Process crash via zero-overlap pooling window
Published Aug 12, 2021 · Updated Aug 4, 2024
Divide-by-zero in TensorFlow Lite pooling before 2.3.4, 2.4.0 through 2.4.2, and 2.5.0 allows local users to crash a process. The AveragePool implementations derive a filter count from the overlapping filter area and divide accumulated values by it without rejecting zero. A local low-privilege user must cause an affected process to evaluate a crafted TFLite model whose pooling window has no input overlap, producing a floating-point exception and terminating that process.
Summary
What happened
Divide-by-zero in TensorFlow Lite pooling before 2.3.4, 2.4.0 through 2.4.2, and 2.5.0 allows local users to crash a process. The AveragePool implementations derive a filter count from the overlapping filter area and divide accumulated values by it without rejecting zero. A local low-privilege user must cause an affected process to evaluate a crafted TFLite model whose pooling window has no input overlap, producing a floating-point exception and terminating that process.
The record
- CVE
- CVE-2021-37684
- Published
- Aug 12, 2021
- Updated
- Aug 4, 2024
- Vendor
- Product
- TensorFlow
- Classifications
- CWE-369, T1499.004
- Attack vector
- local
- Privileges
- authenticated
Timeline
How it unfolded
- Aug 12, 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=< 2.3.4
- Affected versionversion=>= 2.4.0, < 2.4.3
- Affected versionversion=>= 2.5.0, < 2.5.1
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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Public exploit references
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Technologies
Your stack
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Your stack
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