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Operationalizing Contextual Integrity in Privacy-Conscious Assistants
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Advanced AI assistants combine frontier LLMs and tool access to autonomously perform complex tasks on behalf of users. While the helpfulness of such assistants can increase dramatically with access to user information including emails and documents, this raises privacy concerns about assistants sharing inappropriate information with third parties without user supervision. To steer information-sharing assistants to behave in accordance with privacy expectations, we propose to operationalize contextual integrity (CI), a framework that equates privacy with the appropriate flow of information in a given context. In particular, we design and evaluate a number of strategies to steer assistants' information-sharing actions to be CI compliant. Our evaluation is based on a novel form filling benchmark composed of human annotations of common webform applications, and it reveals that prompting frontier LLMs to perform CI-based reasoning yields strong results.
Forward citations
Cited by 4 Pith papers
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NeuroFilter: Activation-Based Guardrails for Privacy-Conscious LLM Agents
Private-information-extraction intent can be detected by a linear probe on LLM activations, including via a new 'activation velocity' signal for multi-turn attacks.
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CaMeLs Can Use Computers Too: System-level Security for Computer Use Agents
Computer-use agents can run under Dual-LLM isolation with single-shot branching plans, preserving partial utility while blocking instruction injection, but remain open to branch-steering attacks.
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Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning
A Context Reasoner pipeline that cold-starts LLMs on distilled legal reasoning and applies PPO with a rule-based compliance reward improves performance on CI-based legal compliance benchmarks and transfers to general ...
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Position: Contextual Integrity is Inadequately Applied to Language Models
Many prior studies that apply Contextual Integrity to language models omit the theory's core tenets, which can make their privacy conclusions unreliable.
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