Adversarial Attacks on AI
Adversarial attacks on AI are inputs deliberately crafted to manipulate a model's behavior, causing it to misclassify data, produce unsafe outputs, leak training information, or take actions the developer did not intend.
How Adversarial Attacks Work
Adversarial attacks exploit the way machine learning models process input rather than the way software processes code. Traditional cyber attacks target vulnerabilities in application logic or infrastructure. Adversarial attacks target the model itself, crafting inputs that look normal to a human but produce a specific unintended response from the AI system.
Common categories include evasion attacks that bypass detection or classification, extraction attacks that reveal information about training data or model parameters, and manipulation attacks that trigger a specific unintended action or output. Each maps to a different point in the AI lifecycle.
Why Adversarial Attacks Matter for Enterprise AI
The move from narrow ML models to enterprise deployment of LLMs and agentic AI systems has expanded the adversarial attack surface substantially. Generative and agentic systems accept natural-language inputs, act on external content, and increasingly call tools and APIs. That combination gives attackers more ways to embed adversarial payloads than any prior generation of ML systems.
The categories that matter most for enterprise AI programs today include prompt injection, jailbreaks, data poisoning, and model extraction. Frameworks like MITRE ATLAS catalog these attack types alongside their techniques, giving security teams a shared vocabulary for AI-specific threats.
Adversarial Attacks in AI Governance Programs
A mature AI governance program treats adversarial attack testing as part of pre-deployment validation and ongoing monitoring, not a one-time exercise. AI red teaming and adversarial testing exercise models against known attack categories before production release, and continuous monitoring watches for anomalous inputs and outputs once systems are live.
Related Terms
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