Alright folks.. To qualify myself. I am a vulnerability Researcher @ MIT. My day to day research concerns embedded hardware/software security. Some of my current/past endeavors involve AI/ML integration and understanding just how useful it actually is for finding/exploiting vulnerabilities. Just last week my lab hosted a conference that included MIT folks and the outsiders we invite. One talk was on the current state of AI/LLM. To keep things short, this article is sensationalized and overstates the utility of AI/ML on finding actual novel vulnerabilities. As it currently stands, LLMs cannot even reliably find bugs other less sophisticated tools could have found in much less time. Binary Exploitation is a great place for illustrating the wall you’ll hit using LLMs hoping for a 0day. While LLMs can help with things like setting up fuzzers or maybe giving you a place to start manual analysis, their utility kind of stops there. They cannot reliably catch memory corruption bugs that a basic fuzzers or sanitizers could have found within seconds. This makes sense for that class of bugs. LLMs are fuzzy logic and these issues aren’t reliably found with that paradigm. That’s the whole reason we have fuzzers; they find subtle bugs worth triaging. You’ve seen how well LLMs count, it’s no surprise they might miss many of the same things a humans would but fuzzers wouldn’t (think UaF, OOB, etc). All the other tools you see written for script kiddies yield the same amount of false positives they could have gotten with other tools that already exist.. I can go on and on but I am on shuttle, typing on small phone. TLDR: Article is trying to say LLMs are super hackers already and that’s simply false. They definitely have allure for script kiddies. In the future this might change. LLMs time saving aspects are definitely worth checking out for static binary analysis. Binary Ninja with Sidekick saves a lot of time! But again. You still need to double check important things!