---
id: CVE-2021-29532
aliases:
  - GHSA-j47f-4232-hvv8
  - BIT-tensorflow-2021-29532
  - PYSEC-2021-169
  - PYSEC-2021-460
  - PYSEC-2021-658
title: Heap out of bounds read in `RaggedCross`
summary: Heap out of bounds read in `RaggedCross`
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-j47f-4232-hvv8'
references:
  - url: >-
      https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j47f-4232-hvv8
  - url: 'https://nvd.nist.gov/vuln/detail/CVE-2021-29532'
  - url: >-
      https://github.com/tensorflow/tensorflow/commit/44b7f486c0143f68b56c34e2d01e146ee445134a
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-460.yaml
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-658.yaml
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-169.yaml
  - url: 'https://github.com/tensorflow/tensorflow'
tags:
  - osv
  - pip
epss: 0.00198
epssPercentile: 0.0855
ingestedAt: '2026-07-08T18:25:50.567Z'
---

## Overview

### Impact
An attacker can force accesses outside the bounds of heap allocated arrays by passing in invalid tensor values to `tf.raw_ops.RaggedCross`:

```python
import tensorflow as tf

ragged_values = []
ragged_row_splits = [] 
sparse_indices = []
sparse_values = []
sparse_shape = []

dense_inputs_elem = tf.constant([], shape=[92, 0], dtype=tf.int64)
dense_inputs = [dense_inputs_elem]

input_order = "R"
hashed_output = False
num_buckets = 0
hash_key = 0 

tf.raw_ops.RaggedCross(ragged_values=ragged_values,
    ragged_row_splits=ragged_row_splits,
    sparse_indices=sparse_indices,
    sparse_values=sparse_values,
    sparse_shape=sparse_shape,
    dense_inputs=dense_inputs,
    input_order=input_order,
    hashed_output=hashed_output,
    num_buckets=num_buckets,
    hash_key=hash_key,
    out_values_type=tf.int64,
    out_row_splits_type=tf.int64)
```

This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/efea03b38fb8d3b81762237dc85e579cc5fc6e87/tensorflow/core/kernels/ragged_cross_op.cc#L456-L487) lacks validation for the user supplied arguments:

```cc
int next_ragged = 0;
int next_sparse = 0;
int next_dense = 0;
for (char c : input_order_) {
  if (c == 'R') {
    TF_RETURN_IF_ERROR(BuildRaggedFeatureReader(
        ragged_values_list[next_ragged], ragged_splits_list[next_ragged],
        features));
    next_ragged++;
  } else if (c == 'S') {
    TF_RETURN_IF_ERROR(BuildSparseFeatureReader(
        sparse_indices_list[next_sparse], sparse_values_list[next_sparse],
        batch_size, features));
    next_sparse++;
  } else if (c == 'D') {
    TF_RETURN_IF_ERROR(
        BuildDenseFeatureReader(dense_list[next_dense++], features));
  }
  ...
}
```

Each of the above branches call a helper function after accessing array elements via a `*_list[next_*]` pattern, followed by incrementing the `next_*` index. However, as there is no validation that the `next_*` values are in the valid range for the corresponding `*_list` arrays, this results in heap OOB reads.

### Patches
We have patched the issue in GitHub commit [44b7f486c0143f68b56c34e2d01e146ee445134a](https://github.com/tensorflow/tensorflow/commit/44b7f486c0143f68b56c34e2d01e146ee445134a).

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 Ying Wang and Yakun Zhang 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`