Adversarial evaluation
Test the system the way an attacker will.
We challenge AI applications and agents to expose exploitable behaviour, hidden failure paths and control weaknesses before they become incidents.
Attack surface
A model can be safe while the system around it is not.
Our red teams test the connected system: application logic, retrieval, permissions, tools, APIs, memory, data flows and autonomous actions.
Prompt layer
Jailbreaks, direct and indirect prompt injection, instruction hierarchy failures.
Data layer
Sensitive-data retrieval, cross-tenant exposure, training-data leakage and exfiltration.
Tool layer
Tool misuse, unsafe function calls, privilege escalation and unauthorized actions.
Agent layer
Goal hijacking, excessive agency, memory poisoning and unsafe delegation.
Application layer
Weak authorization, insecure orchestration, API abuse and control bypass.
Engagement design
Built around your system and risk.
- Threat-led test plan tied to the system's use case
- Rules of engagement and safe testing boundaries
- Manual and automated adversarial testing
- Reproducible evidence for validated findings
- Severity, exploitability and business-impact analysis
- Remediation guidance and optional retesting
Begin with evidence