---
id: CVE-2021-29614
aliases:
  - GHSA-8pmx-p244-g88h
  - BIT-tensorflow-2021-29614
  - PYSEC-2021-251
  - PYSEC-2021-542
  - PYSEC-2021-740
title: Interpreter crash from `tf.io.decode_raw`
summary: Interpreter crash from `tf.io.decode_raw`
severity: high
cvss: 7.1
cvssVector: 'CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:H'
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: '2026-09-10'
sourceUpdated: '2026-09-10T03:49:15.107186659Z'
source: OSV
sourceUrl: 'https://osv.dev/vulnerability/GHSA-8pmx-p244-g88h'
references:
  - url: >-
      https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8pmx-p244-g88h
  - url: 'https://nvd.nist.gov/vuln/detail/CVE-2021-29614'
  - url: >-
      https://github.com/tensorflow/tensorflow/commit/698e01511f62a3c185754db78ebce0eee1f0184d
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-542.yaml
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-740.yaml
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-251.yaml
  - url: 'https://github.com/tensorflow/tensorflow'
tags:
  - osv
  - pip
epss: 0.00221
epssPercentile: 0.11267
ingestedAt: '2026-09-12T03:13:01.678Z'
---

## Overview

### Impact
The implementation of `tf.io.decode_raw` produces incorrect results and crashes the Python interpreter when combining `fixed_length` and wider datatypes.

```python
import tensorflow as tf

tf.io.decode_raw(tf.constant(["1","2","3","4"]), tf.uint16, fixed_length=4)
```                
                   
The [implementation of the padded version](https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc) is buggy due to a confusion about pointer arithmetic rules.

First, the code [computes](https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L61) the width of each output element by dividing the `fixed_length` value to the size of the type argument:

```cc
int width = fixed_length / sizeof(T);
```

The `fixed_length` argument is also used to determine the [size needed for the output tensor](https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L63-L79):

```cc
TensorShape out_shape = input.shape();
out_shape.AddDim(width);
Tensor* output_tensor = nullptr;
OP_REQUIRES_OK(context, context->allocate_output("output", out_shape, &output_tensor));

auto out = output_tensor->flat_inner_dims<T>();
T* out_data = out.data();
memset(out_data, 0, fixed_length * flat_in.size());
```

This is followed by [reencoding code](https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L85-L94):

```cc
for (int64 i = 0; i < flat_in.size(); ++i) {
  const T* in_data = reinterpret_cast<const T*>(flat_in(i).data());

  if (flat_in(i).size() > fixed_length) {
    memcpy(out_data, in_data, fixed_length);
  } else {
    memcpy(out_data, in_data, flat_in(i).size());
  }
  out_data += fixed_length;
}
```

The erroneous code is the last line above: it is moving the `out_data` pointer by `fixed_length * sizeof(T)` bytes whereas it only copied at most `fixed_length` bytes from the input. This results in parts of the input not being decoded into the output.

Furthermore, because the pointer advance is far wider than desired, this quickly leads to writing to outside the bounds of the backing data. This OOB write leads to interpreter crash in the reproducer mentioned here, but more severe attacks can be mounted too, given that this gadget allows writing to periodically placed locations in memory.

### Patches
We have patched the issue in GitHub commit [698e01511f62a3c185754db78ebce0eee1f0184d](https://github.com/tensorflow/tensorflow/commit/698e01511f62a3c185754db78ebce0eee1f0184d).

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.

## 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`