CyberAutophagy identifies and exploits weaknesses across AI applications, agents, MCP servers, RAG pipelines, models, tools, memory systems and AI infrastructure through adversarial testing and realistic attack simulation.
Modern AI applications connect models to agents, tools, data, APIs, identities and infrastructure. Testing only the model misses the attack paths that matter.
We follow trust, authority and attack paths across the complete AI system instead of stopping at isolated model vulnerabilities.
What does the AI trust?
Can we influence that trust?
What authority does that trust unlock?
Can weaknesses combine into an attack path?
What's the worst action that chain can perform?
Facilitated AI architecture and threat-modeling engagement using stakeholder interviews, system review, and structured attack-surface analysis—without active exploitation.
Manual adversarial testing, attack chains, exploitation, reporting, and a remediation workshop targeting trust, permissions, agents, tools, data, and infrastructure.
Prepare your SOC to see AI attacks before they become incidents. We assess detection coverage and help build rules, alerts, and investigation signals for threats targeting AI applications, agents, MCP, RAG, and enterprise AI infrastructure.
Retesting after model, prompt, RAG, MCP, or tool changes to catch regressions and keep AI security assurance current as systems evolve.
Tell us what you're building or deploying and we'll help determine the right security assessment or red team approach.