Runtime crash via zero-sized matrix rank input
Published Jan 3, 2024 · Updated May 21, 2025
Divide-by-zero in paddle.linalg.matrix_rank in PaddlePaddle before 2.6.0 allows remote attackers to crash a consuming runtime. The GPU MatrixRankTolKernel divides the tensor element count by rows multiplied by columns without first rejecting a zero-sized matrix dimension. An application must pass an attacker-influenced zero-sized tensor to the API; the resulting process crash interrupts that application's service.
Summary
What happened
Divide-by-zero in paddle.linalg.matrix_rank in PaddlePaddle before 2.6.0 allows remote attackers to crash a consuming runtime. The GPU MatrixRankTolKernel divides the tensor element count by rows multiplied by columns without first rejecting a zero-sized matrix dimension. An application must pass an attacker-influenced zero-sized tensor to the API; the resulting process crash interrupts that application's service.
The record
- CVE
- CVE-2023-38675
- Published
- Jan 3, 2024
- Updated
- May 21, 2025
- Vendor
- PaddlePaddle
- Product
- PaddlePaddle
- Classifications
- CWE-369, T1499.004
- Attack vector
- network
- Privileges
- unauthenticated
Timeline
How it unfolded
- Jan 3, 2024CVE publishedPublication date reported by the CVE source.
- May 21, 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=0 <2.6.0
What conditions does exploitation require?
What is affected?
Published CVSS scores
CVSS describes severity. EPSS estimates exploitation probability.
Attacks
What attackers are doing with it
Daily unique IPs observed by Shadowserver honeypots for known exploited vulnerabilities (KEVs). Missing observations do not establish an absence of attacks.
Public exploit references
- PDSA-2023-007 zero-sized tensor proof of conceptproof of concept · demonstrated
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Technologies
Your stack
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Your stack
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