AI applications contain skills, workflow definitions, tool permissions, model configurations, MCP servers, system prompts, and orchestration logic. Each of these can introduce security or governance risks before an agent ever runs. Over the past couple of weeks I've been extending SafeAI, an open-source static AI Capability & Risk Analyzer. The latest beta expands analysis beyond agents themselves into the AI components that make up modern AI applications. I've just released **SafeAI v1.1 Beta (open source)**, the next step towards making static analysis understand AI applications instead of just source code. New capabilities include: * AI component discovery (skills, prompts, workflows, tools and model configs) * Prompt security analysis * Skill security analysis * Tool definition analysis * Workflow template analysis * Provider-aware model safety checks * Deep MCP security analysis * Capability diff between scans * Early support for Claude Code, Google ADK, Haystack, Mastra, LlamaIndex, Dify and n8n SafeAI remains completely offline—no agent execution, no LLM calls and no cloud services. One area I'd particularly appreciate feedback on is AI-specific detection rules. Repository: [github.com/.../SafeAI](github.com/.../SafeAI) If you're building AI applications, I'd love to know: * What would you expect an AI-aware static analyzer to detect? * Which false positives would be unacceptable?

Build detection at scale. Design and ship security workflows that combine deterministic analysis (taint, reachability, static slicing) with LLM reasoning to find real vulnerabilities (SSRF, IDOR, injection, auth gaps, supply-chain risk, and beyond) across many languages and frameworks. Make LLMs viable for security-critical work. Engineer agentic pipelines and prompts that are precise, cost-aware, and trustworthy: atomic, well-scoped steps grounded in deterministic context, with attention to hallucination, confidence calibration, and which models see sensitive code.