Shannon Pentest Agent Integration: Red-Team AI Attack Patterns Mapped to OWASP LLM Top 10

A new red-team agent, codenamed 'Shannon,' is being integrated into a security testing framework, with its attack patterns explicitly mapped to the OWASP Top 10 for Large Language Model applications. This integration positions Shannon as a specialized AI adversary designed to probe and exploit vulnerabilities in LLM systems. The development tasks involve creating a dedicated adapter for the agent, configuring communication via the Model Context Protocol (MCP), and building test scenarios where Shannon functions as the primary attacker.

The integration architecture reveals a direct pipeline from the main testing engine ('Gauntlet') through a Shannon-specific adapter. This adapter communicates via the MCP protocol to a Shannon endpoint, which then deploys attack payloads with the ultimate goal of flag capture or vulnerability demonstration. The explicit reference to the OWASP LLM Top 10 suggests a focused effort to automate testing against recognized threat categories like prompt injection, data leakage, and insecure output handling. The project links to the 'Shannon Auth Orchestrator' documentation, indicating it may be part of a broader security orchestration platform.

This development signals a move towards more sophisticated, automated adversarial testing for AI systems. By formalizing the connection between a pentest agent and a standardized vulnerability framework, the project could enable more systematic and repeatable security assessments for organizations deploying LLMs. The work, tracked as a GitHub issue, represents an active engineering effort to harden AI applications against emerging attack vectors by simulating them directly.