---
id: CVE-2020-15193
aliases:
  - GHSA-rjjg-hgv6-h69v
  - BIT-tensorflow-2020-15193
  - PYSEC-2020-116
  - PYSEC-2020-273
  - PYSEC-2020-308
title: Memory corruption in Tensorflow
summary: Memory corruption in Tensorflow
severity: high
cvss: 7.1
cvssVector: 'CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:L'
vendor: tensorflow
product: tensorflow
ecosystem: pip
affected:
  - 'tensorflow >= 2.2.0, < 2.2.1'
  - 'tensorflow >= 2.3.0, < 2.3.1'
  - 'tensorflow-cpu >= 2.2.0, < 2.2.1'
  - 'tensorflow-cpu >= 2.3.0, < 2.3.1'
  - 'tensorflow-gpu >= 2.2.0, < 2.2.1'
  - 'tensorflow-gpu >= 2.3.0, < 2.3.1'
patched:
  - tensorflow 2.2.1
  - tensorflow 2.3.1
  - tensorflow-cpu 2.2.1
  - tensorflow-cpu 2.3.1
  - tensorflow-gpu 2.2.1
  - tensorflow-gpu 2.3.1
published: '2020-09-25'
updated: '2026-07-08'
source: OSV
sourceUrl: 'https://osv.dev/vulnerability/GHSA-rjjg-hgv6-h69v'
references:
  - url: >-
      https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rjjg-hgv6-h69v
  - url: 'https://nvd.nist.gov/vuln/detail/CVE-2020-15193'
  - url: >-
      https://github.com/tensorflow/tensorflow/commit/22e07fb204386768e5bcbea563641ea11f96ceb8
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-273.yaml
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-308.yaml
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-116.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: 0.0083
epssPercentile: 0.55756
ingestedAt: '2026-07-08T18:25:52.744Z'
---

## Overview

### Impact
The implementation of `dlpack.to_dlpack` can be made to use uninitialized memory resulting in further memory corruption. This is because the pybind11 glue code assumes that the argument is a tensor:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/python/tfe_wrapper.cc#L1361

However, there is nothing stopping users from passing in a Python object instead of a tensor.
```python
In [2]: tf.experimental.dlpack.to_dlpack([2])                                                                                                                                            
==1720623==WARNING: MemorySanitizer: use-of-uninitialized-value                                                                                                                            
    #0 0x55b0ba5c410a in tensorflow::(anonymous namespace)::GetTensorFromHandle(TFE_TensorHandle*, TF_Status*) third_party/tensorflow/c/eager/dlpack.cc:46:7
    #1 0x55b0ba5c38f4 in tensorflow::TFE_HandleToDLPack(TFE_TensorHandle*, TF_Status*) third_party/tensorflow/c/eager/dlpack.cc:252:26
... 
```

The uninitialized memory address is due to a `reinterpret_cast`
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/python/eager/pywrap_tensor.cc#L848-L850

Since the `PyObject` is a Python object, not a TensorFlow Tensor, the cast to `EagerTensor` fails. 

### Patches
We have patched the issue in 22e07fb204386768e5bcbea563641ea11f96ceb8 and will release a patch release for all affected versions.

We recommend users to upgrade to TensorFlow 2.2.1 or 2.3.1.

### 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 reported by members of the Aivul Team from Qihoo 360.

## Affected packages

- `tensorflow >= 2.2.0, < 2.2.1`
- `tensorflow >= 2.3.0, < 2.3.1`
- `tensorflow-cpu >= 2.2.0, < 2.2.1`
- `tensorflow-cpu >= 2.3.0, < 2.3.1`
- `tensorflow-gpu >= 2.2.0, < 2.2.1`
- `tensorflow-gpu >= 2.3.0, < 2.3.1`

## Remediation

Upgrade to a patched release:

- `tensorflow 2.2.1`
- `tensorflow 2.3.1`
- `tensorflow-cpu 2.2.1`
- `tensorflow-cpu 2.3.1`
- `tensorflow-gpu 2.2.1`
- `tensorflow-gpu 2.3.1`