Service · AI / LLM Security
Real red teaming for LLM apps, agents, and RAG pipelines.
LLM red teaming that goes past jailbreak prompts - we test prompt injection via indirect channels, tool-use abuse, data poisoning, model exfiltration, and multi-agent compromise.
OWASP LLM Top 10MITRE ATLASNIST AI RMF
What's covered
Prompt injection
Direct, indirect (web, doc, email), and cross-agent injection.
Tool / function-call abuse
Escalation via agent tools, arbitrary code execution, external side-effects.
Data leakage
Training-data extraction, RAG context leakage, PII regurgitation.
Model supply chain
Model provenance, fine-tune poisoning, dependency risk.
Multi-agent systems
Agent-to-agent trust abuse, orchestrator compromise, goal hijacking.
Deliverables
- Attack library mapped to OWASP LLM Top 10 and MITRE ATLAS
- Exploit PoCs with reproducible payloads
- Guardrail recommendations (input / output / tool-use / human-in-the-loop)
- Evaluator suite you can re-run in CI
Outcomes
- A threat model for your AI stack that maps to real failure modes, not hype.
- A CI-ready evaluator that catches regressions before customers do.
FAQ
We use a third-party model - is this relevant?
Yes. Most real risk lives in the app, the tool-use surface, and the data boundary - not the model weights.
Do you test agents?
Yes - agent frameworks are our most common engagement in 2026.
Talk to an operator
Your next finding is one scoping call away.
Thirty minutes with a real operator tells us what you need and what we can deliver. No BDR handoff, no sales engineer theater. The person you talk to is the person who scopes the work.
● Responses in < 1 business day