Urgent.News

What's breaking now, across thousands of outlets.

AI

CIPL: A Channel-Aware Framework for Recoverable Privacy Leakage in LLM Agents

Privacy leakage in LLM agents is commonly evaluated within individual components such as memory, retrieval, or tool-use pipelines, which makes it difficult to distinguish internal exposure from information that an external observer can actually recover. We present CIPL (Channel Inversion for Privacy Leakage), a channel-aware evaluation framework for black-box privacy leakage in LLM agents. CIPL…

We haven't written up this one. arXiv cs.AI has the full story — the link below goes straight to it.

Read the original at arxiv.org →

More in AI

A one-page brief gives an AI agent enough context to stay useful

A focused brief keeps AI work on track An AI agent can lose the point when it receives too much background or unclear priorities.

  • One-page brief helps AI stay relevant with concise context
  • Precise instructions and clear parameters prevent AI from generic or incorrect responses

More from Friday 18 September →