Security for AI agents & LLM systems
AI agents increasingly act autonomously — with tool access, memory and connections to your systems via protocols such as MCP. This creates attack surfaces that conventional security testing does not cover. VamiSec assesses, hardens and governs your AI systems across their entire lifecycle.
From the offensive testing of agentic systems through MCP Security and Threat Modeling to transparency across the AI supply chain: our Agentic AI Security services interlock and are guided by established frameworks such as the OWASP projects for GenAI and agentic applications, MITRE ATLAS and the NIST AI Risk Management Framework.
Where agentic systems are vulnerable
From prompt to target system: every station of an AI agent has its own threats. Pick a station — we show typical risks and the services that address them.
Typical threats
- Direct and indirect prompt injection via user input, documents and web content
- Jailbreaks and goal manipulation that bypass guardrails
- Poisoned context from RAG sources and retrieval pipelines
Matching services
Threat selection based on the OWASP Top 10 for Agentic Applications (ASI01–ASI10), the OWASP MCP Top 10 and CSA MAESTRO.
Three fields, eight services
Our portfolio mirrors the navigation: consulting & platform security, offensive testing with detection & response, and threat modeling & supply chain transparency — every service links to its in-depth page.
Testing & Hardening
Agentic AI Pentesting
Offensive security testing of autonomous AI agents: we test tool access, memory, orchestration and guardrails against real-world attack scenarios — following the OWASP methodology for agentic applications.
- Prompt injection, jailbreak and tool abuse scenarios
- Memory and context manipulation in the live system
- Reproducible findings with exploit path and fix recommendation
AI Product Pentesting
Pentesting and red teaming for products with embedded AI – robots, AIoT devices, AI cameras and voice assistants: from hardware and firmware to model, LLM and physical impact.
- Threat modeling with STRIDE × MAESTRO incl. physical layer
- Testing along the OWASP LLM Top 10, AISVS, ISTG and IoT Top 10
- Evidence for CRA, EN 18031, AI Act and Machinery Regulation
Agent Skills Security
Assessment and hardening of Agent Skills and Progressive Disclosure mechanisms — against compromised capabilities, tool misuse and the OWASP Agentic Skills Top 10.
- Analysis of skill definitions and progressive disclosure
- Detection of embedded instructions and rug-pull risks
- Approval and update processes for skill catalogues
ADR — Detection & Response
Runtime detection and response for AI agents: telemetry across prompts, tool calls and agent identities, detection use cases against goal hijacking and more, and rehearsed response playbooks — integrated with your SOC.
- Telemetry across prompts, tool calls and identities
- Detection rules for agentic misbehaviour
- Response paths and playbooks for emergencies
Threat Modeling & Transparency
AI Threat Modeling (MAESTRO)
Systematic threat modeling for AI and agent systems following MAESTRO — identifying attack paths before they reach production.
- MAESTRO applied across 7 architecture layers
- Combined with STRIDE and the OWASP catalogues
- Prioritised risks with a measures roadmap
MCP Threat Modeling
Threat modeling built specifically for MCP landscapes: STRIDE, MAESTRO and the OWASP MCP Top 10 combined into one dependable threat map.
- Modeling of MCP servers, clients and tool chains
- STRIDE × MAESTRO × OWASP MCP Top 10
- Trust boundaries and authorisation architecture
Automated Threat Modeling
Diagram-as-Code and automated analysis embed Threat Modeling directly into your development process — reproducible and scalable.
- Diagram-as-code from architecture artefacts
- Threat modeling anchored in CI/CD
- Continuously current models instead of one-off workshops
SBOM for AI
Transparency across the AI supply chain: capturing models, datasets, libraries and dependencies as an AI-Bill-of-Materials — the foundation for Governance and evidence requirements.
- Inventory of models, datasets and libraries
- AI-BOM formats and tooling integration
- Evidence for the EU AI Act, NIS2 and customer audits
From threat model to secure operations
Four phases, one thread: each phase builds on the results of the previous one — and each can be commissioned on its own.
Capture threats systematically
We model your agent system with MAESTRO across 7 architecture layers, combine STRIDE with the OWASP catalogues and prioritise risks along your real architecture — the basis for testing, hardening and operations.
Anchored in recognised standards
Our services follow the authoritative catalogues and regulations for AI and agent security — from OWASP to the EU AI Act.
Deepen your knowledge
In our knowledge section you will find well-founded deep dives on MCP Security, the OWASP standards, Zero Trust for AI agents and AI security frameworks — with verified sources.
Frequently asked questions
Answered briefly — we clarify the details in an initial consultation.
What distinguishes agentic AI security from classic LLM security?
Agents plan, use tools, keep memory and act autonomously in real systems. That creates risks far beyond prompt filters: tool poisoning, memory poisoning, misaligned autonomy and unattributable agentic identities. Our services address exactly these layers — from modeling to operations.
We use MCP — where do we start?
With an MCP security assessment: we review servers, authorisation (OAuth 2.1), tool definitions and trust boundaries, and prioritise the findings. MCP threat modeling and targeted pentests build on top of that.
Do we need a threat model first, or a pentest first?
Ideally both, in that order: the threat model makes architecture and risks visible and focuses the pentest on the critical paths. For systems already in production you can also start with a pentest — the results then feed back into the model.
Does this also cover compliance requirements such as the EU AI Act?
Yes — threat models, test reports and AI SBOMs are solid evidence for the EU AI Act, ISO/IEC 42001 and NIS2. For the governance side we work seamlessly with our GRC team — up to a certifiable AI management system.
How quickly does an engagement deliver first results?
Assessments and threat modeling workshops deliver prioritised results within days; pentests follow the agreed scope. Detection & response is built iteratively — starting with the signals that cover your biggest risk.
Are you deploying AI agents? Then secure them.
In a no-obligation initial consultation we assess your AI landscape and show which testing and hardening building blocks deliver the greatest leverage for you.
Schedule an initial consultation