I'm not exactly pro-AI, but even I can see that their system clearly works well in this case. If you tune the model to favour false positives, with a human review step (that's quick), I can image your response time being cut from days to hours (and your customers getting their updates that much faster).
You can't catch everything with normal static analysis either. LLM just produces some additional signal in this case, false negatives can be tolerated.
So what? They're not replacing standard tooling like static analysis with it. As they mention, it's being used as additional signal alongside static analysis.
There are cases an LLM may be able to catch that their static analysis can't currently catch. Should they just completely ignore those scenarios, thereby doing the worst thing by their customers, just to stay purist?
What is the worst case scenario that you're envisioning from an LLM hallucinating in this use case? To me the worst case is that it might incorrectly flag a package as malicious, which given they do a human review anyway isn't the end of the world. On the flip side, you've got LLM catching cases not yet recognised by static analysis, that can then be accounted for in the future.
If they were just using an LLM, I might share similar concerns, but they're not.