{"id":316413,"date":"2026-05-25T12:00:13","date_gmt":"2026-05-25T11:00:13","guid":{"rendered":"https:\/\/www.transcend.org\/tms\/?p=316413"},"modified":"2026-05-19T09:23:12","modified_gmt":"2026-05-19T08:23:12","slug":"the-ai-threat-landscape-common-attack-vectors","status":"publish","type":"post","link":"https:\/\/www.transcend.org\/tms\/2026\/05\/the-ai-threat-landscape-common-attack-vectors\/","title":{"rendered":"The AI Threat Landscape: Common Attack Vectors"},"content":{"rendered":"<div class=\"guide-content\">\n<p>Understanding the security threats facing AI systems, including but not limited to language models, is essential for developing robust defenses. Modern AI systems introduce attack vectors that differ from traditional software security because their behavior is shaped by data, statistical learning, and opaque internal representations. These characteristics make them powerful but also create unique avenues for adversarial manipulation.<\/p>\n<ul class=\"sans\">\n<li><strong>Traditional software-defined systems<\/strong> (e.g., rule-based engines, expert systems, knowledge graphs) follow deterministic logic and produce predictable outputs given fixed rules.<\/li>\n<li>In contrast, <strong>ML systems<\/strong> (including text, vision, audio, multimodal, RL, and scientific or biological models) learn patterns from data. This makes their outputs probabilistic, their decision processes difficult to inspect, and their reliability dependent on data integrity. If training data are flawed, biased, or tampered with, the resulting models can behave unpredictably or even dangerously.<\/li>\n<\/ul>\n<p>These properties introduce vulnerabilities that threat actors can exploit across <em>all modalities<\/em>. Input-manipulation attacks may appear as adversarial prompts in language models, adversarial patches in vision systems, or corrupted sensor signals in robotic control. Similarly, data poisoning, model tampering, and model or data extraction attacks have well-documented analogs in vision, audio, control system, and scientific\/biomedical ML models. The attack mechanisms differ, but the threat patterns are universal.<\/p>\n<p>This section outlines <strong>six common AI attack vectors and the consequences<\/strong> of successful attacks. While not an exhaustive survey of the adversarial ML domain, these categories provide a practical baseline for understanding how attackers target AI systems and the broader infrastructure that supports them. They apply broadly across architectures, modalities, and operational environments.<\/p>\n<div class=\"row\">\n<div class=\"six columns\">\n<aside class=\"callout\">\n<blockquote>\n<p id=\"want-baseline-security-measure-\" class=\"c6\"><em><strong>Want baseline security measures to protect your model from attacks? <\/strong><\/em><a target=\"_blank\" href=\"https:\/\/www.rand.org\/pubs\/tools\/TLA4174-1\/ai-security\/guide\/baseline-security.html\" class=\"more-link icon-after icon-after-arrow\" >See Baseline Security Controls for Every AI <span class=\"last-word\">Model<\/span><\/a><\/p>\n<\/blockquote>\n<\/aside>\n<\/div>\n<div class=\"six columns\">\n<aside class=\"callout\">\n<blockquote>\n<p id=\"want-tailored-phasespecific-se-\" class=\"c6\"><em><strong>Want tailored, phase-specific security controls and related guidance? <\/strong><\/em><a target=\"_blank\" href=\"https:\/\/www.rand.org\/pubs\/tools\/TLA4174-1\/ai-security\/guide\/securing-the-ai-lifecycle.html\" class=\"more-link icon-after icon-after-arrow\" >See Securing the AI <span class=\"last-word\">Lifecycle<\/span><\/a><\/p>\n<\/blockquote>\n<\/aside>\n<\/div>\n<\/div>\n<h3 id=\"reference-frameworks-for-ai-th-\">Reference Frameworks for AI Threat Modeling<\/h3>\n<p>This guide draws on established adversarial ML and cybersecurity frameworks most relevant to AI security, including<\/p>\n<ul class=\"sans\">\n<li>MITRE ATLAS<a target=\"_blank\" href=\"https:\/\/www.rand.org\/pubs\/tools\/TLA4174-1\/ai-security\/guide\/threat-landscape.html#fn1\" id=\"fnb1\" class=\"footnote-anchor\" ><sup>\u20601<\/sup><\/a>\n<div class=\"footnote-preview sans-small\" aria-hidden=\"true\">\n<div>MITRE, \u201c<a target=\"_blank\" href=\"https:\/\/atlas.mitre.org\/\" >MITRE ATLAS<\/a>.