Service crash via unchecked convolution channel divisor
Published Mar 24, 2023 · Updated Feb 19, 2025
Denial of service in TensorFlow before 2.11.1 allows remote attackers to crash a TFLite consumer via a crafted convolution model. The Prepare path in tensorflow/lite/kernels/conv.cc uses filter_input_channel in a modulo operation before verifying that the value is positive, so zero becomes a divisor and raises a floating-point exception. Exploitation requires an application to process an attacker-supplied TFLite model and interrupts that host process without reported data disclosure or modification.
Summary
What happened
Denial of service in TensorFlow before 2.11.1 allows remote attackers to crash a TFLite consumer via a crafted convolution model. The Prepare path in tensorflow/lite/kernels/conv.cc uses filter_input_channel in a modulo operation before verifying that the value is positive, so zero becomes a divisor and raises a floating-point exception. Exploitation requires an application to process an attacker-supplied TFLite model and interrupts that host process without reported data disclosure or modification.
The record
- CVE
- CVE-2023-27579
- Published
- Mar 24, 2023
- Updated
- Feb 19, 2025
- Vendor
- Product
- TensorFlow
- Classifications
- CWE-697, T1499.004
- Attack vector
- network
- Privileges
- unauthenticated
Timeline
How it unfolded
- Mar 24, 2023CVE publishedPublication date reported by the CVE source.
- Feb 19, 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?
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- Affected versionversion=< 2.11.1
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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