---
id: CVE-2021-29514
aliases:
  - GHSA-8h46-5m9h-7553
  - BIT-tensorflow-2021-29514
  - PYSEC-2021-151
  - PYSEC-2021-442
  - PYSEC-2021-640
title: Heap out of bounds write in `RaggedBinCount`
summary: Heap out of bounds write in `RaggedBinCount`
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 >= 2.3.0, < 2.3.3'
  - 'tensorflow >= 2.4.0, < 2.4.2'
  - 'tensorflow-cpu >= 2.3.0, < 2.3.3'
  - 'tensorflow-cpu >= 2.4.0, < 2.4.2'
  - 'tensorflow-gpu >= 2.3.0, < 2.3.3'
  - 'tensorflow-gpu >= 2.4.0, < 2.4.2'
patched:
  - tensorflow 2.3.3
  - tensorflow 2.4.2
  - tensorflow-cpu 2.3.3
  - tensorflow-cpu 2.4.2
  - 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-8h46-5m9h-7553'
references:
  - url: >-
      https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8h46-5m9h-7553
  - url: 'https://nvd.nist.gov/vuln/detail/CVE-2021-29514'
  - url: >-
      https://github.com/tensorflow/tensorflow/commit/eebb96c2830d48597d055d247c0e9aebaea94cd5
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-442.yaml
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-640.yaml
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-151.yaml
  - url: 'https://github.com/tensorflow/tensorflow'
tags:
  - osv
  - pip
epss: 0.00211
epssPercentile: 0.10136
ingestedAt: '2026-07-08T18:25:47.501Z'
---

## Overview

### Impact
If the `splits` argument of `RaggedBincount` does not specify a valid [`SparseTensor`](https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor), then an attacker can trigger a heap buffer overflow:

```python
import tensorflow as tf
tf.raw_ops.RaggedBincount(splits=[7,8], values= [5, 16, 51, 76, 29, 27, 54, 95],\
                          size= 59, weights= [0, 0, 0, 0, 0, 0, 0, 0],\
                          binary_output=False)
```

This will cause a read from outside the bounds of the `splits` tensor buffer in the [implementation of the `RaggedBincount` op](https://github.com/tensorflow/tensorflow/blob/8b677d79167799f71c42fd3fa074476e0295413a/tensorflow/core/kernels/bincount_op.cc#L430-L446):
    
```cc 
    for (int idx = 0; idx < num_values; ++idx) {
      while (idx >= splits(batch_idx)) {
        batch_idx++;
      }
      ...
      if (bin < size) {
        if (binary_output_) {
          out(batch_idx - 1, bin) = T(1);
        } else {
          T value = (weights_size > 0) ? weights(idx) : T(1);
          out(batch_idx - 1, bin) += value;
        }
      } 
    }
```

Before the `for` loop, `batch_idx` is set to 0. The attacker sets `splits(0)` to be 7, hence the `while` loop does not execute and `batch_idx` remains 0. This then results in writing to `out(-1, bin)`, which is before the heap allocated buffer for the output tensor.

### Patches
We have patched the issue in GitHub commit [eebb96c2830d48597d055d247c0e9aebaea94cd5](https://github.com/tensorflow/tensorflow/commit/eebb96c2830d48597d055d247c0e9aebaea94cd5).

The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3, as these are also affected.

### 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 >= 2.3.0, < 2.3.3`
- `tensorflow >= 2.4.0, < 2.4.2`
- `tensorflow-cpu >= 2.3.0, < 2.3.3`
- `tensorflow-cpu >= 2.4.0, < 2.4.2`
- `tensorflow-gpu >= 2.3.0, < 2.3.3`
- `tensorflow-gpu >= 2.4.0, < 2.4.2`

## Remediation

Upgrade to a patched release:

- `tensorflow 2.3.3`
- `tensorflow 2.4.2`
- `tensorflow-cpu 2.3.3`
- `tensorflow-cpu 2.4.2`
- `tensorflow-gpu 2.3.3`
- `tensorflow-gpu 2.4.2`