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An adversarial attack that crafts input designed to cause a deployed model to produce wrong output while appearing benign to a human reviewer. Includes adversarial examples for image classifiers, perturbed text for spam filters, and jailbreak prompts for LLM content filters. Named in the NIST AI 100-2 e2025 taxonomy.
Evasion is the most-studied adversarial-ML attack category. AI Security Engineers measure model robustness with adversarial test suites; AI Red Team Engineers design the attacks.
An adversarial attack that crafts input designed to cause a deployed model to produce wrong output while appearing benign to a human reviewer. Includes adversarial examples for image classifiers, perturbed text for spam filters, and jailbreak prompts for LLM content filters. Named in the NIST AI 100-2 e2025 taxonomy.
Evasion is the most-studied adversarial-ML attack category. AI Security Engineers measure model robustness with adversarial test suites; AI Red Team Engineers design the attacks.
Definitions are original explanations written for career development purposes. For authoritative technical definitions, refer to NIST, ISO, or the relevant standards body.
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