{"id":87042,"date":"2026-06-04T18:18:56","date_gmt":"2026-06-04T18:18:56","guid":{"rendered":""},"modified":"-0001-11-30T00:00:00","modified_gmt":"-0001-11-30T00:00:00","slug":"cve-2025-55560-denial-of-service-vulnerability-in-pytorch-v2-7-0","status":"publish","type":"post","link":"https:\/\/www.ameeba.com\/blog\/cve-2025-55560-denial-of-service-vulnerability-in-pytorch-v2-7-0\/","title":{"rendered":"CVE-2025-55560: Denial of Service Vulnerability in PyTorch v2.7.0<\/strong>"},"content":{"rendered":"

Overview<\/strong><\/p>\n

A significant issue has been identified in pyTorch v2.7.0, a popular open-source machine learning library. This vulnerability, identified as CVE-2025-55560, can lead to a Denial of Service (DoS) attack, potentially compromising systems and leading to data leakage. Developers, system administrators, and organizations using affected versions are advised to implement the necessary patches or mitigation strategies to prevent a potential exploit.<\/p>\n

Vulnerability Summary<\/strong><\/p>\n

CVE ID: CVE-2025-55560
\nSeverity: High (7.5 CVSS Score)
\nAttack Vector: Network
\nPrivileges Required: None
\nUser Interaction: None
\nImpact: System compromise or data leakage<\/p>\n

Affected Products<\/strong><\/p>\n

Product | Affected Versions<\/p>\n

PyTorch | v2.7.0<\/p>\n

How the Exploit Works<\/strong><\/p>\n

The exploit takes advantage of a specific issue in pyTorch v2.7.0, where the combination of torch.Tensor.to_sparse() and torch.Tensor.to_dense() in a PyTorch model can lead to a Denial of Service (DoS) when compiled by Inductor. Attackers can craft malicious models that, when processed, exhaust system resources, causing a DoS condition and potentially leading to system compromise or data leakage.<\/p>\n

Conceptual Example Code<\/strong><\/p>\n

The following is a simplified conceptual example of how an attacker might exploit this vulnerability:<\/p>\n

import torch\n# Define a PyTorch model with the vulnerability\nclass VulnerableModel(torch.nn.Module):\ndef forward(self, x):\nx = x.to_sparse()\nreturn x.to_dense()\n# Compile the model with Inductor\nmodel = VulnerableModel()\n# Craft a malicious input that triggers the vulnerability\nmalicious_input = torch.randn(1000000, 1000000)\n# Pass the malicious input to the model\nmodel(malicious_input)<\/code><\/pre>\n
In this example, the malicious_input tensor is large enough to exhaust system resources when the `to_dense()` method is called, causing a DoS condition.<\/p>\n","protected":false},"excerpt":{"rendered":"
Overview A significant issue has been identified in pyTorch v2.7.0, a popular open-source machine learning library. This vulnerability, identified as CVE-2025-55560, can lead to a Denial of Service (DoS) attack, potentially compromising systems and leading to data leakage. Developers, system administrators, and organizations using affected versions are advised to implement the necessary patches or mitigation […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"footnotes":""},"categories":[],"tags":[],"vendor":[],"product":[],"attack_vector":[],"asset_type":[],"severity":[],"exploit_status":[],"class_list":["post-87042","post","type-post","status-publish","format-standard","hentry"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t