Research · curated 11 Aug 2026
Your Agentic LLMs Secretly Encode Indirect Prompt-Injection Exposure in Hidden States
First reported · updated · 3 reports arxiv.org
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Why it matters
Probing hidden states for indirect prompt-injection exposure offers defenders a potential internal-signal detection layer for agentic LLMs, where malicious instructions hidden in retrieved content (emails, code, web pages) can hijack agent actions.
Research described under the title 'Your Agentic LLMs Secretly Encode Indirect Prompt-Injection Exposure in Hidden States' argues that an agentic LLM's internal hidden-state representations encode a signal of whether the model has been exposed to indirect prompt injection, and that this signal can be probed for detection. Related artifacts referenced include an IPI-exposure-signal code repository and rule-based/monitor detection work such as AgentWatcher.