MAV CYBER / CAPABILITY 01
AI & LLM red teaming
Find the attack paths hiding inside your AI.
Adversarial testing for LLM applications, copilots, and RAG systems. Challenge the boundaries between untrusted content, private data, and model behavior.
Prompt injectionRAG securityModel abuse
What is AI red teaming?
AI red teaming is adversarial testing of an AI system to find ways an attacker could manipulate its behavior, expose sensitive information, or bypass intended controls. MAV tests the application around the model, including retrieval, tools, permissions, and data flows.
What we examine
- Direct and indirect prompt injection across user input, retrieved documents, and external content.
- Sensitive information exposure, cross-user data leakage, and retrieval authorization failures.
- Unsafe output handling, instruction hierarchy failures, and excessive trust in model responses.
- Abuse paths, resource consumption, and the limits of existing filters and safety controls.
When this is the right move
Teams preparing to launch an LLM product, connect private knowledge, expand tool access, or validate a major model or architecture change.
Understand how your AI application can fail under adversarial pressure, then give your team concrete changes to make before the next release.