Model-evaluation denial of service via recursive subgraphs
Published May 14, 2021 · Updated Aug 3, 2024
Denial of service in Google TensorFlow allows attackers to crash TFLite model evaluation with a crafted cyclic subgraph. The TFLite interpreter accepts recursive subgraph references, so While evaluation can repeatedly call the same condition and body graph until an infinite loop or stack exhaustion occurs. Exploitation requires an application to evaluate an attacker-controlled model; the bounded consequence is a hang or process crash.
Summary
What happened
Denial of service in Google TensorFlow allows attackers to crash TFLite model evaluation with a crafted cyclic subgraph. The TFLite interpreter accepts recursive subgraph references, so While evaluation can repeatedly call the same condition and body graph until an infinite loop or stack exhaustion occurs. Exploitation requires an application to evaluate an attacker-controlled model; the bounded consequence is a hang or process crash.
The record
- CVE
- CVE-2021-29591
- Published
- May 14, 2021
- Updated
- Aug 3, 2024
- Vendor
- Product
- TensorFlow
- Classifications
- CWE-674, CWE-835, T1499.004
- Attack vector
- local
- Privileges
- authenticated
Timeline
How it unfolded
- May 14, 2021CVE publishedPublication date reported by the CVE source.
- Aug 3, 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=< 2.1.4
- Affected versionversion=>= 2.2.0, < 2.2.3
- Affected versionversion=>= 2.3.0, < 2.3.3
- Affected versionversion=>= 2.4.0, < 2.4.2
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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