Summary
CHECK fail in TensorListScatter and TensorListScatterV2 in eager mode
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Attribution
This vulnerability has been reported by Pattarakrit Rattankul
Impact
Another instance of CVE-2022-35991, where TensorListScatter and TensorListScatterV2 crash via non scalar inputs inelement_shape, was found in eager mode and fixed.
import tensorflow as tf
arg_0=tf.random.uniform(shape=(2, 2, 2), dtype=tf.float16, maxval=None)
arg_1=tf.random.uniform(shape=(2, 2, 2), dtype=tf.int32, maxval=65536)
arg_2=tf.random.uniform(shape=(2, 2, 2), dtype=tf.int32, maxval=65536)
arg_3=''
tf.raw_ops.TensorListScatter(tensor=arg_0, indices=arg_1,
element_shape=arg_2, name=arg_3)
Affected versions
Security releases
Kodem intelligence
Severity tells you how bad this could be in the worst case. It does not tell you whether you are exposed. Exploitability and impact are functions of runtime truth: whether the vulnerable code is present, reachable, and actually executes in your application. A vulnerable package can sit in your dependency tree and never run.
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We have patched the issue in GitHub commit bf9932fc907aff0e9e8cccf769e8b00d30fd81a1.
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.
Frequently Asked Questions
- What is GHSA-XF83-Q765-XM6M? GHSA-XF83-Q765-XM6M is a low-severity security vulnerability in tensorflow (pip), affecting versions < 2.8.4. It is fixed in 2.8.4, 2.9.3, 2.10.1.
- Which packages are affected by GHSA-XF83-Q765-XM6M?
tensorflow(pip) (versions < 2.8.4)tensorflow-cpu(pip) (versions < 2.8.4)tensorflow-gpu(pip) (versions < 2.8.4)
- Is there a fix for GHSA-XF83-Q765-XM6M? Yes. GHSA-XF83-Q765-XM6M is fixed in 2.8.4, 2.9.3, 2.10.1. Upgrade to this version or later.
- Is GHSA-XF83-Q765-XM6M exploitable, and should I be worried? Whether GHSA-XF83-Q765-XM6M 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
- What actually determines whether GHSA-XF83-Q765-XM6M 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.
- How do I fix GHSA-XF83-Q765-XM6M?
- Upgrade
tensorflowto 2.8.4 or later - Upgrade
tensorflowto 2.9.3 or later - Upgrade
tensorflowto 2.10.1 or later - Upgrade
tensorflow-cputo 2.8.4 or later - Upgrade
tensorflow-gputo 2.8.4 or later - Upgrade
tensorflow-cputo 2.9.3 or later - Upgrade
tensorflow-gputo 2.9.3 or later - Upgrade
tensorflow-cputo 2.10.1 or later - Upgrade
tensorflow-gputo 2.10.1 or later
- Upgrade