Xuan Huy Dinh@dinhxuanhuy

As enterprises deploy autonomous customer-facing chatbots and agents, they introduce critical security risks: prompt injection, boundary violations, and unauthorized exposure of internal system prompts and tools. Traditional static testing cannot keep pace with dynamic LLM behaviors. AI-Sec RedTeam solves this by delivering an autonomous, end-to-end security and compliance auditing system: 1. Dynamic Probing & Reconnaissance: The system interfaces with live target endpoints, executing adaptive multi-turn reconnaissance and boundary stress-testing. 2. Multi-Agent Reasoning Pipeline: Leveraged on Evolus AI and accelerated by open models (Google Gemma) hosted on AMD ROCm/Token Factory infrastructure, the audit pipeline orchestrates four specialized agents: - ReconAgent: Extracts the target profile, constraints, and observable guardrails. - PlannerAgent: Formulates attack hypotheses mapped against OWASP Top 10 for LLMs. - BoundaryProber: Synthesizes high-impact adversarial probes targeting specific logic gaps. - ExplainJudge: Evaluates the complete interaction transcript against business policies, producing a structured JSON audit verdict with severity ratings and evidence quotes. 3. Automated Remediation: Beyond scoring, ExplainJudge synthesizes concrete, hardened system prompt patches to immediately close detected vulnerabilities. Built with a containerized Python/Streamlit architecture communicating via the Evolus REST API, AI-Sec RedTeam transforms manual red-teaming into a scalable, continuous compliance workflow for modern enterprises on AMD hardware.

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