{"id":86856,"date":"2026-04-03T13:10:08","date_gmt":"2026-04-03T13:10:08","guid":{"rendered":""},"modified":"-0001-11-30T00:00:00","modified_gmt":"-0001-11-30T06:00:00","slug":"cve-2025-48956-denial-of-service-vulnerability-in-vllm-language-models","status":"publish","type":"post","link":"https:\/\/www.ameeba.com\/blog\/cve-2025-48956-denial-of-service-vulnerability-in-vllm-language-models\/","title":{"rendered":"<strong>CVE-2025-48956: Denial of Service Vulnerability in vLLM Language Models<\/strong>"},"content":{"rendered":"<p><strong>Overview<\/strong><\/p>\n<p>The CVE-2025-48956 vulnerability is a significant security flaw found in vLLM, an inference and serving engine for large language models. The vulnerability can lead to server memory exhaustion, potentially resulting in a system crash or unresponsiveness. This issue affects versions of vLLM from 0.1.0 to before 0.10.1.1 and can be easily exploited by any remote user making it a significant concern for all users of the affected application.<\/p>\n<p><strong>Vulnerability Summary<\/strong><\/p>\n<p>CVE ID: CVE-2025-48956<br \/>\nSeverity: High (CVSS: 7.5)<br \/>\nAttack Vector: Network<br \/>\nPrivileges Required: None<br \/>\nUser Interaction: None<br \/>\nImpact: System Compromise, Potential Data Leakage<\/p>\n<p><strong>Affected Products<\/strong><\/p><div id=\"ameeb-1332365624\" 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>vLLM | 0.1.0 to 0.10.1.0<\/p>\n<p><strong>How the Exploit Works<\/strong><\/p>\n<p>The vulnerability is based on a flaw in the handling of HTTP GET requests by vLLM. When a large header is sent to an HTTP endpoint, the system fails to manage the memory properly, leading to memory exhaustion. This can result in the system becoming unresponsive or crashing entirely. The flaw does not require authentication, allowing any remote user to exploit it.<\/p>\n<p><strong>Conceptual Example Code<\/strong><\/p><div id=\"ameeb-1022518166\" 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>This is a conceptual example of how an HTTP GET request might be sent with an excessively large header, triggering the vulnerability.<\/p>\n<pre><code class=\"\" data-line=\"\">GET \/vulnerable\/endpoint HTTP\/1.1\nHost: target.example.com\nContent-Type: application\/json\nX-Custom-Header: AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA...[continues]<\/code><\/pre>\n<p>In this example, the `X-Custom-Header` field is filled with an excessively large value, causing the server to exhaust its memory trying to process the request.<\/p>\n<p><strong>Mitigation Guidance<\/strong><\/p>\n<p>Users are advised to update to vLLM version 0.10.1.1 or later, which contains a fix for the vulnerability. If unable to update immediately, it is recommended to use a Web Application Firewall (WAF) or Intrusion Detection System (IDS) to mitigate the risks temporarily.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Overview The CVE-2025-48956 vulnerability is a significant security flaw found in vLLM, an inference and serving engine for large language models. The vulnerability can lead to server memory exhaustion, potentially resulting in a system crash or unresponsiveness. This issue affects versions of vLLM from 0.1.0 to before 0.10.1.1 and can be easily exploited by any [&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-86856","post","type-post","status-publish","format-standard","hentry"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/posts\/86856","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=86856"}],"version-history":[{"count":0,"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/posts\/86856\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/media?parent=86856"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/categories?post=86856"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/tags?post=86856"},{"taxonomy":"vendor","embeddable":true,"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/vendor?post=86856"},{"taxonomy":"product","embeddable":true,"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/product?post=86856"},{"taxonomy":"attack_vector","embeddable":true,"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/attack_vector?post=86856"},{"taxonomy":"asset_type","embeddable":true,"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/asset_type?post=86856"},{"taxonomy":"severity","embeddable":true,"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/severity?post=86856"},{"taxonomy":"exploit_status","embeddable":true,"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/exploit_status?post=86856"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}