Cost-Efficient Privacy Enforcement for Email-Generating LLM Agents under Strategic Information Requests

Key points

• LLM agents now act as delegated communication intermediaries: they read private context, draft messages, and send externally visible emails on behalf of a user.1
• Existing checker-repair defenses can reduce inappropriate disclosure, but they rely on repeated calls to a remote action LLM (the model that drafts and repairs the email) and a remote checker LLM whenever a draft fails validation.1
• The mechanism intercepts an outgoing draft, constructs an information-release contract that specifies what to preserve, generalize, and forbid, rewrites the draft with a local Gemma rewriter, and selectively invokes a final PrivacyChecker.1
• These results support a local-first privacy enforcement design: move repair to a cheaper trusted local layer when possible, but retain the checker as the final semantic gate for high-risk sends.1

Sources (1)

• [1]Cost-Efficient Privacy Enforcement for Email-Generating LLM Agents under Strategic Information Requests
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 09:06 AM

  We propose local contract-conditioned rewriting as a cost-efficient enforcement layer for email-generating agents.

  LLM agents now act as delegated communication intermediaries: they read private context, draft messages, and send externally visible emails on behalf of a user.

Extractive summary: sentences quoted from the sources.