Break your AI before attackers do
AI apps bring new ways to get hacked. We test chatbots, RAG systems and agents for prompt injection, data leakage, jailbreaks and unsafe tool use, following the OWASP Top 10 for LLM applications, and help you fix what we find.
What you get
LLM red teaming
Manual and automated attacks: prompt injection, jailbreaks and system-prompt extraction.
RAG & data leakage tests
Check whether users can pull data they should never see through the assistant.
Agent permission review
Make sure agents cannot be tricked into dangerous actions with their tools.
AI governance
Policies, risk assessments and controls aligned with ISO 42001 and the EU AI Act.
Step by step
- 1
Threat model
Map what the AI can see and do, and who can talk to it.
- 2
Attack
Structured red-team testing against realistic abuse cases.
- 3
Report
Findings with proof, risk rating and concrete fixes.
- 4
Retest
Verify fixes and add regression tests to your pipeline.
What changes for you
- Prompt injection risks found
- Sensitive data protected
- Safe agent permissions
Tools we use
Questions about ai security & llm red teaming
Is this different from a normal pentest?
Yes. It covers AI-specific risks such as prompt injection and model misuse, on top of the usual web and API testing.
Do you test third-party AI tools we use?
We test how you have integrated and configured them, within the provider's terms of service.
Tell us what you are building, or what is keeping you up at night.
A 30-minute call with an engineer, not a sales script. You will leave with practical next steps even if we never work together.