{"id":87041,"date":"2026-06-04T15:18:34","date_gmt":"2026-06-04T15:18:34","guid":{"rendered":""},"modified":"-0001-11-30T00:00:00","modified_gmt":"-0001-11-30T06:00:00","slug":"cve-2025-55559-tensorflow-v2-18-0-vulnerability-leads-to-denial-of-service-attacks","status":"publish","type":"post","link":"https:\/\/www.ameeba.com\/blog\/cve-2025-55559-tensorflow-v2-18-0-vulnerability-leads-to-denial-of-service-attacks\/","title":{"rendered":"<strong>CVE-2025-55559: TensorFlow v2.18.0 Vulnerability Leads to Denial of Service Attacks<\/strong>"},"content":{"rendered":"<p><strong>Overview<\/strong><\/p>\n<p>This report focuses on CVE-2025-55559, a high-severity vulnerability discovered in TensorFlow v2.18.0. This vulnerability, if exploited, can lead to a Denial of Service (DoS) attack, potentially compromising systems or leading to data leakage. It affects all systems utilizing TensorFlow v2.18.0, highlighting the urgent need for mitigation and patching.<\/p>\n<p><strong>Vulnerability Summary<\/strong><\/p>\n<p>CVE ID: CVE-2025-55559<br \/>\nSeverity: High (7.5 CVSS Score)<br \/>\nAttack Vector: Network<br \/>\nPrivileges Required: None<br \/>\nUser Interaction: None<br \/>\nImpact: Potential system compromise or data leakage<\/p>\n<p><strong>Affected Products<\/strong><\/p><div id=\"ameeb-4008052516\" 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>TensorFlow | v2.18.0<\/p>\n<p><strong>How the Exploit Works<\/strong><\/p>\n<p>The vulnerability is triggered when padding is set to &#8216;valid&#8217; in tf.keras.layers.Conv2D within TensorFlow v2.18.0. This incorrect configuration can lead to a buffer overflow condition, causing the system to become unresponsive, leading to a Denial of Service (DoS) situation. Attackers can exploit this vulnerability remotely over a network connection, without requiring any user interaction.<\/p>\n<p><strong>Conceptual Example Code<\/strong><\/p><div id=\"ameeb-999456213\" 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 pseudocode outlines a potential exploitation scenario:<\/p>\n<pre><code class=\"\" data-line=\"\">import tensorflow as tf\n# Create a maliciously configured Conv2D layer\nlayer = tf.keras.layers.Conv2D(64, (3, 3), padding=&#039;valid&#039;)\n# Prepare a large input tensor\ninput = tf.random.uniform((1, 3000, 3000, 3))\n# Apply the malicious layer\noutput = layer(input)<\/code><\/pre>\n<p>In this example, the attacker creates a Conv2D layer with &#8216;valid&#8217; padding and applies this to a large input tensor. This can cause the system to overflow, leading to the Denial of Service (DoS) condition.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Overview This report focuses on CVE-2025-55559, a high-severity vulnerability discovered in TensorFlow v2.18.0. This vulnerability, if exploited, can lead to a Denial of Service (DoS) attack, potentially compromising systems or leading to data leakage. It affects all systems utilizing TensorFlow v2.18.0, highlighting the urgent need for mitigation and patching. Vulnerability Summary CVE ID: CVE-2025-55559 Severity: [&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-87041","post","type-post","status-publish","format-standard","hentry"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/posts\/87041","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=87041"}],"version-history":[{"count":0,"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/posts\/87041\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/media?parent=87041"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/categories?post=87041"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/tags?post=87041"},{"taxonomy":"vendor","embeddable":true,"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/vendor?post=87041"},{"taxonomy":"product","embeddable":true,"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/product?post=87041"},{"taxonomy":"attack_vector","embeddable":true,"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/attack_vector?post=87041"},{"taxonomy":"asset_type","embeddable":true,"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/asset_type?post=87041"},{"taxonomy":"severity","embeddable":true,"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/severity?post=87041"},{"taxonomy":"exploit_status","embeddable":true,"href":"https:\/\/www.ameeba.com\/blog\/wp-json\/wp\/v2\/exploit_status?post=87041"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}