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 representational space as the trusted role it imitates. We demonstrate this with CoT Forgery, a zero-shot attac
The rapid deployment of AI agents in various applications brings immediate attention to fundamental security vulnerabilities like prompt injection, making this research highly relevant.
This research identifies a core mechanism behind prompt injection (role confusion), offering a foundational understanding necessary for developing robust defenses and ensuring the trustworthiness of AI agents.
The understanding of prompt injection shifts from a purely input-based problem to one rooted in an AI's internal perception of information source, necessitating new detection and prevention strategies.
- · AI security researchers
- · Developers of secure AI agent platforms
- · Enterprises deploying AI agents
- · Organizations with vulnerable AI agents
- · Attackers relying on current prompt injection techniques
Increased investment in AI interpretability and secure prompt engineering techniques.
Development of new AI architectures that inherently differentiate trusted commands from user input, reducing 'role confusion'.
Enhanced AI agent security could accelerate their adoption in sensitive white-collar workflows, potentially leading to more significant automation impacts.
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Read at arXiv cs.AI