❗️Malware developers recently discovered a clever way to evade AI-powered...

❗️Malware developers recently discovered a clever way to evade AI-powered security tools. They embedded references to nuclear and biological weapons inside their spyware. The goal wasn’t to build weapons. It was to trigger the model’s safety systems, causing the AI to refuse analysis or provide less useful responses. It’s a practical example of a growing challenge in AI safety. When models are trained to aggressively avoid certain topics, they can create blind spots that attackers learn to exploit. As both closed and open models become more widely used in cybersecurity, these second-order effects will become increasingly important. We’re still in the early stages of adversaries testing the boundaries of AI safety systems, and it’s easy to imagine future security teams preferring models that are less prone to safety-triggered analysis failures when dealing with complex threats. Excellent example for "AI safety" measures actually leading to much greater danger due to second-order effects! In this case the "safety" measures were abused by malware to skip detection. @aipost 🏴