CVE-2025-55558: n/a
Source CVE Database V5
Not known to be exploited · CVSS 7.5 High.
Data as of 25 Oct 2026, 19:02 UTC
- EPSS
- 0.4% chance of exploitation in 30 days – higher than 38% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:HExploitation evidence
Disclosed: evidence found.
CVE published 25 Sep 2025
Detectable: no evidence on record.
No public nuclei template indexed
Weaponised: no evidence on record.
No public exploit code indexed in Metasploit or Exploit-DB
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by Mirage: no evidence on record.
No attack traffic recorded by OffSeq Mirage sensors
A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 2026-10-25.
What to do
AI-suggested
To mitigate this vulnerability effectively, European organizations should: 1) Immediately audit their AI/ML pipelines to identify any PyTorch 2.7.0 models that use the combination of torch.nn.Conv2d, torch.nn.functional.hardshrink, and tensor operations involving view and mv compiled with Inductor. 2) Temporarily disable or avoid using the Inductor compiler backend for model compilation until a patch is released. 3) Implement network-level protections such as firewalls and segmentation to restrict access to AI model serving endpoints, minimizing exposure to remote exploitation. 4) Monitor application logs and system metrics for signs of crashes or resource exhaustion indicative of attempted exploitation. 5) Engage with PyTorch community and vendors for updates and patches, and plan for rapid deployment once available. 6) Consider fallback to earlier stable PyTorch versions or alternative compilation backends if feasible. 7) Incorporate runtime protections such as container resource limits and watchdog processes to recover from unexpected crashes promptly.
Description
A buffer overflow occurs in pytorch v2.7.0 when a PyTorch model consists of torch.nn.Conv2d, torch.nn.functional.hardshrink, and torch.Tensor.view-torch.mv() and is compiled by Inductor, leading to a Denial of Service (DoS).