\u201d<\/div>\n<\/div>\n<\/li>\n<li>Open Web Application Security Project (OWASP) Machine Learning Security Top 10<a target=\"_blank\" href=\"https:\/\/www.rand.org\/pubs\/tools\/TLA4174-1\/ai-security\/guide\/threat-landscape.html#fn2\" id=\"fnb2\" class=\"footnote-anchor\" ><sup>\u20602<\/sup><\/a>\n<div class=\"footnote-preview sans-small\" aria-hidden=\"true\">\n<div>OWASP, \u201c<a target=\"_blank\" href=\"https:\/\/owasp.org\/www-project-machine-learning-security-top-10\/\" >Machine Learning Security Top 10<\/a>.\u201d<\/div>\n<\/div>\n<\/li>\n<li>Cloud Security Alliance MAESTRO<a target=\"_blank\" href=\"https:\/\/www.rand.org\/pubs\/tools\/TLA4174-1\/ai-security\/guide\/threat-landscape.html#fn3\" id=\"fnb3\" class=\"footnote-anchor\" ><sup>\u20603<\/sup><\/a>\n<div class=\"footnote-preview sans-small\" aria-hidden=\"true\">\n<div>Cloud Security Alliance, \u201c<a target=\"_blank\" href=\"https:\/\/cloudsecurityalliance.org\/blog\/2025\/02\/06\/agentic-ai-threat-modeling-framework-maestro\" >MAESTRO Framework<\/a>.\u201d<\/div>\n<\/div>\n<\/li>\n<li>NIST AI Risk Management Framework.<a target=\"_blank\" href=\"https:\/\/www.rand.org\/pubs\/tools\/TLA4174-1\/ai-security\/guide\/threat-landscape.html#fn4\" id=\"fnb4\" class=\"footnote-anchor\" ><sup>\u20604<\/sup><\/a>\n<div class=\"footnote-preview sans-small\" aria-hidden=\"true\">\n<div>NIST, \u201c<a target=\"_blank\" href=\"https:\/\/www.nist.gov\/itl\/ai-risk-management-framework\" >AI Risk Management Framework<\/a>.\u201d<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<p>These frameworks support systematic threat modeling and help practitioners align AI-specific risks with confidentiality, integrity, and availability objectives in existing enterprise security programs.<\/p>\n<h2 id=\"applying-taxonomies-to-a-varie-\">Applying Taxonomies to a Variety of AI Modalities<\/h2>\n<p>This guide applies the <strong>Berryville Institute of Machine Learning\u2019s (BIML\u2019s) taxonomy<\/strong> as a foundation for organizing adversarial threats.<a target=\"_blank\" href=\"https:\/\/www.rand.org\/pubs\/tools\/TLA4174-1\/ai-security\/guide\/threat-landscape.html#fn5\" id=\"fnb5\" class=\"footnote-anchor\" ><sup>\u20605<\/sup><\/a><\/p>\n<div class=\"footnote-preview sans-small\" aria-hidden=\"true\">\n<div>Shepardson et al., \u201c<a target=\"_blank\" href=\"https:\/\/berryvilleiml.com\/taxonomy\/\" >A Taxonomy of ML Attacks<\/a>.\u201d<\/div>\n<\/div>\n<p>Although BIML\u2019s framework was originally formulated with general ML systems in mind, its attack patterns\u2014input manipulation, data poisoning, model tampering, inversion, data extraction, and model extraction\u2014map cleanly across AI modalities.<\/p>\n<p>Whether the model processes language, images, genomes, chemical structures, audio, or sensor data, the same underlying vulnerabilities apply:<\/p>\n<ul>\n<li>Untrusted inputs can steer models off course.<\/li>\n<li>Poisoned data can distort learning.<\/li>\n<li>Models can be tampered with during development or deployment.<\/li>\n<li>Sensitive data or intellectual property can be extracted through unintended memorization or uncontrolled interfaces.<\/li>\n<\/ul>\n<p>By grounding the taxonomy in these cross-modal patterns, organizations can map AI-specific threats to their operational context and integrate defenses into existing risk, compliance, and governance frameworks.<\/p>\n<aside class=\"blue10\">\n<h2 id=\"six-categories-of-adversarial--\">Six Categories of Adversarial Threats to AI Models<\/h2>\n<ul class=\"sans\">\n<li><strong>Input Manipulation<\/strong>: Attack on an AI model during runtime in which an attacker\u2019s \u201cinput\u201d or prompt produces a different output than the model\u2019s creators intended. <a target=\"_blank\" href=\"https:\/\/www.rand.org\/pubs\/tools\/TLA4174-1\/ai-security\/guide\/threat-landscape\/input-manipulation.html\" >Learn more<\/a><\/li>\n<li><strong>(Training) Data Manipulation<\/strong>: Attack on an AI model via the training process in which an attack modifies the data used to train a model to impair or influence the model\u2019s behavior. <a