Guide on how to attack and defend prompts (learn.chat.win)

A new practical course, "Learn LLM Security," bundles hands‑on lessons on prompt engineering, attack techniques, and defenses to teach practitioners how to both break and harden LLM-based systems. The curriculum starts with Prompting 101—system prompts, parameters (temperature, top‑p, penalties, token handling) and safe prompt design—then moves into an exploit-focused section that covers AI red‑teaming, jailbreaking vs. prompt injection, system prompt extraction, indirect injection via data sources, and common social‑engineering vectors. A companion module, chat.win, gamifies challenge creation and solving so users can practice attack and defense playbooks in a community setting. This matters because prompt injection and hidden instruction extraction are now practical attack vectors against deployed LLMs, risking policy bypasses and data exfiltration. The guide’s defensive lessons are concrete: input validation and sanitization, strict prompt isolation (separating system instructions from user content), output filtering and monitoring, and secure architectural patterns for deployment. For engineers and red‑teamers, the course delivers immediately actionable techniques—parameter tuning, prompt structuring, monitoring heuristics, and design patterns—that reduce attack surface and improve model safety across real‑world applications.