Scope. Assess. Fix. Prove.
Autonomous AI agent security demands moving beyond static Web2 code scanning. We evaluate non-deterministic model behavior, memory state poisoning, tool call privileges, and RAG pipeline boundary leakage.
The Four-Phase Technical Audit Workflow
Scope & Threat Model Phase
We deconstruct your AI agent architecture, vector pipelines, MCP tool definitions, and trust boundaries during interactive scoping workshops.
- Map agent state machines, tool access rights, and LLM API roles.
- Identify critical assets, sensitive knowledge bases, and privilege thresholds.
- Define test scope, staging parameters, and rules of engagement.
Assess & Adversarial Red Team Phase
Our team executes automated fuzzing and expert manual red teaming against prompt interfaces, RAG context vectors, and MCP extensions.
- Deploy 150+ direct and indirect prompt injection attack vectors.
- Test tool argument poisoning, schema bypasses, and memory corruption.
- Probe RAG cross-tenant retrieval boundaries and PII exfiltration channels.
Fix & Remediate Phase
We partner directly with your engineering leads to deliver precise, production-grade security patches and guardrail rule configurations.
- Provide exact system prompt refactoring rules and structural delimiters.
- Deliver Zod validation wrappers and parameter sanitizers for MCP tools.
- Configure guardrail policies (NeMo, Llama Guard) and vector query filters.
Prove & Re-Test Phase
We re-execute test suites against remediated staging environments to verify exploit resolution and issue executive attestation reports.
- Re-test all identified vulnerability vectors to confirm successful patch application.
- Issue executive summary reports and compliance alignment certificates.
- Deliver post-engagement debrief and SecOps monitoring recommendations.
Framework & Taxonomy Alignment
Our audit test suites map directly to globally recognized security standards and taxonomies.
Model Injections & Memory Poisoning
Covers prompt injections, sensitive information disclosure, supply chain risks, and excessive agency.
Map, Measure, Manage, Govern
Structured risk management functions tailored to artificial intelligence system deployments.
Adversarial Threat Taxonomy
Empirical matrix of tactics and techniques targeting machine learning and agentic systems.
AI Management System Controls
Global standard defining organizational requirements for responsible and secure AI management.
Ready to Apply This Methodology to Your AI System?
Schedule a scoping call with our lead AI security architects to map out an audit plan tailored to your technical stack.