Not without a lot of other software to give it access to whatever OS you are testing. Gemini can't even directly access google docs yet, let alone your C drive, networked machines, or virtual machines.
if your argument is that Claude can't operate as a full-fledged autonomous pen tester and do the whole job for you, that's true. but any competent engineer should be able to find some productivity boost from it.
An LLM wouldn't really be the right tool. But some of the black-box fuzzing tools out there were already using machine learning approaches before the "AI boom" happened.
I'm sure someone's found a way to apply agentic shit to it.
There is only a minor difference. One is a random destination and one is a random walk. In a two dimensional space there is no expected difference in outcome. In higher dimensional spaces the random walk fails almost immediately.
There is zero evidence this is happening. Plus you can't really use LLMs to effectively find vulnerabilities in a closed source product.
you can't use LLMs to reason about the source code itself, sure. but it can absolutely help you pentest.
Not without a lot of other software to give it access to whatever OS you are testing. Gemini can't even directly access google docs yet, let alone your C drive, networked machines, or virtual machines.
if your argument is that Claude can't operate as a full-fledged autonomous pen tester and do the whole job for you, that's true. but any competent engineer should be able to find some productivity boost from it.
An LLM wouldn't really be the right tool. But some of the black-box fuzzing tools out there were already using machine learning approaches before the "AI boom" happened.
I'm sure someone's found a way to apply agentic shit to it.
Monte carlo methods. Literally: "Try random shit in a simple pattern designed to maximize coverage."
The search space is simply too large to do anything else.
And waste millions of dollars in tokens in the process.
I was talking about the mutation-based ones. They get hybridized with MC frequently, but would be classed as Genetic Algorithms if I'm not mistaken.
There is only a minor difference. One is a random destination and one is a random walk. In a two dimensional space there is no expected difference in outcome. In higher dimensional spaces the random walk fails almost immediately.