Prompt Injection Explained as a Role Confusion Problem in LLMs
Summary
This paper presents a theory of prompt injection attacks on LLMs, arguing that the root cause is a fundamental flaw in how models perceive roles — they cannot distinguish between their own thoughts and injected content. The authors demonstrate that LLMs identify roles by writing style rather than explicit tags, and exploit this with a technique called CoT Forgery, where fake reasoning is injected that models mistake for their own thoughts. The work connects prompt injection to mechanistic interpretability results, predicts when attacks succeed, and proposes a new research agenda for a "science of roles" in LLMs.
Source
bskyPrompt Injection Explained as a Role Confusion Problem in LLMsrole-confusion.github.ioKey quotes
· 3 pulledWe show prompt injections are driven by a flaw in how LLMs perceive roles.
LLMs can't tell who's speaking. We show they identify roles by writing style, not tags.
We exploit this with CoT Forgery, injecting fake reasoning that models mistake for their own thoughts.
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