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Before an AI agent goes live in front of customers, someone on your team has to sign it off, often with less evidence than they would like. Our assessment gives them that evidence. It ends with a go/no-go recommendation signed by the security experts who tested your agent, together with a report of what we found, ranked by severity, and how to fix each issue. Request your assessment below.

Our security researchers test your customer-facing AI agent the way an attacker would. We also check for the quality issues that scanners tend to miss, such as invented policies, wrong refusals or advice outside the agent's scope. You receive a vulnerability report ranked by severity, a go/no-go recommendation signed by our experts, and specific fixes for your agent. If we find nothing, we refund you. Request your assessment below.

We planted 17 vulnerabilities in a banking agent and ran the best free scanner against it. It found 2 of them. Six of the 17 were not found by any tool at all. Our security researchers run the longer, multi-turn attacks and the attacks through your agent's tools that free scans leave out. You get a report ranked by severity, a signed go/no-go recommendation and a fix for each finding. Request your assessment below.

When our assessment finds issues, we stay with your team afterwards. We help you separate real risks from noise, decide what to fix first, and work out the right fix for your agent. Every assessment includes a report ranked by severity, a signed go/no-go recommendation and specific remediation guidance. Request your assessment below.

Before BPCE launched its customer-facing banking chatbot, two of our researchers spent several weeks trying to break it. BPCE now runs three assistants, and we keep testing all of them. We can run the same assessment on one of your agents. You get a report ranked by severity, a signed go/no-go recommendation and a fix for each finding. We are an independent team, and everything is built and hosted in Europe. Request your assessment below.

Most of the failures we find in customer-facing assistants are subtle. They tend to fall into four groups: • Sycophancy: the assistant agrees with a false claim to please the user. • Prompt injection: a user gets it to ignore its own instructions. • Data disclosure: a user pulls out confidential or personal information. • Inappropriate content: a user pushes it into biased or off-limits answers. Our researchers test your agent for all four, along with the quality issues scanners tend to miss. You get a report ranked by severity, a signed go/no-go recommendation and a fix for each finding. Request your assessment below.