INTENT-AS-A-TOOL Makes it Easy to Track Agentic Misalignment

Noosaga Placements

• Ai Safetysubfield95%
  The paper directly addresses AI safety by detecting and intervening on agentic misalignment, a core concern in safe deployment of LLM agents.
  We study agentic misalignment, where agents take harmful actions under goal conflicts and pressures.Our results show that INTENT-AS-A-TOOL complements CoT monitoring, expands post-hoc CoT labels into dense trajectories, and identifies critical steps for online intervention.
• AI Safety and Alignmentframework90%
  The paper's approach is a form of AI safety and alignment, as it aims to detect and prevent misaligned agent behavior.
  We study agentic misalignment, where agents take harmful actions under goal conflicts and pressures.Our results show that INTENT-AS-A-TOOL complements CoT monitoring, expands post-hoc CoT labels into dense trajectories, and identifies critical steps for online intervention.
• Natural Language Processingsubfield70%
  The method is applied to LLM agents and leverages chain-of-thought reasoning, which is a core NLP topic, though the focus is on safety rather than language understanding.
  As large language models (LLMs) are deployed as autonomous agents, safety failures increasingly involve consequential actions.Using chain-of-thought (CoT) monitoring, we find that harmful execution is often preceded by intent signals in reasoning.
• Interpretabilityframework60%
  The intent-tool probability serves as an interpretability signal for understanding model reasoning and intent, which aligns with interpretability goals.
  The probability of calling an intent tool provides a judge-free, fine-grained signal of the model's tendency to pursue that behavior.Tracking this signal reveals intriguing reasoning patterns, including post-hoc rationalization.
• Multiagent Systemssubfield50%
  The scenarios involve agents interacting with other entities (e.g., employees) in an email environment, but the focus is on individual agent behavior rather than multi-agent coordination.
  Each scenario places an agent in a sandboxed email environment, where it is given a specific role, a task objective, a time-stamped inbox, and a set of tools for interacting with employees and workplace resources.

Abstract

As large language models (LLMs) are deployed as autonomous agents, safety failures increasingly involve consequential actions. We study agentic misalignment, where agents take harmful actions under goal conflicts and pressures. Using chain-of-thought (CoT) monitoring, we find that harmful execution is often preceded by intent signals in reasoning. However, post-hoc CoT labels are too coarse to show how intent changes during generation. We introduce INTENT-AS-A-TOOL, an approach that adds intent-targeted tools to give the model a dedicated channel for expressing commitment to a target behavior. The probability of calling an intent tool provides a judge-free, fine-grained signal of the model's tendency to pursue that behavior. Our results show that INTENT-AS-A-TOOL complements CoT monitoring, expands post-hoc CoT labels into dense trajectories, and identifies critical steps for online intervention. These findings suggest that action preferences are useful for tracking agentic misalignment during reasoning. Our code and data are accessible: https://github.com/RebeccaZhang22/intent-as-a-tool.

Paper Context