Use case · AI & ML
Red teaming that meets LLM systems where the real failure modes live.
Prompt injection, tool-use abuse, data exfil via RAG, agent-to-agent compromise, and supply-chain risks across the model stack.
NIST AI RMFEU AI Act (mapping)OWASP LLM Top 10MITRE ATLAS
The problems we see
- OWASP LLM Top 10 isn't enough for agents
- Prompt injection via indirect channels (docs, web, email)
- Data-leakage via RAG context
- Model supply-chain opacity
Our approach
Prompt injection
Direct, indirect, cross-agent, and tool-mediated.
Agent abuse
Tool escalation, arbitrary code paths, goal hijacking.
Data exfil
RAG context leakage, system-prompt extraction, embedding inversion.
Evaluator suite
We hand you an eval suite you can run in CI.
Compliance mapping
NIST AI RMFEU AI Act (mapping)OWASP LLM Top 10MITRE ATLAS
Every engagement produces framework-mapped evidence. Your auditor gets a control-by-control package, not a narrative PDF.
Outcomes
- A threat model grounded in real LLM failure modes.
- CI-ready evals that catch regressions before customers do.
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