Arbitrary write via unchecked sparse tensor indices
Published Feb 4, 2022 · Updated Apr 23, 2025
Out-of-bounds write in Google TensorFlow before 2.5.3, 2.6.0 through 2.6.2, and 2.7.0 allows attackers arbitrary writes via crafted TFLite models. During sparse fully connected evaluation, fully_connected.cc trusts output dimensions and sparse array indices without verifying that derived offsets fit the input and output tensors. Processing a malicious model is required; unchecked writes can corrupt allocator linked-list metadata and yield an arbitrary write primitive in the TensorFlow process.
Summary
What happened
Out-of-bounds write in Google TensorFlow before 2.5.3, 2.6.0 through 2.6.2, and 2.7.0 allows attackers arbitrary writes via crafted TFLite models. During sparse fully connected evaluation, fully_connected.cc trusts output dimensions and sparse array indices without verifying that derived offsets fit the input and output tensors. Processing a malicious model is required; unchecked writes can corrupt allocator linked-list metadata and yield an arbitrary write primitive in the TensorFlow process.
The record
- CVE
- CVE-2022-23561
- Published
- Feb 4, 2022
- Updated
- Apr 23, 2025
- Vendor
- Product
- TensorFlow
- Classifications
- CWE-787
- Attack vector
- network
- Privileges
- authenticated
Timeline
How it unfolded
- Feb 4, 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=< 2.5.3
- Affected versionversion=>= 2.6.0, < 2.6.3
- Affected versionversion=>= 2.7.0, < 2.7.1
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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