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Macksofy Technologies
Topic hub · 4 guides

AI, LLM and Agentic Security Guides

Practical AI security guides for LLM, RAG, MCP and agentic systems, covering provider selection, prompt injection, tool abuse, identity, memory and regression testing.

In short

What does AI application security include?

AI application security protects the complete system around a model: instructions, retrieval, data, tools, APIs, identity, authorization, memory, approvals and operations. This hub helps teams model that attack surface, test prompt and tool abuse, evaluate assessment providers and turn findings into repeatable security regression tests.

How to use this hub

AI risk is rarely contained inside the chat window. A hostile document can influence retrieval, a model can call a permitted tool with unsafe arguments, or a shared service identity can cross a tenant boundary. The security boundary extends to the furthest system the AI can influence.

Use this cluster to inventory the action chain, understand MCP and agent-specific failure modes, and compare assessment providers on end-to-end impact rather than jailbreak counts. The guides use current OWASP, MITRE and NIST concepts while keeping the testing plan specific to the application being shipped.

What you can decide
  • Inventory model, RAG, tool, identity, memory and operational trust boundaries
  • Test direct and indirect injection through to unauthorized data or action
  • Evaluate AI security providers on impact evidence and remediation depth
  • Build versioned regression tests for high-risk abuse cases
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