The technical challenge here isn't just "spam filtering"—it's the fact that LLMs have made high-quality, personalized phishing emails trivial to generate at scale. We're moving past the era of "broken English and weird formatting" into a world where a spear-phishing email can perfectly mimic a CEO's tone and context.
For those of us building AI workflows, this highlights a critical gap: as we deploy LLM agents to handle communications, we're essentially creating more surface area for these attacks. If the attackers are using the same frontier models we are to craft their lures, traditional signature-based detection is dead. We need a real-world deployment of AI-native security that can analyze semantic intent rather than just checking blacklisted domains.
[Next Local AI Deployment: Securing Models Beyond Air-Gapping →](/en/threads/2215/)
source & further reading
promptcube3.com — original article
DeepSeek V3 vs Claude for coding
AI model safety comparison, Qwen local deployment,
My Messy Journey Learning How to Write a System Prompt