02 / RED TEAMING
AI Red Teaming
We attack your AI systems before someone else does. Jailbreaks, prompt injection, data leakage and agent abuse paths — found, documented and fixed.
Work with us →Who it's for
- Teams about to put a chatbot, copilot or AI agent in front of customers.
- Companies whose AI features touch private data, payments or internal tools.
- Security and compliance teams that need evidence an AI system was tested.
Problems we solve
- Users can jailbreak your assistant into harmful, off-brand or embarrassing output.
- Instructions hidden in documents, emails or web pages hijack your agent (prompt injection).
- System prompts, customer data or secrets leak through model responses.
- Agents with tool access can be steered into actions they shouldn't take.
What you get
- Threat model of your AI system: assets, entry points and abuse paths.
- Adversarial test suite: jailbreaks, injections and leakage probes tailored to your app.
- Findings report with severity, reproduction steps and evidence.
- Hardening recommendations: prompts, guardrails, permissions and architecture.
- Re-test after fixes, plus a regression suite you can keep running.
How it works
- 01
Scope
We learn the system, its data, its users and what a serious incident would look like for your business.
- 02
Threat model
We map assets, trust boundaries and the most likely abuse paths.
- 03
Attack
Manual and automated adversarial testing against your models, retrieval (RAG) and tools.
- 04
Report
Prioritized findings with reproduction steps and concrete fixes.
- 05
Harden & re-test
We help you ship the fixes, then test again to confirm they hold.
Typical engagement: 2–4 weeks, depending on how many models, tools and data sources are in scope.
Related work
Red Teaming Latent Spaces
Hands-on examples for red teaming latent spaces and protecting LLM apps — a notebook-driven toolkit for exploring AI security workflows.
Le Confidant
Private, AI-powered insights from your WhatsApp chats — sentiment, engagement, relationship dynamics and red flags. Then chat with your Confidant to go deeper.
FAQ
What is AI red teaming?
Adversarial testing for AI systems. We act like a determined attacker or malicious user and try to make your model or agent leak data, break its rules or take unsafe actions — then help you fix what we find.
How is this different from a regular penetration test?
Pentests target infrastructure and code. AI red teaming targets model behavior: prompts, context, retrieval, tool use and how much your app trusts model output. Most AI-specific risk lives there.
Do you need access to our model weights?
No. Most testing happens through the same interfaces your users and integrations touch. More access — system prompts, logs, architecture — lets us go deeper.
Can you test AI agents with tool access?
Yes, and that's usually where the highest-impact issues are. We test how far an agent can be tricked into misusing its tools and permissions.
Launching an AI system soon?
Tell us what it does and who uses it. We'll suggest a test scope that matches your risk.
Start a conversation