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Researchers propose skilder framework to limit LLM agent tool access

A new paper on arXiv introduces skilder, a framework designed to restrict tool access for LLM agents by bundling capabilities into role-based packages. The authors argue that current methods—like multi-agent delegation—fail to enforce strict governance, as policies written in prompts are unreliable. Skilder assigns agents minimal roles upfront, then dynamically grants tools and instructions only…

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Key points

  • skilder bundles tools, skills, and instructions into role-based packages to enforce strict access control for LLM agents
  • central MCP server dynamically grants capabilities only when needed, preventing unauthorized tool use or parameter breaches
  • testing across 13 tasks with six models showed zero governance violations but varied task success rates

The researchers tested skilder against flat-context tool selection and multi-agent systems across 13 tasks using six models, with 10 runs each. Results show zero unauthorized tool calls or parameter violations when governance boundaries were enforced. However, task pass rates varied depending on whether models followed the discovery protocol and met response-quality checks. The paper suggests skilder balances security with adaptability by allowing mid-task capability acquisition.

Read the original at arXiv cs.AI · by Michael Stettler, Benjamin Girardet, Jonas Canton, Nicolas Corod primary sourceOpen source ↗
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The headline, key points and digest above were generated by Digest AI's editorial model from the linked sources. Automated summaries can contain errors: the sources are the record. Spotted a mistake? Tell us. Published by Martin K., who runs Digest AI and handles corrections.

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