Three research papers dropped this week that paint a sobering picture of AI security vulnerabilities we're just beginning to understand.
First, researchers discovered that LLM watermarks—our main tool for tracking AI-generated content—completely fail when users access multiple models simultaneously. The math is brutal: independent perturbations from different watermarking schemes wash each other out entirely.
Meanwhile, a ChatGPT extension for Google Sheets was caught exfiltrating entire workbooks, and new protocols are emerging to test AI agent security against prompt injection and indirect attacks.
Here's what keeps me up at night: we're deploying AI systems faster than we're securing them.
After 25+ years in technology and working extensively with AI agents, I've seen this pattern before. Every transformative technology goes through a "move fast and break things" phase. But with AI, the stakes feel different.
The enterprise adoption curve is steep, but the security learning curve is steeper.
Companies are integrating AI into critical workflows—financial analysis, legal research, healthcare protocols—while fundamental security mechanisms like watermarking prove fragile under real-world conditions.
This isn't about slowing down innovation. It's about building security-first architectures from day one.
What do you think? Are we moving too fast, or is this the necessary friction of early adoption?
— Alonso Palacios
#AIecurity #LLMSafety #EnterpriseAI #CyberSecurity #AIGovernance