I don't really find the "because it's difficult" arguments convincing at all. Especially the one claiming it's hard because it requires designing and running a large number of tests and reasoning about the results of each one. That kind of tedious grinding is exactly where LLMs should shine vs humans! The only convincing argument here is that these things are battle tested (literally in most cases I would guess), wit…
There are very few computer-era symmetric ciphers that were truly broken. RC4 is probably the worst example. There are no reasonable attacks even on the good old DES. And by "reasonable" I mean attacks that would bring down the complexity to a practical level if the DES key size were to be extended to something like 128 bits. We can brute-force DES keys trivially, but that's not a fault of the cipher per se.
LLMs won't break symmetric crypto
41–50 of 108 posts
Re: LLMs won't break symmetric crypto
#42LLMs will accelerate math research, increasing understanding in areas like quantum which will eventually lead to breakthroughs that will break most standard asymmetric encryption algorithms with the side effect of breaking crypto
Re: LLMs won't break symmetric crypto
#43This is kind of a stupid argument. How about make a slightly stronger claim like "models won't break symmetric crypto" ? I mean, language models aren't even trained to break symmetric crypto. There is not good reason to think they will. It seems possible to train a large model to do it though.
To train a large model to do what? Break AES? How would that work?
Re: LLMs won't break symmetric crypto
#44okay so silicon valley won't happen all the way
Re: LLMs won't break symmetric crypto
#45Cryptographic systems are based on 1) mathematical impossibility of reversing some integer/mod calculation, 2) time required for a brute force attack, 3) correctness of algorithms and code used in implementations. The last part (algorithms and code) is where LLMs have a chance. The first one is not similar to the mathematical breakthroughs LLMs are making recently. There is a loss of information in mods and integer c…
Yeah, I wouldn’t say with certainty that LLMs will never break any symmetrical crypto algorithm. It will certainly require a lot of effort, but so does solving some hard math challenges and it has been proven successful in that in the past. Most likely outcome will be that a security researcher is able to break one with assistance of / in collaboration with an LLM.
Re: LLMs won't break symmetric crypto
#46Re: LLMs won't break symmetric crypto
#47This is kind of a stupid argument. How about make a slightly stronger claim like "models won't break symmetric crypto" ? I mean, language models aren't even trained to break symmetric crypto. There is not good reason to think they will. It seems possible to train a large model to do it though.
Agreed that many of the articles claims are a bit weak. One point is reasonably strong though: symmetric crypto may not be breakable (battle tested).
Re: LLMs won't break symmetric crypto
#48Earlier quoted context omitted.
Train on plaintext, ciphertext -> key.
LLMs aren't literally science fiction.
And, just because what I'm saying isn't especially likely to work, it's not obvious that it cannot. Very large models are doing all manner of things that very smart people thought were not possible just 6 or 7 years ago.
Re: LLMs won't break symmetric crypto
#49LLMs will accelerate math research, increasing understanding in areas like quantum which will eventually lead to breakthroughs that will break most standard asymmetric encryption algorithms with the side effect of breaking crypto
"LLMs will accelerate math research, allowing us to prove that meaningfully sized quantum computers are impossible and crypto is secure. Modern cryptographic algorithms remains unbroken until the last human is turned into a paperclip in the year 2430"
Re: LLMs won't break symmetric crypto
#50Earlier quoted context omitted.
LLMs aren't literally science fiction.
Well, again, i'm not talking about a language model.... And, just because what I'm saying isn't especially likely to work, it's not obvious that it cannot. Very large models are doing all manner of things that very smart people thought were not possible just 6 or 7 years ago.