AI red teaming
AI red teaming tests an AI system the way a determined adversary would, against a goal: leak this data, make the agent take this action, get past this policy. HYDN red teams agentic, multi-agent and human-in-the-loop deployments, using the HYDN AI Attack Engine and tailored threat scenarios to validate whether your security programme, tools and controls hold.
What we target
- Goal hijack. Steering an agent away from its task through injected instructions in content it reads.
- Tool misuse. Getting an agent to call tools in harmful ways, or with parameters it should never use.
- Privilege abuse. Using an agent’s access to reach data or systems the user could not reach directly.
- Memory and context poisoning. Planting content that changes later behaviour.
- Inter-agent communication. Messages between agents that can be spoofed or tampered with.
- Human-in-the-loop. Approval steps that can be rushed, confused or bypassed.
These map to the OWASP Top 10 for Agentic Applications.
How it works
We agree objectives and rules of engagement with you, then run campaigns combining the HYDN AI Attack Engine with tailored threat scenarios and manual testing. The result is a validation of how effective your security programmes, tools and controls are against realistic attacks, with full human and machine readable reports, remediation recommendations and post-remediation re-testing.
Frequently asked questions
What is the difference between AI red teaming and LLM penetration testing?
A penetration test looks for as many vulnerabilities as it can in a defined application. An AI red team is goal driven: it picks the outcomes that would hurt you most and tries every route to them, including through people, data sources and connected systems.
Do you test agents that take real actions?
Yes. Agents that call tools, move data or act on behalf of users are where red teaming matters most, because a successful attack does something rather than just saying something.
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