Prompt injection
Direct and indirect attempts to redirect system behavior through user input and untrusted content.
AI security assessments
Your AI can take action. We test the boundaries.
We evaluate AI applications and agents when inputs, data, and connected tools turn adversarial. The goal is to show what can happen in your environment, what to fix, and how to prove it was fixed.
Trust boundary / illustrative01 — 05
Where authority changes hands.
01User input
02Agent & model
03Tools / MCP
04Customer data
05Production systems
Boundary under test
What we evaluate
The system around the model matters.
We test how the model, app, tools, data, identities, and infrastructure interact. Automated adversarial testing is paired with human validation.
Agents & tools
Excessive agency, unsafe actions, connected tools, MCP servers, and permission boundaries.
Data & RAG
Sensitive information exposure, retrieval manipulation, and access across users or tenants.
Models & infrastructure
API keys, model endpoints, vector stores, cloud resources, and third-party dependencies.
Authorization
The gap between what an AI system is asked to do and what the user is allowed to access or change.
Complete attack paths
How an AI weakness can combine with application, identity, or cloud weaknesses to reach a real outcome.
What you receive
Validated findings and usable proof.
The same Dravian standard applies: clear scope, reproducible evidence, remediation guidance, retesting, and documentation for customers or auditors.
AI security brief
Trust boundary & capability map
Adversarial test matrix
AI attack path analysis
Technical report & remediation board
Retest & independent assessment attestation
The next step is simple
Launch your AI product with its real boundaries understood.
Tell us what you are building and what needs to be tested. We will scope an engagement around your product and timeline.