| ▲ | mirekrusin 11 hours ago | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
If mythos can break into almost all of their classified systems in hours then other models including opus, gpt, gemini and large open weight models can do so as well, maybe you'll have to double hours or it may become days, but they also will, there is no "maybe" in here. State sponsored, non-public penetration fine tunes (of possibly public ones) likely can do it even faster. Unsupervised penetration RL loop is ideal setup similar to optimization one – it's relatively easy to gain function on it. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| ▲ | dualvariable 3 hours ago | parent | next [-] | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Also, this is just security through obscurity. The holes that mythos exploited still exist after you've tried to limit mythos accessibility. And the fact that all our systems are riddled with security holes shouldn't be too much of a surprise given the way that we all know that software is developed and how tech debt / chores are constantly underbudgeted (plus I think this underscores that any one human's knowledge and attention are inherently limited, and even the best PR review is going to leak all kinds of security holes). | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| ▲ | johndough 10 hours ago | parent | prev [-] | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
I don't think that is necessarily true. - With a weaker model, the time to break into the system might grow so larger that it becomes infeasible, similar to how password hashes can be bruteforced, but if the password is long enough, that is not going to happen in our lifetime. - There might be problems which are inherently unsolvable with a lower level of intelligence. For example, your dog won't derive calculus from scratch, even if it lived forever. - LLMs might be biased in such a way that they never explore the entire solution space, no matter how many attempts are made. Some models are notorious for getting stuck in a loop, trying small variations of the same approach every time, even though it is doomed to fail. This can be counteracted somewhat with higher sampling temperature, but that hurts reasoning capabilities. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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