Process crashes via unvalidated checkpoint tensor metadata
Published Nov 5, 2021 · Updated Aug 4, 2024
Checkpoint parsing in Google TensorFlow before 2.4.4, 2.5.0 through 2.5.1, and 2.6.0 allows local users to crash processes. TensorSliceReader constructs tensor shapes and slices from checkpoint metadata without rejecting negative dimensions, starts, or lengths. Exploitation requires control of a saved checkpoint outside TensorFlow and can cause integer overflow, undefined behavior, segmentation faults, or CHECK-fail termination.
Summary
What happened
Checkpoint parsing in Google TensorFlow before 2.4.4, 2.5.0 through 2.5.1, and 2.6.0 allows local users to crash processes. TensorSliceReader constructs tensor shapes and slices from checkpoint metadata without rejecting negative dimensions, starts, or lengths. Exploitation requires control of a saved checkpoint outside TensorFlow and can cause integer overflow, undefined behavior, segmentation faults, or CHECK-fail termination.
The record
- CVE
- CVE-2021-41203
- Published
- Nov 5, 2021
- Updated
- Aug 4, 2024
- Vendor
- Product
- TensorFlow
- Classifications
- CWE-190, CWE-345, T1565.001
- Attack vector
- local
- Privileges
- authenticated
Timeline
How it unfolded
- Nov 5, 2021CVE publishedPublication date reported by the CVE source.
- Aug 4, 2024Record updatedLatest update available in the CVE record.
Exploitability
Present is not the same as exploitable
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Is a vulnerable build present?
Compare these published version ranges with your installed build and any vendor patches.
- Affected versionversion=< 2.4.4
- Affected versionversion=>= 2.5.0, < 2.5.2
- Affected versionversion=>= 2.6.0, < 2.6.1
What conditions does exploitation require?
What is affected?
Published CVSS scores
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Attacks
What attackers are doing with it
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Weakness, pattern, technique
Public exploit references
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Technologies
Your stack
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Your stack
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