AI RED TEAMING
Find weaknesses in your agents before attackers do. Noma AI Red Teaming uses an adversarial LLM to run automated multi-turn attacks against your agents and applications, finding vulnerabilities, weaknesses, and compliance violations before they can be exploited.
Automated testing that discovers AI risk
Attack like a real adversary
Real attackers chain their attacks instead of running one at a time. Noma simulates this by compounding multiple techniques into one attack sequence, raising attack success rate until your application's real weaknesses surface. Techniques include prompt injection, jailbreak, data leakage, denial-of-wallet, harmful content generation, and more.
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Escalate the attack across turns
Most attacks don't land in one prompt. Noma runs multi-turn attack sequences with escalating pressure at each step, the way a patient attacker works a target.
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Prove compliance with presets
Automatically map findings to compliance presets including OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF, EU AI Act, and ISO 42001 for audit-ready reporting.
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Turn findings into runtime policy
Noma AI Red Team findings feed into AI-DR, so weaknesses that are exposed harden the policies protecting applications at runtime.
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Customers Love Noma
“Our first Noma scan was an eye-opener for many of the developers and data scientists on what the risks are and what you can actually address with a tool like Noma. It gave them confidence to be able to tell our customers that the system is safe and secure.”
Part of the Noma platform
Surface all AI Assets and their risks
Find every agent, model, MCP server, and tool, and surface the risks.
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Set and enforce the rules.
Define what each agent can access and do, enforced in real time.
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Test AI apps and agents continuously.
Attack your AI apps and agents before real attackers do.
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See and stop threats in context.
Stop risky agent behavior at runtime, with full context.
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Secure AI. Control Your Agents.
Built to Higher Standards.
FAQs
What is AI red teaming?
AI red teaming is adversarial testing of AI agents and applications: running real attack techniques against them to find vulnerabilities before attackers do. Noma automates this with an adversarial LLM that attacks like a human red teamer.
What attacks does Noma AI Red Teaming simulate?
Noma runs prompt injection, jailbreak, data leakage, denial-of-wallet, and harmful content attacks, compounded into multi-turn campaigns that escalate pressure at each step the way a patient attacker works a target.
How is this different from one-shot prompt testing?
Most automated testing tries one technique at a time, so defenses catch each attack in isolation. Noma compounds techniques into a single attack sequence across turns, which is how real attackers actually operate.
Do red teaming findings improve runtime protection?
Yes. Findings feed directly into Noma AI-DR detection policies, so every weakness discovered in testing becomes an enforced pattern in production. The loop between testing and protection closes automatically.
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