Comment by jakozaur
15 hours ago
Funny coincidence, I'm working on a benchmark showcasing AI capabilities in binary analysis.
Actually, AI has huge potential for superhuman capabilities in reverse engineering. This is an extremely tedious job with low productivity. Currently reserved, primarily when there is no other option (e.g., malware analysis). AI can make binary analysis go mainstream for proactive audits to secure against supply-chain attacks.
Great point! Not just binary analysis, plus even self-analysis! (See skill-snitch analyze and snitch on itself below!)
MOOLLM's Anthropic skill scanning and monitoring "skill-snitch" skill has superhuman capabilities in reviewing and reverse engineering and monitoring the behavior of untrusted Anthropic and MOOLLM skills, and is also great for debugging and optimizing skills.
It composes with the "cursor-mirror" skill, which gives you full reflective access to all of Cursor's internal chat state, behavior, tool calls, parameters, prompts, thinking, file reads and writes, etc.
That's but one example of how skills can compose, call each other, delegate from one to another, even recurse, iterate, and apply many (HUNDREDS) of skills in one llm completion call.
https://github.com/SimHacker/moollm/blob/main/designs/SPEED-...
Haven't dived deep into it yet, but dabbled in similar areas last year (trying to get various bits to reliably "run" in-context).
My immediate thought was to want to apply it to the problem I've been having lately: could it be adapted to soothe the nightmare of bloated llm code environments where the model functionally forgets how to code/follow project guidelines & just wants to complete everything with insecure tutorial style pattern matching?