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Missing validation causes denial of service via `LSTMBlockCell`

Moderate severity GitHub Reviewed Published May 17, 2022 in tensorflow/tensorflow • Updated Jul 21, 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.raw_ops.LSTMBlockCell does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack:

import tensorflow as tf

tf.raw_ops.LSTMBlockCell( 
  x=tf.constant(0.837607, shape=[28,29], dtype=tf.float32),
  cs_prev=tf.constant(0, shape=[28,17], dtype=tf.float32),
  h_prev=tf.constant(0.592631638, shape=[28,17], dtype=tf.float32),
  w=tf.constant(0.887386262, shape=[46,68], dtype=tf.float32),
  wci=tf.constant(0, shape=[], dtype=tf.float32),
  wcf=tf.constant(0, shape=[17], dtype=tf.float32),
  wco=tf.constant(0.592631638, shape=[28,17], dtype=tf.float32),
  b=tf.constant(0.75259006, shape=[68], dtype=tf.float32),
  forget_bias=1, cell_clip=0, use_peephole=False)

The code does not validate the ranks of any of the arguments to this API call. This results in CHECK-failures when the elements of the tensor are accessed.

Patches

We have patched the issue in GitHub commit 803404044ae7a1efac48ba82d74111fce1ddb09a.

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 by Neophytos Christou from Secure Systems Lab at Brown University.

References

@mihaimaruseac mihaimaruseac published to tensorflow/tensorflow May 17, 2022
Published by the National Vulnerability Database May 20, 2022
Published to the GitHub Advisory Database May 24, 2022
Reviewed May 24, 2022
Last updated Jul 21, 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

0.098%
(42nd percentile)

Weaknesses

CVE ID

CVE-2022-29200

GHSA ID

GHSA-2vv3-56qg-g2cf

Source code

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