AI security services: LLM application testing, threat modeling and governance

A standard test checks authentication, authorization, injection and logic. An LLM test adds the model as an attack surface: direct and indirect prompt injection, insecure handling of model output by downstream code, data leakage through retrieval, excessive agency in agents, and abuse of tool or MCP permissions. Both are needed, so the work is usually scoped together with penetration testing of the surrounding application.

Penetration testing looks for exploitable security flaws in a defined application: what an attacker can read, change or trigger. AI red teaming is broader and more open-ended. It probes model behavior for harmful, biased or policy-violating output as well as security failures. Teams shipping an LLM feature usually start with a penetration test, then add red teaming when the model's behavior is itself a risk.

Yes. The vendor secures the model and its hosting. Your application decides what the model can see and do: the system prompt, the documents it retrieves, the tools it can call and how its output is used. Most findings sit in that layer, and no vendor assessment covers it. Our threat model for LLM applications sets out where those boundaries fall.

Testing is planned to avoid it. Work normally runs against a staging environment with production-like data flows and test accounts. Where production testing is unavoidable, the rules of engagement set limits on volume, on actions an agent may trigger and on data that may be retrieved. Anything that could send messages, move money or change records is tested with those actions stubbed or sandboxed.

Findings are reported against the OWASP Top 10 for LLM Applications, which names the main risk classes, and MITRE ATLAS, which catalogs adversary techniques against AI systems. Governance work aligns with the NIST AI Risk Management Framework and with ISO/IEC 42001, the management system standard for AI. We support readiness against these. We do not certify.