Amazing write-up of security exploits found in iOS: https://googleprojectzero.blogspot.com/2019/08/a-very-deep-dive-into-ios-exploit.html
(Found in the wild and used indiscriminately! These weren't targeted attacks.)
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I've been impressed with code written by Fable in my testing:
Difftastic: found small optimisations in a hot loop I'd already profiled extensively. Helped me prototype Dijkstra to A* too (hard to find a good heuristic).
Garden: Found some real bugs in my simplistic typechecker.
One fun way of testing new AI models: take an existing codebase you have and just ask them to "review it and fix bugs".
In principle this should find more issues over time as models get smarter. I've found a few bugs this way at least.
After further playing with my LLM project, I'm surprised how hard it is to tune with system prompts.
My agent kept saying "obviously" even though my prompt said "helpful, professional". Eventually I found that a "courteous" prompt gets "of course" which is better but not ideal.