Prompt injection and jailbreak testing
We attempt to override the system's instructions and safety controls with crafted and indirect prompts, and document what gets through.
AI Red Teaming Services
AI Red Teaming Services
Trusted across 20+ countries by Fortune 500 companies and growth-stage brands
Find out how your AI can be broken, before someone else does. Noseberry adversarially tests your LLM, RAG and agent systems for prompt injection, jailbreaks, data leakage and unsafe outputs, and hands back a prioritised findings report with reproducible cases and clear fixes. Independent, technical assurance, from a team that also builds secure AI.
Book a free red-teaming scope callDefinition
What is AI red teaming?
AI red teaming is structured, adversarial testing of an AI system to discover how it can be broken, misused or made to behave unsafely, before attackers or real users do. It targets the failure modes unique to AI, prompt injection, jailbreaks, data and secret leakage, harmful or off-policy outputs, and unsafe tool or agent behaviour, and delivers a prioritised findings report with reproducible cases and remediation. It reduces risk and evidences weaknesses; it is technical assurance, not a certification or a guarantee that the system is safe.
Key takeaways
- AI red teaming adversarially tests an AI system to find how it can be broken, misused or made to behave unsafely, before attackers or users do.
- It targets AI-specific failure modes: prompt injection, jailbreaks, data leakage, harmful or off-policy outputs, and unsafe tool use.
- The output is a prioritised findings report with reproducible cases and clear remediation, not a pass/fail stamp.
- Red teaming tests defences; AI security services build them. The two work best together.
2M+Lives touched
15+Fortune 500 clients
20+Countries served
250+Digital solutions delivered
Scope
What AI red teaming covers
We test the AI-specific ways a system fails, to agreed rules of engagement, and give you findings you can act on.
Data leakage and extraction
We probe whether the system can be made to reveal secrets, system prompts, other users' data, or its own training or retrieval sources.
Harmful and off-policy output testing
We test whether the system can be pushed to produce unsafe, biased, non-compliant or off-brand responses.
Tool and agent abuse
For agents, we test whether tools, actions and permissions can be misused to reach beyond the intended scope.
Robustness and evasion
We test how the system holds up under adversarial, malformed and edge-case input designed to make it fail.
Prioritised findings and remediation
A clear report of what we found, ranked by severity, with reproducible cases and specific fixes your team can action.
Who this is for
Built for teams launching AI they cannot afford to get wrong
Product, security and engineering leaders putting an LLM, RAG or agent system in front of users or into a workflow, who want independent adversarial assurance before and after go-live.
Signs you need it
- You are about to launch an LLM, RAG or agent system to real users.
- Your AI system can take actions, touch sensitive data or represent your brand.
- You want independent, adversarial assurance before go-live, not just internal testing.
- A customer, partner or board is asking how safe your AI actually is.
- You have added guardrails and want to know whether they hold under attack.
How we work
Scope, attack, report, retest
1
Scope and rules of engagement
We agree the system, the goals, the boundaries and what success looks like.
2
Recon
We map the system's inputs, tools, data sources and intended behaviour.
3
Attack
We run structured adversarial tests across the AI-specific failure modes.
4
Report
We deliver prioritised, reproducible findings with clear remediation.
5
Retest
After fixes, we re-run the cases to confirm the issues are closed.
Why Noseberry
Why choose Noseberry for AI red teaming
Adversarial mindset, AI depth
We test the AI-specific failure modes, not just a generic pen-test checklist.
Actionable, not theatrical
Reproducible findings and specific fixes your engineers can act on, ranked by real severity.
Build and break under one roof
The team that builds secure AI also breaks it, so remediation is realistic and fast.
Independent and honest
A straight read of where your AI is exposed. A report, not a rubber stamp or guarantee.
Frequently Asked Questions
AI red teaming is adversarial testing of an AI system to find how it can be broken, misused or made to behave unsafely, before attackers or real users do. It targets AI-specific weaknesses such as prompt injection, jailbreaks, data leakage and unsafe outputs, and produces a prioritised findings report with fixes.
Security services design and build the defences; red teaming adversarially tests them to see what holds. They are complementary, and we offer both, but you can engage red teaming on a system built by anyone.
Prompt injection and jailbreaks, data and secret leakage, harmful or off-policy outputs, bias, tool and agent abuse, and robustness under adversarial input, scoped to your system and goals.
A prioritised findings report with reproducible test cases, severity ratings and specific remediation guidance, plus an optional retest after you apply the fixes.
No. Red teaming finds and evidences weaknesses so you can fix them; it reduces risk but cannot prove the absence of all risk. It is technical assurance, not a certification or a guarantee.
Yes. We work to agreed rules of engagement against existing LLM, RAG or agent systems, whoever built them.
Break it before someone else does
Book a free scope call and we will design a red-teaming engagement for your AI system.
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