🛡️ CVE-2021-37665 — tensorflow
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
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
Affected Packages
| Software | From version | Fixed in |
|---|---|---|
| tensorflow | — | — |
| tensorflow-cpu | 2.5.0 | 2.5.1 |
| tensorflow-gpu | 2.5.0 | 2.5.1 |
References
Similar Threats
More CVE 2021 advisories
Browse all of CVE 2021 in the advisory index.
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