After reading Binance's investigation report, I'm still a bit shaken. One engineer wanted to install a popular AI programming tool and found a tampered Homebrew installer. Another received an invitation on a legitimate job search platform, first with a live video call with HR, then with the "CEO," whose social media accounts contained photos and videos of his personal life, before finally asking him to download a "homework assignment" containing malicious code. If it were just an unfamiliar link or an unknown installer, I believe everyone would be wary. But with all this buildup, I'm not sure I could have detected it myself. So, rather than lamenting how easily employees are fooled, I'm more interested in: once an employee's computer is compromised, how far can the attacker go? The report shows two different attack paths that ultimately converged within the internal system. The attacker used internal AI tools to further control other engineers' devices, obtain deployment tokens, and ultimately access the wallet transaction authorization process. How could a single compromised terminal escalate into such a widespread loss of control? That's what's truly frightening. Thinking about my own work, I realize I often prioritize convenience in many situations. API keys are readily available, server credentials are valid indefinitely, and temporarily granted permissions are often forgotten to be revoked after the task is completed. The same applies to AI tools. You can grant them access to read files, execute commands, connect to servers, and automate deployments… every time you add permissions, there's a reason, and they do indeed work faster afterward. After much effort, when it's almost running, a permission request pops up, and it's easy to casually grant it. A Binance report mentions that internal AI tools are being used as springboards for further intrusion, though the specific mechanisms haven't been disclosed. But reading this makes me want to check: which files can my AI read, and which credentials can it access? When testing a new project, could I be exposing data from other projects as well? We always hope AI asks fewer questions and does more itself. But to save on confirmations, we might grant permissions far exceeding the current task's needs. I can't guarantee I'll never be fooled. At least we can take preventative measures: separate work and testing environments, keep wallets and keys separate, and reclaim temporary permissions after use. Don't let a one-time authorization become a long-term trust.