  - url: 'https://nvd.nist.gov/vuln/detail/CVE-2020-15210'
  - url: >-
      https://github.com/tensorflow/tensorflow/commit/094329d0dcb8290bed2b1ee420934971f422c86d
  - url: >-
      https://github.com/tensorflow/tensorflow/commit/1c8709b437fec10875b0cf271889afec9bbf582e
  - url: >-
      https://github.com/tensorflow/tensorflow/commit/8c2092e9f9ef78b3f9060f8bf5ce7a49d1ccdc8f
  - url: >-
      https://github.com/tensorflow/tensorflow/commit/d58c96946b2880991d63d1dacacb32f0a4dfa453
  - url: >-
      https://github.com/tensorflow/tensorflow/commit/f4159ccef23d11eb58ee4263beaaeac1be3343c7
  - url: >-
      https://github.com/tensorflow/tensorflow/commit/f50a14b00560a383865c2273e4a9094add3888d5
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-290.yaml
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-325.yaml
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-133.yaml
  - url: 'https://github.com/tensorflow/tensorflow'
  - url: 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'
  - url: 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html'
tags:
  - osv
  - pip
epss: 0.00739
epssPercentile: 0.52658
ingestedAt: '2026-07-08T18:25:54.161Z'
---

## Overview

### Impact
If a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption.

### Patches
We have patched the issue in d58c96946b and will release patch releases for all versions between 1.15 and 2.3.

We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

### Workarounds
A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that no operator reuses tensors as both inputs and outputs. Care should be taken to check all types of inputs (i.e., constant or variable tensors as well as optional tensors).

### 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.

### Attribution
This vulnerability has been discovered from a variant analysis of [GHSA-cvpc-8phh-8f45](https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45).

## Affected packages

- `tensorflow < 1.15.4`
- `tensorflow >= 2.0.0, < 2.0.3`
- `tensorflow >= 2.1.0, < 2.1.2`
- `tensorflow >= 2.2.0, < 2.2.1`
- `tensorflow >= 2.3.0, < 2.3.1`
- `tensorflow-cpu < 1.15.4`
- `tensorflow-cpu >= 2.0.0, < 2.0.3`
- `tensorflow-cpu >= 2.1.0, < 2.1.2`
- `tensorflow-cpu >= 2.2.0, < 2.2.1`
- `tensorflow-gpu < 1.15.4`
- `tensorflow-gpu >= 2.0.0, < 2.0.3`
- `tensorflow-gpu >= 2.1.0, < 2.1.2`
- `tensorflow-gpu >= 2.2.0, < 2.2.1`
- `tensorflow-cpu >= 2.3.0, < 2.3.1`
- `tensorflow-gpu >= 2.3.0, < 2.3.1`

## Remediation

Upgrade to a patched release:

- `tensorflow 1.15.4`
- `tensorflow 2.0.3`
- `tensorflow 2.1.2`
- `tensorflow 2.2.1`
- `tensorflow 2.3.1`
- `tensorflow-cpu 1.15.4`
- `tensorflow-cpu 2.0.3`
- `tensorflow-cpu 2.1.2`
- `tensorflow-cpu 2.2.1`
- `tensorflow-gpu 1.15.4`
- `tensorflow-gpu 2.0.3`
- `tensorflow-gpu 2.1.2`
- `tensorflow-gpu 2.2.1`
- `tensorflow-cpu 2.3.1`
- `tensorflow-gpu 2.3.1`