Beyond Jailbreak Folklore: Why LLM Security Is a Systems Engineering Problem
Beyond Jailbreak Folklore: Why LLM Security Is a Systems Engineering Problem
Independent analysis of agent engineering, red team research, and evaluation practice. For practitioners who ship and secure AI systems.
Deep-dive analysis of multi-agent architectures, tool routing, memory systems, and production deployment patterns for LLM-based pipelines.
Research-grade breakdowns of prompt injection, jailbreak techniques, supply chain risks, and adversarial hardening for AI products in the wild.
Beyond leaderboards — evaluation design, drift monitoring, release gates, and benchmark construction for teams that need to know if a model is actually ready.
Architecture decisions, security boundaries, code review workflows, and testing strategies for AI coding assistants and autonomous software agents.
Step-by-step walkthroughs for building, evaluating, and securing AI systems. From first agent to production-grade deployment — with working, audited code.
Tracking and reviewing the open source frameworks, datasets, and infrastructure that practitioners use to build, evaluate, and secure AI systems in production.