Prompt-Injection

Comprehensive AI / LLM Security Guide 🆕 Enhanced May 2, 2026 - Updated with rapid-exploitation CVEs and AI security analysis including LLM prompt injection, jailbreak techniques, and agentic system vulnerabilities. A practitioner’s reference for securing Large Language Model and agentic AI systems — attack surface, exploitation techniques, real-world CVE chains, payloads, and layered detection/prevention. Compiled from 60 research sources (OWASP, NVIDIA AI Red Team, Unit 42, Lakera/Check Point, NCSC, CrowdStrike/Pangea, Equixly, Anthropic, OpenAI, Microsoft MSRC, Google, AWS, MITRE ATLAS, Penligent, Red Hat, Pillar Security, JFrog, AuthZed, Trend Micro, Nature, and independent researchers). ...

I’ve spent the last couple of years watching teams bolt an LLM onto a product and then look genuinely surprised when it does something dumb, dangerous, or both. The pattern is always the same. Ship first, wonder about security later. So when OWASP put together a Top 10 specifically for LLM applications, I was relieved. Finally there’s a shared vocabulary I can point at during a review instead of explaining prompt injection from scratch for the hundredth time. ...

I run about a dozen MCP servers in my daily workflow. Playwright for browser automation, Raindrop for bookmarks, Todoist for tasks, a couple of custom ones. Every time I start a Claude Code session, my agent loads all of their tool descriptions into context and uses them to decide what to call. Last month I started thinking about what would happen if one of those tool descriptions was lying to me. ...