Engagement Methodology
A structured lifecycle that maps your AI stack, attacks it like a real adversary, and leaves hardened guardrails behind, aligned with OWASP LLM Top 10 and MITRE ATLAS.
Threat Modeling & Scoping
We map your GenAI stack: model endpoints, retrieval pipelines, agents, plugins, and data flows. Together we define use cases, trust boundaries, and the assets that matter most before a single prompt is sent.
Adversarial Red-Teaming
Hands-on attacks against your LLM applications: direct and indirect prompt injection, jailbreaks, prompt extraction, and output manipulation. We chain real exploits to show business impact, not just theory.
Model & Pipeline Review
We review model provenance, weight integrity, fine-tuning data, and retrieval-augmented generation (RAG) controls. Insecure serialization, poisoned datasets, and supply-chain risks in model artifacts are identified and ranked.
Guardrail Validation
We pressure-test input/output filters, safety policies, rate limits, and access controls against OWASP LLM Top 10 and MITRE ATLAS, measuring how well your guardrails resist evasion and enforced behavior.
Reporting & Roadmap
You receive a prioritized report with reproducible proof-of-concepts, severity ratings, and a concrete remediation roadmap covering guardrail rollout, monitoring, and continuous red-teaming.
Attack Surface Coverage
From prompt injection to insecure model weights, we cover the full LLM threat landscape across interaction, retrieval, models, and autonomous agents.