Memory disclosure or corruption via empty input tensor
Published Aug 12, 2021 · Updated Aug 4, 2024
Out-of-bounds heap access in Google TensorFlow before 2.3.4, 2.4.0 through 2.4.2, and 2.5.0 allows local users to read or write memory. FractionalAvgPoolGrad accepts a zero-sized input shape, constructs an empty EigenDoubleMatrixMap, and indexes beyond that allocation. Reachability requires local low-privileged access to execute TensorFlow code and pass a zero dimension to the operation, exposing or corrupting process memory.
Summary
What happened
Out-of-bounds heap access in Google TensorFlow before 2.3.4, 2.4.0 through 2.4.2, and 2.5.0 allows local users to read or write memory. FractionalAvgPoolGrad accepts a zero-sized input shape, constructs an empty EigenDoubleMatrixMap, and indexes beyond that allocation. Reachability requires local low-privileged access to execute TensorFlow code and pass a zero dimension to the operation, exposing or corrupting process memory.
The record
- CVE
- CVE-2021-37651
- Published
- Aug 12, 2021
- Updated
- Aug 4, 2024
- Vendor
- Product
- TensorFlow
- Classifications
- CWE-125, CWE-787
- 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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