---
id: CVE-2021-29601
aliases:
  - GHSA-9c84-4hx6-xmm4
  - BIT-tensorflow-2021-29601
  - PYSEC-2021-238
  - PYSEC-2021-529
  - PYSEC-2021-727
title: Integer overflow in TFLite concatentation
summary: Integer overflow in TFLite concatentation
severity: medium
cvss: 6.3
cvssVector: 'CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:H/A:H'
vendor: tensorflow
product: tensorflow
ecosystem: pip
affected:
  - tensorflow < 2.1.4
  - 'tensorflow >= 2.2.0, < 2.2.3'
  - 'tensorflow >= 2.3.0, < 2.3.3'
  - 'tensorflow >= 2.4.0, < 2.4.2'
  - tensorflow-cpu < 2.1.4
  - 'tensorflow-cpu >= 2.2.0, < 2.2.3'
  - 'tensorflow-cpu >= 2.3.0, < 2.3.3'
  - 'tensorflow-cpu >= 2.4.0, < 2.4.2'
  - tensorflow-gpu < 2.1.4
  - 'tensorflow-gpu >= 2.2.0, < 2.2.3'
  - 'tensorflow-gpu >= 2.3.0, < 2.3.3'
  - 'tensorflow-gpu >= 2.4.0, < 2.4.2'
patched:
  - tensorflow 2.1.4
  - tensorflow 2.2.3
  - tensorflow 2.3.3
  - tensorflow 2.4.2
  - tensorflow-cpu 2.1.4
  - tensorflow-cpu 2.2.3
  - tensorflow-cpu 2.3.3
  - tensorflow-cpu 2.4.2
  - tensorflow-gpu 2.1.4
  - tensorflow-gpu 2.2.3
  - tensorflow-gpu 2.3.3
  - tensorflow-gpu 2.4.2
published: '2021-05-21'
updated: '2026-07-08'
source: OSV
sourceUrl: 'https://osv.dev/vulnerability/GHSA-9c84-4hx6-xmm4'
references:
  - url: >-
      https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9c84-4hx6-xmm4
  - url: 'https://nvd.nist.gov/vuln/detail/CVE-2021-29601'
  - url: >-
      https://github.com/tensorflow/tensorflow/commit/4253f96a58486ffe84b61c0415bb234a4632ee73
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-529.yaml
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-727.yaml
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-238.yaml
  - url: 'https://github.com/tensorflow/tensorflow'
  - url: >-
      https://github.com/tensorflow/tensorflow/blob/7b7352a724b690b11bfaae2cd54bc3907daf6285/tensorflow/lite/kernels/concatenation.cc#L70-L76
tags:
  - osv
  - pip
epss: 0.00192
epssPercentile: 0.09116
ingestedAt: '2026-07-08T18:25:47.817Z'
---

## Overview

### Impact
The TFLite implementation of concatenation is [vulnerable to an integer overflow issue](https://github.com/tensorflow/tensorflow/blob/7b7352a724b690b11bfaae2cd54bc3907daf6285/tensorflow/lite/kernels/concatenation.cc#L70-L76):

```cc
for (int d = 0; d < t0->dims->size; ++d) {
  if (d == axis) { 
    sum_axis += t->dims->data[axis]; 
  } else {
    TF_LITE_ENSURE_EQ(context, t->dims->data[d], t0->dims->data[d]);
  }
}
```

An attacker can craft a model such that the dimensions of one of the concatenation input overflow the values of `int`. TFLite uses `int` to represent tensor dimensions, whereas TF uses `int64`. Hence, valid TF models can trigger an integer overflow when converted to TFLite format.

### Patches
We have patched the issue in GitHub commit [4253f96a58486ffe84b61c0415bb234a4632ee73](https://github.com/tensorflow/tensorflow/commit/4253f96a58486ffe84b61c0415bb234a4632ee73).

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](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 < 2.1.4`
- `tensorflow >= 2.2.0, < 2.2.3`
- `tensorflow >= 2.3.0, < 2.3.3`
- `tensorflow >= 2.4.0, < 2.4.2`
- `tensorflow-cpu < 2.1.4`
- `tensorflow-cpu >= 2.2.0, < 2.2.3`
- `tensorflow-cpu >= 2.3.0, < 2.3.3`
- `tensorflow-cpu >= 2.4.0, < 2.4.2`
- `tensorflow-gpu < 2.1.4`
- `tensorflow-gpu >= 2.2.0, < 2.2.3`
- `tensorflow-gpu >= 2.3.0, < 2.3.3`
- `tensorflow-gpu >= 2.4.0, < 2.4.2`

## Remediation

Upgrade to a patched release:

- `tensorflow 2.1.4`
- `tensorflow 2.2.3`
- `tensorflow 2.3.3`
- `tensorflow 2.4.2`
- `tensorflow-cpu 2.1.4`
- `tensorflow-cpu 2.2.3`
- `tensorflow-cpu 2.3.3`
- `tensorflow-cpu 2.4.2`
- `tensorflow-gpu 2.1.4`
- `tensorflow-gpu 2.2.3`
- `tensorflow-gpu 2.3.3`
- `tensorflow-gpu 2.4.2`