5.9

CVSS Score

3.1

-

CVSS Score

Basic Information

CVE ID

CVE-2022-35966

GHSA ID

GHSA-4w68-4x85-mjj9

EPSS Score

0.4168%

CWE

CWE-20

Published

9/16/2022

Updated

1/28/2023

KEV Status

Technology

Python

Basic Information

CVE ID

GHSA ID

EPSS Score

CWE

Published

Updated

KEV Status

Technology

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CVE-2022-35966

CVE-2022-35966: TensorFlow vulnerable to segfault in `QuantizedAvgPool`

Impact

If QuantizedAvgPool is given min_input or max_input tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack.

import tensorflow as tf

ksize = [1, 2, 2, 1]
strides = [1, 2, 2, 1]
padding = "SAME"
input = tf.constant(1, shape=[1,4,4,2], dtype=tf.quint8)
min_input = tf.constant([], shape=[0], dtype=tf.float32)
max_input = tf.constant(0, shape=[1], dtype=tf.float32)
tf.raw_ops.QuantizedAvgPool(input=input, min_input=min_input, max_input=max_input, ksize=ksize, strides=strides, padding=padding)

Patches

We have patched the issue in GitHub commit 7cdf9d4d2083b739ec81cfdace546b0c99f50622.

The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by Neophytos Christou, Secure Systems Labs, Brown University.

(GitHub Advisory)

Miggo Vulnerability Database

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CVE-2022-35966

CVE-2022-35966:

5.9

CVSS Score

3.1

-

CVSS Score

Basic Information

CVE ID

CVE-2022-35966

GHSA ID

GHSA-4w68-4x85-mjj9

EPSS Score

0.4168%

CWE

CWE-20

Published

9/16/2022

Updated

1/28/2023

KEV Status

Technology

Python

Basic Information

CVE ID

GHSA ID

EPSS Score

CWE

Published

Updated

KEV Status

Technology

Technical Details

CVSS Vector

CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H

Package Name	Ecosystem	Vulnerable Versions	First Patched Version
tensorflow	pip	< 2.7.2	2.7.2
tensorflow	pip	>= 2.8.0, < 2.8.1	2.8.1
tensorflow	pip	>= 2.9.0, < 2.9.1	2.9.1
tensorflow-cpu	pip	< 2.7.2	2.7.2
tensorflow-cpu	pip	>= 2.8.0, < 2.8.1	2.8.1
tensorflow-cpu	pip	>= 2.9.0, < 2.9.1	2.9.1
tensorflow-gpu	pip	< 2.7.2	2.7.2
tensorflow-gpu	pip	>= 2.8.0, < 2.8.1	2.8.1
tensorflow-gpu	pip	>= 2.9.0, < 2.9.1	2.9.1

Technical Details

Vulnerability Intelligence
Miggo AI

Root Cause Analysis

The vulnerability stems from improper input validation in QuantizedAvgPool's implementation. The commit diff shows:

Added OP_REQUIRES checks to enforce scalar shape for min_input/max_input
Changed value access from .flat<float>()(0) to .scalar<float>()()
Added test cases in quantization_ops_test.py to verify rank validation This confirms the vulnerable code path was in QuantizedAvgPoolingOp's Compute method where input tensors' ranks were not properly validated before access.

Vulnerable functions

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WAF Protection Rules

WAF Rule

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Reasoning

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5.9

-

Basic Information

Basic Information

Concerned about an active attack path?

CVE-2022-35966: TensorFlow vulnerable to segfault in `QuantizedAvgPool`

Impact

Patches

For more information

Attribution

CVE-2022-35966:

5.9

-

Basic Information

Basic Information

Technical Details

Technical Details

Vulnerability IntelligenceMiggo AI

Root Cause Analysis

Vulnerable functions

WAF Protection Rules

WAF Rule

Reasoning

Vulnerability IntelligenceMiggo AI

5.9

5.9

Basic Information

Basic Information

Vulnerability IntelligenceMiggo AI

Root Cause Analysis

Vulnerable functions

WAF Protection Rules

WAF Rule

Reasoning

Technical Details

CVE-2022-35966: TensorFlow vulnerable to segfault in `QuantizedAvgPool`

Impact

Patches

For more information

Attribution

Vulnerability Intelligence
Miggo AI

Vulnerability Intelligence
Miggo AI

Vulnerability Intelligence
Miggo AI