Red Team Every Release. Not Every Quarter.

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.

HOW IT WORKS

The red team that fits in your governance stack.

1. Connect your AI app in minutes.
   Point red teaming at any endpoint, agent, or chatbot. No SDK rewrite, no instrumentation — just an API call away.

2. Pick the security framework that fits.
   Start from OWASP LLM Top 10, NIST AI RMF, or your own custom policy. Choose which vulnerabilities and attack categories matter for your app.

3. Get a clear risk assessment.
   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.

4. See where risk is concentrating across your portfolio.
   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.

Connect Any Endpoint

Point at any AI app like Postman. No SDK, no code changes.

POST https://api.your-app.com/v1/chat

ParamsHeadersBodyAuthJSON

{
  "model": "gpt-4o",
  "messages": [
    {
      "role": "user",
      "content": "How do I dispute a charge?"
    }
  ]
}

Response 200 OK 412 ms 1.2 KB

{
  "id": "chatcmpl-9f2a…",
  "output": "To dispute a charge, open…",
  "latency_ms": 412
}

Select a Security Framework

Start from OWASP, NIST, or your own policy. Pick which vulnerabilities and attack vectors to assess.

OWASP Top 10 for Agentic Applications 2026

A comprehensive list of the most critical security risks associated with agentic AI applications.

Risk Categories

• Automations
  • ASI01:2026 Agent Goal Hijack
    Attackers manipulate agent goals, plans, or decision paths through direct or indirect instruction injection, causing agents to pursue unintended or malicious objectives.
  • ASI02:2026 Tool Misuse & Exploitation
    Agents misuse or abuse tools through unsafe composition, recursion, or excessive execution, causing harmful side effects despite valid permissions.
  • ASI03:2026 Agent Identity & Privilege Abuse
    Abuse of delegated authority, ambiguous agent identity, or trust assumptions leading to unauthorized actions.
  • ASI04:2026 Agentic Supply Chain Compromise
    Compromise of external agents, tools, schemas, or prompts that agents dynamically trust or import.

Risk Assessment

Every vulnerability scored by CVSS, ranked by severity, traceable to the failing probe.

OVERALL CVSS SCORE: 8.4/10.0 HIGH

Findings by Severity

• Critical: 3
• High: 8
• Medium: 5
• Low: 2

See Where Risk Is Concentrating

Run red teams continuously across every AI app. Spot which apps, categories, and trends are heading the wrong way.

Testimonials

Trusted by companies that take AI security seriously.

Finom
"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."

Anoop Mahajan, Director of QA, Amdocs
"Before Confident AI, a single improvement cycle took 10 days — now the same cycle takes three hours, and our product managers can run it themselves."

Have a Question?

What attacks do you simulate?
We cover the OWASP LLM Top 10 and OWASP Agentic AI Top 10 out of the box — prompt injection, jailbreaks, PII leakage, excessive agency, insecure output handling, bias and toxicity, and more.

Do I need to modify my codebase to get started?
No. If your AI app is reachable via an API endpoint, that's enough.

How do I act on a failed attack?
Every failed probe comes with the exact prompt, model's response, severity, and concrete remediation guidance.