{"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":"-0001-11-30T06:00:00","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":"<strong>CVE-2025-55560: Denial of Service Vulnerability in PyTorch v2.7.0<\/strong>"},"content":{"rendered":"<p><strong>Overview<\/strong><\/p>\n<p>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<p><strong>Vulnerability Summary<\/strong><\/p>\n<p>CVE ID: CVE-2025-55560<br \/>\nSeverity: High (7.5 CVSS Score)<br \/>\nAttack Vector: Network<br \/>\nPrivileges Required: None<br \/>\nUser Interaction: None<br \/>\nImpact: System compromise or data leakage<\/p>\n<p><strong>Affected Products<\/strong><\/p><div id=\"ameeb-1451493340\" class=\"ameeb-content-2 ameeb-entity-placement\"><div style=\"border-left: 4px solid #555; padding-left: 20px; margin: 48px 0; font-family: Roboto, sans-serif; color: #ffffff; line-height: 1.6; max-width: 720px;\">\r\n  <h2 style=\"margin-top: 0; font-size: 22px; font-weight: 600; display: flex; align-items: center; letter-spacing: -0.02em;\">\r\n    <a href=\"https:\/\/www.ameeba.com\/chat\" style=\"display: inline-flex; align-items: center; margin-right: 10px;\">\r\n      <img decoding=\"async\" src=\"https:\/\/www.ameeba.com\/blog\/wp-content\/uploads\/2025\/10\/Best-App-icon-Ameeba.png\" alt=\"Ameeba Chat Icon\" style=\"width: 42px; height: 42px;\" \/>\r\n    <\/a>\r\n    Share secrets securely\r\n  <\/h2>\r\n\r\n  <p style=\"margin-bottom: 14px; color: #d1d5db;\">\r\n    Ameeba is private infrastructure for communication and sensitive work built on encrypted identity instead of exposed corporate identity systems.\r\n  <\/p>\r\n\r\n  <p style=\"margin-bottom: 18px; color: #a1a1aa;\">\r\n    Passwords, credentials, confidential files, screenshots, internal discussions, sensitive AI context, and private coordination should not become exposed across ordinary communication platforms.\r\n  <\/p>\r\n\r\n  <ul style=\"list-style: none; padding-left: 0; margin-bottom: 24px; color: #e4e4e7;\">\r\n    <li style=\"margin-bottom: 8px;\">\u2022 Encrypted identity<\/li>\r\n    <li style=\"margin-bottom: 8px;\">\u2022 Private Spaces for organizations and teams<\/li>\r\n    <li style=\"margin-bottom: 8px;\">\u2022 End-to-end encrypted chat, calls, files, and notes<\/li>\r\n    <li style=\"margin-bottom: 8px;\">\u2022 Sensitive AI work and protected collaboration<\/li>\r\n    <li>\u2022 Built for information that cannot leak<\/li>\r\n  <\/ul>\r\n\r\n  <p style=\"font-style: italic; font-weight: 600; margin-bottom: 24px; color: #ffffff;\">\r\n    Our mission is to secure human work alongside AI.\r\n  <\/p>\r\n\r\n  <div style=\"display: flex; flex-wrap: wrap; gap: 12px;\">\r\n    <a href=\"https:\/\/www.ameeba.com\/chat\/download\" style=\"background-color: #ffffff; color: #000000; padding: 10px 20px; text-decoration: none; border-radius: 8px; font-weight: 500;\">\r\n      Download Ameeba\r\n    <\/a>\r\n\r\n    <a href=\"https:\/\/www.ameeba.com\/chat\" style=\"border: 1px solid #ffffff; color: #ffffff; padding: 10px 20px; text-decoration: none; border-radius: 8px; font-weight: 500;\">\r\n      Learn More\r\n    <\/a>\r\n  <\/div>\r\n<\/div><\/div>\n<p>Product | Affected Versions<\/p>\n<p>PyTorch | v2.7.0<\/p>\n<p><strong>How the Exploit Works<\/strong><\/p>\n<p>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<p><strong>Conceptual Example Code<\/strong><\/p><div id=\"ameeb-2031659947\" class=\"ameeb-content ameeb-entity-placement\"><div class=\"poptin-embedded\" data-id=\"f6b387694f681\"><\/div>\r\n\r\n\r\n\r\n\r\n\r\n<\/div>\n<p>The following is a simplified conceptual example of how an attacker might exploit this vulnerability:<\/p>\n<pre><code class=\"\" data-line=\"\">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<p>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":"<p>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 [&hellip;]<\/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,"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":[],"_links":{"self":[{"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/posts\/87042","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/comments?post=87042"}],"version-history":[{"count":0,"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/posts\/87042\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/media?parent=87042"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/categories?post=87042"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/tags?post=87042"},{"taxonomy":"vendor","embeddable":true,"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/vendor?post=87042"},{"taxonomy":"product","embeddable":true,"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/product?post=87042"},{"taxonomy":"attack_vector","embeddable":true,"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/attack_vector?post=87042"},{"taxonomy":"asset_type","embeddable":true,"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/asset_type?post=87042"},{"taxonomy":"severity","embeddable":true,"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/severity?post=87042"},{"taxonomy":"exploit_status","embeddable":true,"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/exploit_status?post=87042"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}