AI agents fail in ways traditional application testing does not look for. A hostile instruction hidden in a document, a tool that returns poisoned content, an agent that can be talked into reaching data belonging to another user — none of these appear in a functional test suite, and all of them are how agents actually get abused.
Status: product concept, in active development. This is something we are building, not something you can buy today. We publish our roadmap because the engineering thinking behind it is the useful part — and because we would rather show you the design than imply a finished product.
Designed Test Coverage
- Threat and attack-case library — a maintained corpus rather than whatever the tester thought of that day.
- Prompt-injection and malicious-document tests — hostile instructions arriving as data.
- Tool-poisoning and excessive-permission tests — what the agent can reach that it should not.
- Data-exfiltration and identity-confusion tests — can user A obtain user B’s data.
- Unsafe chained-action simulations — individually safe steps that combine badly.
- Automated replay across releases — a model or prompt change silently reopening a hole gets caught.
- Execution traces and evidence capture — findings you can demonstrate, not assert.
- Risk scoring and remediation regression suite — fixes stay fixed.
Deliberately Independent
This complements the functional agent evaluations our sister practice builds — it does not replace them, and it is deliberately kept separate. The team that builds an agent should not be the only team that attacks it. The reasoning behind these failure modes is set out in our article on prompt injection.
Tell us if this matches a problem you have — early input shapes what we build first, and we will give you an honest view of where it stands.
Related services
- Penetration Testing and Security Assessment — Red-team work we deliver today, while the lab is in development: penetration testing and security assessment.