Review Adversarial Attack

An adversarial attack is a deliberate attempt to manipulate an AI system's inputs, data, environment, or interfaces so it produces an incorrect, unauthorized, or harmful result.

An attacker looks for weaknesses in what the system sees, trusts, or can access. The attack may target the model directly or the larger application around it.

Ordinary accuracy tests do not establish security under deliberate pressure. Systems need threat modeling, access controls, monitoring, and adversarial testing.

An attacker makes a small, carefully chosen change to an input so an automated classifier assigns the wrong category.

Adversarial attack is not another name for constructive disagreement or red-team review. It describes an attempt to cause failure or misuse.

The input is designed to make the system fail.

* [[adversarial-review|Adversarial Review]] * [[red-teaming|Red Teaming]] * [[prompt-injection|Prompt Injection]] * [[model-poisoning|Model Poisoning]] * [[robustness|Robustness]]

* [NIST AI 100-2 E2025, *Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations*](https://doi.org/10.6028/NIST.AI.100-2e2025) — official taxonomy covering adversarial machine-learning attacks, attacker goals, and mitigations.

* Contrast the attack with defensive [[red-teaming|Red Teaming]].