target=\"_blank\" href=\"https:\/\/www.rand.org\/pubs\/tools\/TLA4174-1\/ai-security\/guide\/threat-landscape\/training-data-manipulation.html\" >Learn more<\/a><\/li>\n<li><strong>Model Manipulation<\/strong>: Attack on the AI model itself to cause the model to malfunction or produce potentially incorrect outputs. <a target=\"_blank\" href=\"https:\/\/www.rand.org\/pubs\/tools\/TLA4174-1\/ai-security\/guide\/threat-landscape\/model-manipulation.html\" >Learn more<\/a><\/li>\n<li><strong>Input Extraction<\/strong>: Attack to access or recover \u201cprivate\u201d data inputs used in the design and training of a model from public outputs. Also called \u201cmodel inversion.\u201d <a target=\"_blank\" href=\"https:\/\/www.rand.org\/pubs\/tools\/TLA4174-1\/ai-security\/guide\/threat-landscape\/input-extraction.html\" >Learn more<\/a><\/li>\n<li><strong>(Training) Data Extraction<\/strong>: Attack to extract data (including both public datasets and proprietary or sensitive data) that a developer used to train the AI model. <a target=\"_blank\" href=\"https:\/\/www.rand.org\/pubs\/tools\/TLA4174-1\/ai-security\/guide\/threat-landscape\/training-data-extraction.html\" >Learn more<\/a><\/li>\n<li><strong>Model Extraction<\/strong>: Attack to extract the model itself to copy or steal a model. <a target=\"_blank\" href=\"https:\/\/www.rand.org\/pubs\/tools\/TLA4174-1\/ai-security\/guide\/threat-landscape\/model-extraction.html\" >Learn more<\/a><\/li>\n<\/ul>\n<\/aside>\n<h3 id=\"difficulty-scoring-criteria-\">Difficulty Scoring Criteria:<\/h3>\n<p>For each category, we describe potential adversarial threats to AI systems and potential consequences of a successful attack. We score both the <strong>difficulty<\/strong> of executing the attack and the <strong>consequence<\/strong> if the attack succeeds. Scores follow a scale of low, moderate, and high, with some threats falling between two scores (e.g., moderate-high). Using ranges allows us to more accurately capture the variation in complexity and impact across different types of attacks.<\/p>\n<div class=\"row\">\n<div class=\"four columns\">\n<blockquote>\n<h3 id=\"low-\" class=\"c4 low\">Low<\/h3>\n<p class=\"sans\">Requires minimal technical skill, low resource cost, and access to readily available tools or data.<\/p>\n<\/blockquote>\n<\/div>\n<blockquote>\n<div class=\"four columns\">\n<h3 id=\"moderate-\" class=\"c4 moderate\">Moderate<\/h3>\n<p class=\"sans\">Requires technical expertise, significant query or compute, some model knowledge or reverse engineering. Defenses may be present.<\/p>\n<\/div>\n<\/blockquote>\n<div class=\"four columns\">\n<blockquote>\n<h2 id=\"high-\" class=\"c4 high\">High<\/h2>\n<p class=\"sans\">Requires advanced skills, deep model access (e.g., weights), specialized tools, and circumvention of strong defenses (e.g., output filtering).<\/p>\n<\/blockquote>\n<\/div>\n<\/div>\n<h3 id=\"consequence-scoring-criteria-\">Consequence Scoring Criteria:<\/h3>\n<div class=\"row\">\n<div class=\"four columns\">\n<blockquote>\n<h3 id=\"low-\" class=\"c4 low\">Low<\/h3>\n<p class=\"sans\">Minimal security impact with no exposure of data, no change in model behavior, minimal user impact, and no regulatory or business risk.<\/p>\n<\/blockquote>\n<\/div>\n<blockquote>\n<div class=\"four columns\">\n<h3 id=\"moderate-\" class=\"c4 moderate\">Moderate<\/h3>\n<p class=\"sans\">Noticeable security impact, with partial exposure of data, temporary or limited model misbehavior, erosion of user trust, and possible reputational damage or compliance obligations.<\/p>\n<\/div>\n<\/blockquote>\n<div class=\"four columns\">\n<blockquote>\n<h2 id=\"high-\" class=\"c4 high\">High<\/h2>\n<p class=\"sans\">Severe security impact, with exposure of personally identifiable information or sensitive data, persistent or targeted model misuse, regulatory violations, loss of operational control or compromise of system integrity, or enablement of further attacks.<\/p>\n<\/blockquote>\n<\/div>\n<\/div>\n<p>These criteria are informed by established risk management frameworks, allowing for consistent evaluation of threats across diverse operational contexts and making it easier to integrate AI-specific risks into existing security and compliance programs.