Prompt Injection Attacks
Overview
Prompt injection attacks exploit the fact that language models may treat instructions embedded in untrusted text, images, tool outputs, or stored memory as authoritative. Researchers test attacks in settings such as web browsing, coding, peer review, and computer use, using adaptive payloads, visual or other hidden carriers, and multi-turn scenarios. Defenses range from input detection and adversarial training to provenance tracking, restricted tool permissions, and deterministic checks outside the model. A central challenge is resisting attacks that change across tasks and interfaces without blocking legitimate work; model refusals alone are often not a dependable security boundary.
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