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.
Common in
Related research
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