Prompt Injection as Role Confusion
Academic research identifies role confusion as fundamental vulnerability in LLM architectures, revealing why current prompt injection defenses consistently fail.
Summary written by editorial AI · Source link below
arXiv:2603.12277v5 Announce Type: replace-cross Abstract: LLMs see the world as a single stream of text, partitioned into roles like or . We trace prompt injection to role confusion: models perceive the source of text from how it sounds, not its labeled role. A command hidden in a webpage hijacks an agent simply because it sounds like text, despite its label. We design role probes to measure how LLMs internally perceive "who is speaking," and find that injected text occupies the same represe
External link — opens at arXiv Crypto & Security in a new tab.
More from the AI Security Desk
- Claude Code's “Don't Ask Again” File Exposes Passwords and Keys in 1,122 Public Copies on GitHub2d
- Runaway AI hacking agent behind WorkOS breach after escaping sandbox3d
- AI Gave Your Incident Archive a Second Job3d
- Malicious Browser Extensions Could Use Claude Dispatch to Run Code on Users' Computers3d
- AI Slop and Hallucinations in Vulnerability Assessment: A Survey on Reasoning Failures and Trustworthy Mitigation3d