AI Security Testing
Recurring adversarial validation through supported AI-system interfaces, designed to surface behavior changes and recheck known weaknesses without waiting for an annual assessment.
Application security, product security, and AI engineering teams operating connected AI systems.
Scheduled baseline runs, plus analyst-triggered replay and remediation retesting.
An authorized supported interface, target scope, test credentials where required, and prohibited-action boundaries.
Judge-scored transcripts, technical findings, evidence, remediation context, and retest status.
What's included
Connected system context
Model the supported AI interface together with the exposed application, API, data, tool, and permission boundaries relevant to its impact.
Versioned attack-template library
A fixed, continuously-updated library covering prompt injection, jailbreaking, data exfiltration, and tool-call abuse categories.
Bounded baseline testing
Run lower-intensity scheduled attacks within defined scope, then escalate selected systems into a separately scoped red-team campaign.
Evidence, not raw transcripts
Every finding is judge-scored with the specific evidence that drove the verdict — not a wall of unreviewed model output.
How it works
Connect a supported interface
Register the authorized chat, voice, API, RAG, agent, or MCP interface and verify the connection.
Define reachable scope
Record exposed tools, actions, data boundaries, credentials, and prohibited behavior relevant to the assessment.
Run the template library
Attack templates execute on your chosen cadence, scored by a separate judge against explicit success criteria.
Review and retest
Inspect transcript-backed findings, evaluate conservatively correlated context, and rerun remediation checks.
See Oxyne on your own systems.
Book a 30-minute walkthrough — we'll scope a real assessment for your AI and web surfaces.