Pytorch: Uncontrolled Resource Consumption
CVE-2025-55560 is a high-severity uncontrolled resource consumption vulnerability in Pytorch from Linux Foundation, with a CVSS 3.1 base score of 7.5 out of 10. It can be exploited remotely over the network by an unauthenticated attacker, without any action from a user. Affected Pytorch versions: 2.7.0 and earlier. The record does not name a fixed version, so check the vendor advisory. There is no confirmed exploitation so far. EPSS estimates a 0.41% chance of exploitation within 30 days, which is low compared with other CVEs.
High severity with no evidence of exploitation so far.
What to do
- Check which version of Pytorch you run against the affected versions below, and install the vendor's latest release.
- Until you can update, restrict network access to the affected service so it is not reachable from untrusted networks.
Affected products and versions
| Vendor | Product | Affected versions | Fixed in |
|---|---|---|---|
| Linux Foundation | Pytorch | 2.7.0 and earlier | Not stated |
Version data comes from NVD's product matching. Confirm against the vendor advisory before deciding you are unaffected.
How the severity score breaks down
- Attack vector
- NetworkCan be exploited remotely over a network, including the internet.
- Attack complexity
- LowNo special conditions are needed; the attack works reliably.
- Privileges required
- NoneNo account or login is needed.
- User interaction
- NoneNo action by a victim is required.
- Scope
- UnchangedImpact stays within the vulnerable component.
- Confidentiality
- NoneNo data is disclosed.
- Integrity
- NoneNothing can be modified.
- Availability
- HighThe component can be taken down completely.
Vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H. Scored by NVD.
| Scoring system | Score | Severity | Scored by |
|---|---|---|---|
| CVSS 3.1 | 7.5 | High | NVD |
| CVSS 3.1 | 7.5 | High | CISA (ADP) |
Official description
An issue in pytorch v2.7.0 can lead to a Denial of Service (DoS) when a PyTorch model consists of torch.Tensor.to_sparse() and torch.Tensor.to_dense() and is compiled by Inductor.
Source: the CVE record as published by its numbering authority, via NVD and CVE.org.
The underlying weakness: Uncontrolled Resource Consumption
The product does not properly control the allocation and maintenance of a limited resource.
How developers prevent this class of flaw
- Ensure that protocols have specific limits of scale placed on them.
- Ensure that all failures in resource allocation place the system into a safe posture.
More Uncontrolled Resource Consumption vulnerabilities (CWE-400)
Timeline
- CVE record published.
- CISA published its SSVC assessment (exploitation: none, automatable: yes, technical impact: partial).
- Record last changed materially (score, affected products, status or references).
References
Patches and release notes
- github.com/pytorch/pytorch/pull/151897Issue Tracking
Third-party and government advisories
- gist.github.com/shaoyuyoung/0e7d2a586297ae9c8ed14d8706749efcThird Party Advisory
Other references
- github.com/pytorch/pytorch/issues/151522Issue Tracking
Other vulnerabilities in Pytorch
- 7.5 High severity
- 7.5 High severity
Recent Uncontrolled Resource Consumption vulnerabilities
- 7.5 High severity
- 7.5 High severity
- 7.5 High severity
- 7.5 High severity
- 7.5 High severity
- 7.5 High severity
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