🛡️ CVE-2021-37665 — tensorflow

🟠 CVSS 8.0 — High ✅ No Known Exploit CWE-20 OSV
8.0
CVSS Score
0 Low4 Medium7 High9 Critical10

Description

Incomplete validation in MKL requantization

Impact

Due to incomplete validation in MKL implementation of requantization, an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays:

```python

import tensorflow as tf

tf.raw_ops.RequantizationRangePerChannel(

input=[],

input_min=[0,0,0,0,0],

input_max=[1,1,1,1,1],

clip_value_max=1)

```

The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/mkl/mkl_requantization_range_per_channel_op.cc) does not validate the dimensions of the input tensor.

A similar issue occurs in MklRequantizePerChannelOp:

```python

import tensorflow as tf

from tensorflow.python.ops import gen_math_ops

gen_math_ops.requantize_per_channel(

input=[],

input_min=[-100,-100,-100,-100,-100],

input_max=[-100,-100,-100],

requested_output_min=[-100,-100,-100,-100,-100],

requested_output_max=[],

out_type=tf.int)

```

The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/mkl/mkl_requantize_per_channel_op.cc) does not perform full validation for all the input arguments.

Patches

We have patched the issue in GitHub commit [9e62869465573cb2d9b5053f1fa02a81fce21d69](https://github.com/tensorflow/tensorflow/commit/9e62869465573cb2d9b5053f1fa02a81fce21d69) and in the Github commit [203214568f5bc237603dbab6e1fd389f1572f5c9](https://github.com/tensorflow/tensorflow/commit/203214568f5bc237603dbab6e1fd389f1572f5c9).

The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, 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 members of the Aivul Team from Qihoo 360.

How this vulnerability can be exploited

This issue can be reached with local access to the system, attack complexity is low, an attacker needs low-level privileges on the target. No user interaction is required. Rated impact: confidentiality high, integrity high, availability high.

Weakness class

CVE-2021-37665 is classified as CWE-20: Improper Input Validation. The application accepts input without checking that it has the expected form, so malformed values reach code that assumes they are well formed.

Affected software

CVE-2021-37665 is recorded against 3 packages.

  • tensorflow
  • tensorflow-cpu (from 2.5.0 up to 2.5.1)
  • tensorflow-gpu (from 2.5.0 up to 2.5.1)

Timeline and source

Published on 6 March 2024 and last revised on 8 July 2026. No public exploit is currently recorded for this entry. A vendor advisory or fix has been published. Record sourced from OSV.

References

github.com (Web)
github.com (Web)
github.com (Web)
nvd.nist.gov (Web)

Details

Severity HIGH
CVSS Score 8.0
CVSS Vector CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N
CWE CWE-20
Public Exploit ✅ No
Source OSV
Published 2024-03-06
Updated 2026-08-12
Modified 2026-07-08

Affected Packages

Software From version Fixed in
tensorflow
tensorflow-cpu 2.5.0 2.5.1
tensorflow-gpu 2.5.0 2.5.1

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