Model-processing interruption via missing sparse graph break
Published Sep 25, 2025 · Updated Sep 25, 2025
Denial of service in PyTorch 2.7.0 and earlier allows remote attackers to interrupt model processing through compiled sparse conversion. Dynamo fails to break the graph when torch.Tensor.to_sparse() is followed by torch.Tensor.to_dense(), so Inductor attempts unsupported lowering and raises a NotImplementedError. Reachability requires an application to accept an attacker-influenced model and compile that operation sequence with the Inductor backend; eager execution succeeds.
Summary
What happened
Denial of service in PyTorch 2.7.0 and earlier allows remote attackers to interrupt model processing through compiled sparse conversion. Dynamo fails to break the graph when torch.Tensor.to_sparse() is followed by torch.Tensor.to_dense(), so Inductor attempts unsupported lowering and raises a NotImplementedError. Reachability requires an application to accept an attacker-influenced model and compile that operation sequence with the Inductor backend; eager execution succeeds.
The record
- CVE
- CVE-2025-55560
- 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 conditions does exploitation require?
What is affected?
Affected products and versions are unavailable in this record.
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
- Inductor sparse-conversion denial-of-service reproducerproof of concept · demonstrated
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Technologies
Your stack
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Your stack
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