Your LLM applications and AI agents, attacked the way a real adversary would — not benchmarked, broken. Mapped to the OWASP Top 10 for LLM Applications.
Request this engagement ▸Targets, models, agents, and boundaries defined in writing before anything starts.
Understanding architecture, system prompts, tool access, and integration points.
RAG sources, agent permissions, MCP connections, and every external input the model trusts.
Direct and indirect injection, crafted to bypass guardrails the way a real attacker would.
Pushing past a successful jailbreak to see what it actually exposes or unlocks.
Every finding with evidence, OWASP LLM Top 10 mapping, severity, and exact reproduction steps.
Verifying the fixes actually hold against the original attack.
…or any team building on fintech or healthcare data, integrating MCP or third-party tools, or shipping an AI feature that's never been adversarially tested.
Findings are mapped to the OWASP Top 10 for LLM Applications and referenced against MITRE ATLAS — the adversarial framework for AI systems.
No. We test in a controlled way and agree on safe boundaries in advance — including whether testing runs against a staging environment or production, and what's off-limits.
Usually, yes — API access at minimum, and often visibility into system prompts, RAG sources, and agent tool configurations, depending on how deep the engagement goes.
A pentest targets infrastructure and code. AI red teaming targets the model's behavior itself — prompts, retrieval, and agent logic — attack surfaces a standard pentest doesn't cover.
Yes. Every finding is classified against the OWASP Top 10 for LLM Applications and rated Critical, High, Medium, or Low.
Tell us what you're building and what you're worried about. We'll come back with a scope, a timeline, and a quote.
Request engagement ▸