Prompt & Model Interaction Security
Prompt injection, instruction hierarchy, unsafe model output handling, sensitive-data exposure and provider boundary review.
AI Security
Secure AI systems before autonomy becomes exposure.
We assess LLM, RAG and agentic AI architectures across prompt boundaries, tool execution, data flows, model providers, authorization, isolation and human-control mechanisms.
Threat surface
AI adds new trust boundaries to familiar systems.
AI security is not a model-only problem. The most consequential failures often emerge where untrusted content, tools, data stores, identities, providers and application logic meet.
Retrieval & Knowledge Security
Retrieval poisoning, authorization-aware retrieval, tenant isolation, document trust and data leakage analysis.
Agent & Tool Security
Excessive agency, tool authorization, command execution, sandboxing, credentials, network egress and human approval boundaries.
AI Security Architecture
Threat modeling and secure-by-design patterns for multi-provider LLM, MCP, agentic and workflow-based AI systems.
Control principles
Models reason. Trusted systems decide what is allowed.
Deterministic authorizationDo not let prompt context silently expand execution capability.
Constrained autonomyScope, sandbox, budgets and approval boundaries reduce blast radius.
Auditable executionCapture decisions, tool activity, mutations and evidence for investigation.
Assessment flow
Architecture through adversarial validation.
01Map
Models, providers, data, tools, agents and trust boundaries.
02Threat model
Abuse cases, attacker paths and control assumptions.
03Validate
Targeted adversarial testing within authorized scope.
04Harden
Prioritized remediation patterns and residual-risk evidence.
Good fit
- Agentic applications with tool execution
- Enterprise RAG with sensitive knowledge
- Multi-tenant AI products
- MCP or external tool integrations
- AI systems approaching production or procurement review
What you receive
- AI attack-surface map
- Threat model and prioritized findings
- Exploitability evidence where appropriate
- Architecture and control recommendations
- Remediation review and retest options
AI Security Assessment
Show us where the model can act.
We will map the system around it — identities, tools, data, policies and execution boundaries — and focus testing where autonomy creates risk.