The full AI attack surface
System prompts, RAG pipelines, tool and function calls, agent chains, and multimodal inputs — attacked end to end, including the injection paths that arrive through the documents and data your product ingests.
Services AI Product Red Teaming
AI Product Red Teaming
Your AI product, attacked the way real adversaries will.
We red team AI products the way attackers actually approach them — prompt injection, jailbreaks, tool and retrieval abuse, data extraction — then hand you the fixes and a regression eval suite, so every future model release gets tested against everything we found.
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What is AI red teaming?
AI red teaming is adversarial testing aimed at the failure modes unique to AI products: making the model ignore its instructions (prompt injection), extracting secrets or other users' data from context and retrieval, abusing the tools and actions an agent can take, and eliciting outputs that create legal or brand exposure. It differs from a penetration test in target and cadence — the attack surface is the model plus its orchestration, prompts, retrieval, and tools, and findings must be re-tested on every model or prompt change, not once a year.
What you get
Offensive engineers, not a script
The same offensive team behind our penetration testing — hackers trusted by companies like Apple, Tesla, and Atlassian — applying real tradecraft, not a public jailbreak list replayed against your API.
Fixes at the right layer
Every finding comes with mitigation worked out with your engineers — prompt hardening, retrieval scoping, tool gating, output policy — and verified fixed, not just reported.
An eval suite you keep
Findings become automated regression tests you run on every model upgrade and prompt change — so the red team's value compounds instead of expiring at the report.
How it works
- 1
Scope
About a week.We threat-model your product: what the AI can access, what actions it can take, and what matters most to protect — your data, your users' trust, and your brand.
- 2
Attack
Sized to your product's surface.Systematic adversarial testing across prompts, retrieval, tools, and agent behavior — prod-safe rules agreed up front, findings reported as they land, with proof-of-concept for every claim.
- 3
Harden & regress
Fixes verified, suite handed over.We work the fixes with your team, re-test until the attacks fail, and hand over the eval suite wired into your pipeline — so the next model release gets the whole gauntlet automatically.
AI Product Red Teaming questions, answered
How is this different from a penetration test?
A pentest targets your application and infrastructure; AI red teaming targets what your product does with a model — prompts, retrieval, tools, agent behavior. The vulnerabilities are different (injection beats SQLi here), and so is the lifecycle: AI findings must be re-tested every time the model or prompts change, which is why we leave an eval suite behind. Most AI products eventually need both, and we run both.
We build on a frontier model. Isn't safety the provider's job?
The provider hardens the model; everything you added is yours — your system prompt, your retrieval corpus, your tools, your agent logic. That's where product-specific testing pays off, and no upstream provider can test what your orchestration allows. The provider hardens the model; we make everything you built around it just as strong.
Do enterprise buyers actually ask for this?
Increasingly, yes — AI review committees now sit inside enterprise procurement, asking how your AI has been adversarially tested. A red team report plus a living eval suite is a strong answer, and it pairs naturally with ISO 42001 if you're headed there.
Will you test against production?
We prefer staging with production-equivalent configuration. When production is the only faithful environment, we agree prod-safe rules first — rate limits, no destructive tool calls, no real customer data extraction beyond proof-of-concept. You get complete evidence, cleanly.
What does it cost?
Scoped after the threat-modeling week, billed in 15-minute increments with an optional monthly cap — surface area drives effort, so a single chatbot and a multi-agent platform price very differently. The attack-surface review is free and gives you the realistic range.
Every attack we run is a question your buyers already have — we hand you the answers.
Ship AI that holds up
Send your company email and we'll come back with your AI attack-surface review.