Whereas finding novel exploits would still be the domain of human experts?
With more specificity: I would not be at all surprised if the "industry standard" netpen was 90%+ agent-mediated by the end of this year. But I also think that within the next 2-3 years, that will be true of web application testing as well, which is in a sense a limited (but important and widespread) instance of "novel vulnerability" discovery.
However, I currently believe that forensic investigations will change post LLMs, because they're very good at translating arbitrary bytecode, assembly, netasm, intel asm etc syntax to example code (in any language). It doesn't have to be 100% correct in those translations, that's why LLMs can be really helpful for the discovery phase after an incident. Check out the ghidra MCP server which is insane to see real-time [2]
These kind of things are very hard for LLMs because they tend to forget way too much important information about both the code (in the branching sense) and the program (in the memory sense).
I can't provide a schematic for this, but it's pretty common in binary exploitation CTF events, and kind of mandatory knowledge about exploit development.
I listed some nice CTFs we did with our group in case you wanna know more about these things [1]. I think in regards to LLMs and this bypass/sidechannel attacks topic I'd refer to the Fusion CTF [2] specifically, because it covers a lot of examples.
I think people are coming to this with the idea that a pentesting agent is pulling all its knowledge of vulnerabilities and testing patterns out of its model weights. No. The whole idea of a pentesting agent is that the agent code --- human-mediated code that governs the LLM --- encodes a large amount of knowledge about how attacks work.
The former is automated by a large part already with fuzz testing of all kinds, so you wouldn't need an LLM if you knew what you were doing and have a TDD workflow or similar that checks against memleaks (say, with valgrind or similar approaches).
The latter part is what I was referring to where I had hope initially that DNCs could help with that, and what I'd say that right now LLMs cannot discover this, only repeat and translate it (e.g. similar vulnerabilities in the past discovered by humans in another programming language).
I'm talking specifically about discovery here because transformers lose symbolic inference, and that's why you can't use them for exploit generation. At least I wasn't able to make them work for the DARPA challenges, and had to use an AlphaGo based model combined with a CPPN and some techniques that worked in ES/HyperNEAT.
I suppose what I'm trying to say is that there's a missing understanding of memory and time when it comes to LLMs. And that is usually manually encoded/governed how you put it by humans. And I would not count that as an LLM doing it, because you could have just automated the tool use without an LLM and get identical results. (When thinking e.g. about an MCP for kernel memory maps or say, valgrind or AFL etc)
Remember, the competition here is against human penetration testers. Humans are extremely lossy testing agents!
If the threshold you're setting is "LLMs can eradicate memory disclosure bugs by statically analyzing codebases to the point of excluding those vulnerabilities as valid propositions", no, of course that isn't going to happen. But nothing on the table today can do that either! That's not the right metric.
Ha, I laughed at that one. I suppose you're right :D
If that part of the job is automated away. I wonder how the talent and skill for finding those exploits will evolve.