arXiv:2606.09563v1 Announce Type: cross Abstract: As LLMs are deployed as agents, reliable monitoring requires knowing not only what they output, but which instructions are steering their behavior. This is difficult when models infer unintended subgoals, follow contextual cues, or are influenced by prompt injections and hidden objectives. While activation-to-language methods suggest that hidden states can reveal natural-language information, existing approaches are not designed to recover the full set of simultaneous instructions, constraints, prohibitions, and subgoals active in agentic setti
As LLMs are increasingly deployed in agentic capacities, the need for robust monitoring and understanding of their internal motivations becomes critical for safety and reliability.
The ability to 'read' the hidden instructions steering AI agents will be crucial for managing their behavior, preventing unintended actions, and mitigating risks like prompt injection.
This research provides a foundational method for reverse-engineering the effective instruction sets of AI agents, offering new tools for interpretability and control over autonomous systems.
- · AI safety researchers
- · Developers of AI agents
- · Organizations deploying autonomous AI
- · Malicious actors attempting prompt injection
- · Developers of opaque AI systems
Improved monitoring and control over AI agents will enhance safety and reliability.
This capability could lead to more robust and trustworthy autonomous AI systems, accelerating wider adoption.
The ability to audit internal AI instructions may become a regulatory requirement for critical AI deployments.
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Read at arXiv cs.LG