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Segfault if `tf.histogram_fixed_width` is called with NaN values in TensorFlow

Moderate severity GitHub Reviewed Published May 17, 2022 in tensorflow/tensorflow • Updated Jan 30, 2023

Package

pip tensorflow (pip)

Affected versions

< 2.6.4
>= 2.7.0, < 2.7.2
>= 2.8.0, < 2.8.1

Patched versions

2.6.4
2.7.2
2.8.1
pip tensorflow-cpu (pip)
< 2.6.4
>= 2.7.0, < 2.7.2
>= 2.8.0, < 2.8.1
2.6.4
2.7.2
2.8.1
pip tensorflow-gpu (pip)
< 2.6.4
>= 2.7.0, < 2.7.2
>= 2.8.0, < 2.8.1
2.6.4
2.7.2
2.8.1

Description

Impact

The implementation of tf.histogram_fixed_width is vulnerable to a crash when the values array contain NaN elements:

import tensorflow as tf
import numpy as np

tf.histogram_fixed_width(values=np.nan, value_range=[1,2])

The implementation assumes that all floating point operations are defined and then converts a floating point result to an integer index:

index_to_bin.device(d) =
    ((values.cwiseMax(value_range(0)) - values.constant(value_range(0)))
         .template cast<double>() /
     step)
        .cwiseMin(nbins_minus_1)
        .template cast<int32>();

If values contains NaN then the result of the division is still NaN and the cast to int32 would result in a crash.

This only occurs on the CPU implementation.

Patches

We have patched the issue in GitHub commit e57fd691c7b0fd00ea3bfe43444f30c1969748b5.

The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, 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 externally via a GitHub issue.

References

@mihaimaruseac mihaimaruseac published to tensorflow/tensorflow May 17, 2022
Published by the National Vulnerability Database May 21, 2022
Published to the GitHub Advisory Database May 24, 2022
Reviewed May 24, 2022
Last updated Jan 30, 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
Local
Attack complexity
Low
Privileges required
Low
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:L/AC:L/PR:L/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.
(25th percentile)

Weaknesses

Improper Input Validation

The product receives input or data, but it does not validate or incorrectly validates that the input has the properties that are required to process the data safely and correctly. Learn more on MITRE.

CVE ID

CVE-2022-29211

GHSA ID

GHSA-xrp2-fhq4-4q3w

Source code

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