AI red teaming
for LLM apps and agents.
We attack your AI systems the way a real adversary would, with prompt injection, data exfiltration, jailbreaks, and tool abuse. You get findings you can act on, help fixing them, and a retest that proves the fixes work.
Every way in.
Before someone else finds it.
LLM applications fail in ways traditional security testing doesn't cover. Our AI security assessments go after the model, its prompts, the data it can reach, and the tools it can use.
Prompt injection
Direct attacks in user input, and indirect ones hidden in documents, web pages, emails, or tool results that try to override your system's instructions.
Data leakage
Attempts to extract system prompts, secrets, other users' data, or documents a user shouldn't be able to see through retrieval.
Jailbreaks and policy bypass
Techniques that push a model past its safety and business rules, from role-play to slow multi-turn escalation.
Tool and agent misuse
Steering an agent into calling tools it shouldn't, with arguments it shouldn't, or chaining harmless steps into a harmful action.
Unsafe output handling
Model output that reaches browsers, databases, shells, or other systems without validation.
Cost and resource abuse
Inputs designed to trigger runaway loops, runaway token bills, or slowdowns.
Mapped to frameworks
your team already uses.
Test plans draw on the OWASP Top 10 for LLM Applications and MITRE ATLAS, so every finding maps to a category your security team and auditors recognize.
When to red team
- Before launch, while fixes are still cheap
- After big changes: new tools, new data sources, or a new model
- On a regular schedule, as attack techniques evolve
- When a customer, auditor, or regulator asks for evidence of testing
Scope, attack,
fix, retest.
Scope & threat model
We map the system: models, prompts, retrieval sources, tools, permissions, and who can reach it. Then we agree what is in scope and what a serious finding looks like for your business.
Attack
Hands-on adversarial testing by engineers who build agents themselves, backed by automated attack suites for breadth.
Report
Each finding comes with a reproduction, severity, business impact, and a specific fix, not a generic checklist.
Fix & retest
We help implement the fixes, from guardrails and permission changes to prompt and architecture changes, then retest to confirm each one is closed.
What you get.
Evidence, not opinions.
- A threat model of your AI system and its attack surface
- A findings report with reproductions, severity ratings, and fixes
- A plain-language summary for leadership
- A retest report confirming which findings are closed
- Attack cases you can keep running as regression tests
Questions,
answered.
What is AI red teaming?
AI red teaming is adversarial testing of an AI system: people deliberately try to make a model or agent misbehave, leak data, or take harmful actions, so the weaknesses are found and fixed before real attackers find them. It covers the model and everything around it, including prompts, retrieval, tools, and permissions.
What is prompt injection?
Prompt injection is an attack where text controlled by an attacker is treated as instructions by a language model. Direct injection comes through user input; indirect injection hides instructions in content the model reads, such as a web page, email, or document. It is ranked first in the OWASP Top 10 for LLM Applications.
How is AI red teaming different from a penetration test?
A penetration test targets infrastructure and application code. AI red teaming targets behavior: what the model can be talked into saying or doing, what data it can be made to reveal, and what its tools let an attacker reach. Most AI systems need both.
Do you test AI agents, or only chatbots?
Both, with extra depth on agents. When a model can call tools, the biggest risks are in what those tools can do and who can steer them, so we test permissions, tool arguments, and multi-step attack chains.
Can you help fix what you find?
Yes. The same team builds and hardens AI agents, so we can implement fixes alongside your engineers and retest them. See how we build agents.
Will testing affect our production system?
We agree the test environment and limits up front. Where we can, we test a staging copy of your system, and we only test production when you ask us to, within agreed boundaries.
Build it. Break it.
Govern it.
Ready to deploy AI
you can actually trust?
Tell us about your workflow. We'll show you what's possible — and exactly what it takes to keep it secure.