Simulate adversarial attacks across OWASP Top 10 for Agentic AI. Run them on every release, not once a quarter. Catch the jailbreak before it ships — not after the screenshot goes viral.
Point red teaming at any endpoint, agent, or chatbot. No SDK rewrite, no instrumentation — just an API call away.
Start from OWASP LLM Top 10, NIST AI RMF, or your own custom policy. Choose which vulnerabilities and attack categories matter for your app.
We replay thousands of adversarial probes and score every finding by CVSS. Drill into each failed attack with the exact prompt, output, and remediation guidance.
Run red teams continuously across every AI app you ship. Watch risk shift by app, by category, and over time — so you know exactly where to focus next.
Point at any AI app like Postman. No SDK, no code changes.
Start from OWASP, NIST, or your own policy. Pick which vulnerabilities and attack vectors to assess.
Attackers manipulate agent goals, plans, or decision paths through direct or indirect instruction injection, causing agents to pursue unintended or malicious objectives.
Every vulnerability scored by CVSS, ranked by severity, traceable to the failing probe.
Run red teams continuously across every AI app. Spot which apps, categories, and trends are heading the wrong way.
FinomBefore Confident AI, a single improvement cycle took 10 days — I'd create a task, assign it to an engineer, wait for availability, and go back and forth. Now the same cycle takes three hours, and our product managers can run it themselves.
Confident AI saves us 480+ hours of manual AI evaluation every month — and gives us the data to defend every quality decision in front of engineering, product, and leadership.
Confident AI gave our team one place to turn production failures into datasets, align metrics, and keep regressions out of releases without waiting on custom engineering work.
HumachWe run a lot of large-scale, multi-turn simulations, and Confident AI made it far easier to design scenarios and execute those tests without piecing together external tools.
Thanks to Confident AI, we were able to move to a fine-tuned model and cut our LLM costs by 80%. This opens up whole new use cases now to generate better output with more targeted LLM calls.
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