Use Case

AI Model Testing

LLMs, RAG pipelines, and agents — red-teamed the way attackers break them.

The challenge

Your model trusts what it reads. Attackers write what it reads.

LLMs, RAG pipelines, and AI agents inherit every flaw of the content and tools you connect them to — poisoned documents, crafted web pages, over-permissioned plugins. One successful injection turns your assistant into their assistant: leaking data, calling tools, and acting on attacker instructions.

Process flow
1 Map the surface models, RAG, tools 2 Attack the model injection, jailbreak 3 Prove & prioritize fix-ready queue
How Foctos helps
  • Prompt-injection battery: direct injections, jailbreak families, and system-prompt extraction — plus indirect attacks smuggled through the content your model retrieves.
  • RAG pipeline abuse: poisoned documents, citation manipulation, sensitive-data leakage through answers, and retrieval paths that bypass access control.
  • Agent overreach: excessive tool permissions, insecure output handling, and data exfiltration through plugins — each proven exploitable before it reaches your team.
Industries that fit
AI builders

SaaS copilots & assistants

Ship model features on every sprint with a red-team battery running against each release.

Enterprise

Support & ops automation

Agents wired into tickets, docs, and internal tools — verified to act only within their mandate.

Regulated

Finance & healthcare AI

Customer data flowing through LLMs and RAG kept provably contained, with audit-ready evidence.

See it on your own stack

Request a demo and watch Foctos AI agents test a live target in real time.

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