Contextual Integrity in LLMs via Reasoning and Reinforcement Learning

Guangchen Lan, Huseyin A Inan, Sahar Abdelnabi, Janardhan Kulkarni, Lukas Wutschitz, Reza Shokri, Christopher Brinton, Robert Sim

Keywords: Reinforcement Learning, LLM Reasoning, Contextual Integrity

TL;DR: A reinforcement-learning post-training framework teaches LLM assistants to reason about contextual integrity, slashing inappropriate information disclosure while helping users complete their tasks.

Abstract:

As the era of autonomous agents making decisions on behalf of users unfolds, ensuring contextual integrity (CI) -- what is the appropriate information to share while carrying out a certain task -- becomes a central question to the field.
We posit that CI demands a form of reasoning where the agent needs to reason about the context in which it is operating.
To test this, we first prompt LLMs to reason explicitly about CI when deciding what information to disclose.
We then extend this approach by developing a reinforcement learning (RL) framework that further instills in models the reasoning necessary to achieve CI.
Using a synthetic, automatically created, dataset of only $\sim700$ examples but with diverse contexts and information disclosure norms, we show that our method substantially reduces inappropriate information disclosure while maintaining task performance across multiple model sizes and families.
Importantly, improvements transfer from this synthetic dataset to established CI benchmarks such as PrivacyLens that has human annotations and evaluates privacy leakage of AI assistants in actions and tool calls.

Primary Area: Social and economic aspects of machine learning (e.g., fairness, interpretability, human-AI interaction, privacy, safety, strategic behavior)

Submission Number: 15201