AI Red Teaming
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We attack your LLM application or AI agent the way a real adversary would — prompt injection, tool misuse, jailbreaks, RAG poisoning, data exfiltration — in a sharp, time-boxed engagement, and hand you a prioritized report you can act on.
Finds the exploitable gaps in your AI before an attacker does — preventing data leaks, unauthorized agent actions, and the compliance exposure that follows a public breach.
What we test
We work across the surfaces where untrusted input meets model capability:
- Prompt injection — direct and indirect (poisoned documents, tool output, retrieved context).
- Tool & function-call abuse — coercing the model into unintended actions and privilege escalation through its tools.
- Jailbreaks & guardrail bypass — defeating safety and policy controls.
- RAG / context poisoning — corrupting retrieval to steer answers or exfiltrate data.
- Sensitive-data leakage — system-prompt extraction, training-data and secret disclosure, exfiltration paths.
- Output handling — downstream injection where model output is trusted by another system.
How it runs
Breadth first, then depth. An automated probe suite — the same engine behind Meridian, our autonomous offensive-research pipeline — sweeps the known attack classes for coverage. Then senior manual testing escalates the interesting signals into chained, business-logic-aware exploits a scanner never finds. Roughly 80% automated reach, 20% human judgment where it counts.
What you get
- A prioritized findings report — each issue with severity, reproduction steps, real-world impact, and concrete remediation.
- An executive summary that a non-specialist stakeholder can act on.
- A re-test on your fixes to confirm they hold.
Scope & engagement
Typically 5–10 business days, fully remote. Rules of engagement are agreed in writing before anything is touched. We assess and report — we don't embed for months. The deliverable is senior judgment, not a seat.
We build the systems we test from
We design and operate autonomous offensive- and agent-security research (Meridian), which is exactly where we learned how automated attack pipelines prioritize and break. For a concrete look at how we think about injection, read our case study on hardening our own AI receptionist against prompt injection. Our evaluation methodologies have been independently validated — Assay confirmed our seal-epd-llm defenses achieve 93.3% injection-blocking across distinct model architectures, proving defense effectiveness is model-independent.
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