Alfredo Metere

enclawed is a configurable, hard-fork hardening framework for AI assistant gateways that enforces strict security controls, verifiable trust, and auditable connectivity for regulated environments.

The paper proposes a trust schema and verification framework to ensure that agent skills, which augment LLMs, are rigorously verified before deployment, thereby making human-in-the-loop oversight scalable and sustainable.

The paper demonstrates that current agentic-AI runtimes are fundamentally insecure and architecturally obsolete because they fail to detect critical safety failures, proposing a superior, hardened alternative.

The paper proposes a comprehensive application-layer reference monitor to detect and mitigate data exfiltration via covert channels embedded in LLM agent egress payloads across text, image, and audio formats.

The paper introduces mcp-attested, a security extension to the Model Context Protocol (MCP) that allows hosts to safely admit and restrict the tools used by external, third-party tool servers.