<\/p>\n<\/div>\n<section class=\"notes-refs\">\n<h2 id=\"notes-\" class=\"c5\">Notes:<\/h2>\n<ol id=\"footnotes\" class=\"footnotes\">\n<li id=\"fn1\">MITRE, \u201c<a target=\"_blank\" href=\"https:\/\/atlas.mitre.org\/\" >MITRE ATLAS<\/a>.\u201d<a target=\"_blank\" href=\"https:\/\/www.rand.org\/pubs\/tools\/TLA4174-1\/ai-security\/guide\/threat-landscape.html#fnb1\" ><span class=\"access\"> Return to content<\/span>\u2060\u2934<\/a><\/li>\n<li id=\"fn2\">OWASP, \u201c<a target=\"_blank\" href=\"https:\/\/owasp.org\/www-project-machine-learning-security-top-10\/\" >Machine Learning Security Top 10<\/a>.\u201d<a target=\"_blank\" href=\"https:\/\/www.rand.org\/pubs\/tools\/TLA4174-1\/ai-security\/guide\/threat-landscape.html#fnb2\" ><span class=\"access\"> Return to content<\/span>\u2060\u2934<\/a><\/li>\n<li id=\"fn3\">Cloud Security Alliance, \u201c<a target=\"_blank\" href=\"https:\/\/cloudsecurityalliance.org\/blog\/2025\/02\/06\/agentic-ai-threat-modeling-framework-maestro\" >MAESTRO Framework<\/a>.\u201d<a target=\"_blank\" href=\"https:\/\/www.rand.org\/pubs\/tools\/TLA4174-1\/ai-security\/guide\/threat-landscape.html#fnb3\" ><span class=\"access\"> Return to content<\/span>\u2060\u2934<\/a><\/li>\n<li id=\"fn4\">NIST, \u201c<a target=\"_blank\" href=\"https:\/\/www.nist.gov\/itl\/ai-risk-management-framework\" >AI Risk Management Framework<\/a>.\u201d<a target=\"_blank\" href=\"https:\/\/www.rand.org\/pubs\/tools\/TLA4174-1\/ai-security\/guide\/threat-landscape.html#fnb4\" ><span class=\"access\"> Return to content<\/span>\u2060\u2934<\/a><\/li>\n<li id=\"fn5\">Shepardson et al., \u201c<a target=\"_blank\" href=\"https:\/\/berryvilleiml.com\/taxonomy\/\" >A Taxonomy of ML Attacks<\/a>.\u201d<a target=\"_blank\" href=\"https:\/\/www.rand.org\/pubs\/tools\/TLA4174-1\/ai-security\/guide\/threat-landscape.html#fnb5\" ><span class=\"access\"> Return to content<\/span>\u2060\u2934<\/a><\/li>\n<\/ol>\n<p>_____________________________________________<\/p>\n<p style=\"padding-left: 40px;\"><em>RAND is a nonprofit, nonpartisan research organization that provides leaders with the information they need to make evidence-based decisions.<\/em><\/p>\n<p><a target=\"_blank\" href=\"https:\/\/www.rand.org\/pubs\/tools\/TLA4174-1\/ai-security\/guide\/threat-landscape.html\" >Go to Original &#8211; rand.org<\/a><\/p>\n<\/section>\n","protected":false},"excerpt":{"rendered":"<p>Modern AI systems introduce attack vectors that differ from traditional software security because their behavior is shaped by data, statistical learning, and opaque internal representations. These characteristics make them powerful but also create unique avenues for adversarial manipulation.<\/p>\n","protected":false},"author":4,"featured_media":285041,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3078],"tags":[1733,4032,254],"class_list":["post-316413","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence-ai","tag-artificial-intelligence-ai","tag-emotion-vectors","tag-security"],"_links":{"self":[{"href":"https:\/\/www.transcend.org\/tms\/wp-json\/wp\/v2\/posts\/316413","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.transcend.org\/tms\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.transcend.org\/tms\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.transcend.org\/tms\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/www.transcend.org\/tms\/wp-json\/wp\/v2\/comments?post=316413"}],"version-history":[{"count":1,"href":"https:\/\/www.transcend.org\/tms\/wp-json\/wp\/v2\/posts\/316413\/revisions"}],"predecessor-version":[{"id":316415,"href":"https:\/\/www.transcend.org\/tms\/wp-json\/wp\/v2\/posts\/316413\/revisions\/316415"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.transcend.org\/tms\/wp-json\/wp\/v2\/media\/285041"}],"wp:attachment":[{"href":"https:\/\/www.transcend.org\/tms\/wp-json\/wp\/v2\/media?parent=316413"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.transcend.org\/tms\/wp-json\/wp\/v2\/categories?post=316413"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.transcend.org\/tms\/wp-json\/wp\/v2\/tags?post=316413"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}