PentStark
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
Talk through your scope
Service boundary

What’s covered

Prompt injection, data leakage, model abuse, agent exploitation.

5 coverage areas
  1. 01

    Prompt injection

    Direct, indirect (web, doc, email), and cross-agent injection.

  2. 02

    Tool / function-call abuse

    Escalation via agent tools, arbitrary code execution, external side-effects.

  3. 03

    Data leakage

    Training-data extraction, RAG context leakage, PII regurgitation.

  4. 04

    Model supply chain

    Model provenance, fine-tune poisoning, dependency risk.

  5. 05

    Multi-agent systems

    Agent-to-agent trust abuse, orchestrator compromise, goal hijacking.

Handoff

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
Intended result

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.
Delivery model

Methodology and service timing

Methodology references

  • LLM
    OWASP LLM Top 10MITRE ATLASNIST AI RMF

Timelines and SLAs

Engagement
2–4 weeks typical
Critical finding alert
Same-day
Questions

AI / LLM Security 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.
Next step

See how AI / LLM Security fits your scope.

Prompt injection, data leakage, model abuse, agent exploitation.

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