Denial of service via unchecked tensor ranks
Published Mar 24, 2023 · Updated Feb 19, 2025
NULL pointer dereference in TensorFlow before 2.11.1 allows remote attackers to crash an MKL-enabled process via malformed range tensors. The MKL QuantizedMatMulWithBiasAndDequantize kernel reads the first element of min/max range tensors without first requiring scalar rank, so empty tensors trigger an invalid access. Reachability requires an application to pass attacker-controlled tensors to the MKL-enabled operation; exploitation terminates the hosting process without reported data disclosure or modification.
Summary
What happened
NULL pointer dereference in TensorFlow before 2.11.1 allows remote attackers to crash an MKL-enabled process via malformed range tensors. The MKL QuantizedMatMulWithBiasAndDequantize kernel reads the first element of min/max range tensors without first requiring scalar rank, so empty tensors trigger an invalid access. Reachability requires an application to pass attacker-controlled tensors to the MKL-enabled operation; exploitation terminates the hosting process without reported data disclosure or modification.
The record
- CVE
- CVE-2023-25670
- Published
- Mar 24, 2023
- Updated
- Feb 19, 2025
- Vendor
- Product
- TensorFlow
- Classifications
- CWE-476, T1499
- 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
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Your stack
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