Redbot Security's AI & LLM Security Testing Service is a human-led adversarial testing engagement targeting AI models, large language models (LLMs), and their supporting infrastructure. The service is performed by U.S.-based senior engineers and is not outsourced or crowdsourced. The engagement follows a four-phase methodology: Phase 1 – Threat Modeling & Architecture Review: Maps the AI ecosystem including models, data stores, vector databases, APIs, and agentic components to identify exploitable trust boundaries and input dependencies. Phase 2 – Adversarial Testing Simulation: Executes controlled attacks such as prompt injection, retrieval poisoning, function-chain manipulation, data exfiltration, and context corruption, with each exploit validated for impact and repeatability. Phase 3 – Control Validation & Hardening: Works with the client's technical team to strengthen defenses, implement content filtering, and validate mitigations through adversarial re-testing. Phase 4 – Reporting & Attestation: Delivers a risk package including technical findings, exploit transcripts, compliance crosswalks, and executive summaries. Vulnerabilities tested include prompt injection, RAG poisoning, tool and API abuse, context leakage, data exfiltration, and model misalignment. Deliverables include an executive summary, exploit proofs with full attack transcripts, compliance mapping to NIST AI RMF, OWASP LLM Top 10, and MITRE ATLAS, a hardening playbook with validation retesting, and an optional attestation report for audit and governance purposes.