Process crash via unchecked tensor ranks
Published Sep 16, 2022 · Updated Apr 23, 2025
Improper input validation in Google TensorFlow before 2.7.2, 2.8.0, and 2.9.0 allows attackers to crash the process. BlockLSTMGradV2 fails to enforce required tensor ranks before accessing dimension sizes, causing a native-kernel segmentation fault. Triggering the flaw requires execution of the raw operation with malformed tensors; the process crash interrupts the hosting ML workload.
Summary
What happened
Improper input validation in Google TensorFlow before 2.7.2, 2.8.0, and 2.9.0 allows attackers to crash the process. BlockLSTMGradV2 fails to enforce required tensor ranks before accessing dimension sizes, causing a native-kernel segmentation fault. Triggering the flaw requires execution of the raw operation with malformed tensors; the process crash interrupts the hosting ML workload.
The record
- CVE
- CVE-2022-35964
- Published
- Sep 16, 2022
- Updated
- Apr 23, 2025
- Vendor
- Product
- TensorFlow
- Classifications
- CWE-20, T1499
- Attack vector
- network
- Privileges
- unauthenticated
Timeline
How it unfolded
- Sep 16, 2022CVE publishedPublication date reported by the CVE source.
- Apr 23, 2025Record 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=< 2.7.2
- Affected versionversion=>= 2.8.0, < 2.8.1
- Affected versionversion=>= 2.9.0, < 2.9.1
What conditions does exploitation require?
What is affected?
Published CVSS scores
CVSS describes severity. EPSS estimates exploitation probability.
Attacks
What attackers are doing with it
Daily unique IPs observed by Shadowserver honeypots for known exploited vulnerabilities (KEVs). Missing observations do not establish an absence of attacks.
Public exploit references
- Upstream BlockLSTMGradV2 crash proof of conceptproof of concept · demonstrated
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Technologies
Your stack
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Your stack
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