Local memory corruption via invalid axis value
Published May 14, 2021 · Updated Aug 3, 2024
Out-of-bounds write in Google TensorFlow TFLite before 2.5.0 allows local users to corrupt memory via a specially crafted model. The ArgMin/ArgMax kernel allocates output_dims for one fewer dimension but fails to validate axis_value, so an out-of-range axis makes the loop write past the heap array. Exploitation requires loading the attacker-controlled TFLite model into an affected local TensorFlow process and can crash or otherwise corrupt that process.
Summary
What happened
Out-of-bounds write in Google TensorFlow TFLite before 2.5.0 allows local users to corrupt memory via a specially crafted model. The ArgMin/ArgMax kernel allocates output_dims for one fewer dimension but fails to validate axis_value, so an out-of-range axis makes the loop write past the heap array. Exploitation requires loading the attacker-controlled TFLite model into an affected local TensorFlow process and can crash or otherwise corrupt that process.
The record
- CVE
- CVE-2021-29603
- Published
- May 14, 2021
- Updated
- Aug 3, 2024
- Vendor
- Product
- TensorFlow
- Classifications
- CWE-787, T1203
- 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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Public exploit references
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Technologies
Your stack
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Your stack
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