Repeatable workflow for AI security audits and LLM vulnerability assessment

This article outlines a repeatable workflow for auditing AI security, focusing on penetration testing for Model Context Protocol (MCP) servers and LLM agents. It emphasizes treating AI security as an ongoing process rather than a one-off event due to the rapid evolution of AI tools and agent configurations. The proposed workflow involves five stages: asset inventory, static detection using a rule-based system, MCP penetration testing, tool-return injection testing, and credential exposure assessment. AI

IMPACT Provides a structured approach for securing AI agents and their integrations, crucial for enterprise adoption and risk management.

RANK_REASON The article describes a practical workflow and methodology for AI security auditing, which falls under tooling and best practices rather than a novel release or research.

Read on dev.to — MCP tag →

• AI Security Audit
• carbon capture and storage
• LLM Vulnerability Assessment
• MCP
• MCP Penetration Testing

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