Memory corruption via truncated TFLite allocation size
Published Feb 4, 2022 · Updated Apr 22, 2025
Integer overflow in TFLite array creation in Google TensorFlow allows attackers to corrupt process memory via a crafted TFLite model. TfLiteIntArrayGetSizeInBytes stores the attacker-controlled byte count in an int, allowing truncation before TfLiteIntArrayCreate allocates the array. Processing attacker-controlled model dimensions is required, and the resulting undersized allocation can corrupt or interrupt the hosting process.
Summary
What happened
Integer overflow in TFLite array creation in Google TensorFlow allows attackers to corrupt process memory via a crafted TFLite model. TfLiteIntArrayGetSizeInBytes stores the attacker-controlled byte count in an int, allowing truncation before TfLiteIntArrayCreate allocates the array. Processing attacker-controlled model dimensions is required, and the resulting undersized allocation can corrupt or interrupt the hosting process.
The record
- CVE
- CVE-2022-23558
- Published
- Feb 4, 2022
- Updated
- Apr 22, 2025
- Vendor
- Product
- TensorFlow
- Classifications
- CWE-190
- Attack vector
- network
- Privileges
- authenticated
Timeline
How it unfolded
- Feb 4, 2022CVE publishedPublication date reported by the CVE source.
- Apr 22, 2025Record updatedLatest update available in the CVE record.
Exploitability
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Is a vulnerable build present?
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- Affected versionversion=< 2.5.3
- Affected versionversion=>= 2.6.0, < 2.6.3
- Affected versionversion=>= 2.7.0, < 2.7.1
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What is affected?
Published CVSS scores
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Attacks
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Technologies
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Your stack
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