XLA compilation interruption via negative Conv2D dimension
Published Sep 25, 2025 · Updated Sep 25, 2025
Runtime error in TensorFlow 2.18.0 allows remote attackers to interrupt XLA model compilation through a crafted Conv2D layer. XLA accepts a 4-by-4 kernel exceeding its 3-by-3 input under valid padding, then derives a negative output dimension and aborts compilation. The trigger requires control of a model submitted to an XLA-enabled service; eager execution completes without the compile-time failure.
Summary
What happened
Runtime error in TensorFlow 2.18.0 allows remote attackers to interrupt XLA model compilation through a crafted Conv2D layer. XLA accepts a 4-by-4 kernel exceeding its 3-by-3 input under valid padding, then derives a negative output dimension and aborts compilation. The trigger requires control of a model submitted to an XLA-enabled service; eager execution completes without the compile-time failure.
The record
- CVE
- CVE-2025-55559
- Published
- Sep 25, 2025
- Updated
- Sep 25, 2025
- Vendor
- Unknown vendor
- Product
- Unknown product
- Classifications
- CWE-400, T1499
- Attack vector
- network
- Privileges
- unauthenticated
Timeline
How it unfolded
- Sep 25, 2025CVE publishedPublication date reported by the CVE source.
- Sep 25, 2025Record updatedLatest update available in the CVE record.
Exploitability
Present is not the same as exploitable
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What is affected?
Affected products and versions are unavailable in this record.
Published CVSS scores
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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
- TensorFlow Conv2D XLA compilation reproducerproof of concept · demonstrated
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Technologies
Your stack
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Your stack
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