The joke is that Xbow only works because they have close to 100 employees operating the software

Took a while to notice it's xbow and not xbox

This is a neat project, I don't know why you'd want to set it up with this comparison to Xbow. As someone who works (worked? I'm non-ironically still trying to figure out if I belong in this space post-AI!) in this space and knows some of the actors, I'm pretty sure there's more to Xbow than ~1800 lines of prompts. Like: this is your RCE template prompt:

https://github.com/usestrix/strix/blob/main/strix/prompts/vu...

... and this is great, I'm not dunking, but pretty basic?

We just had the DARPA AIxCC results come in, and those systems are (1) open source and (2) presumably simpler/less polished than Xbow (some of the authors will be quick to tell you that they're doing PoC work, not product development), and (3) they're more complicated than this.

Again, to be super clear: I think there's a huge amount of potential in building something like this up. Nessus was much simpler than ISS when it first shipped, but you'd rather be Nessus than an ISS scanner developer! I'm just: why set this bar for your project?

Best of luck with this!

Seems heavily vibe coded, down to the Claude-generated README and a lot of the LLM prompts themselves (which I have found works very poorly compared to human-written prompts). While none of this is necessarily bad, it requires a higher burden of proof that it actually works beyond toy problems [0]. I think everyone would appreciate some examples of vulnerabilities it can find. The missing JWT check showcased in the screenshot would've probably been caught with ordinary AI code review, so to my eye that by itself is not persuasive.

Good luck!

[0]: Why I say this --- a 10kLOC piece of software that was mostly human-written would require a large amount of testing, even manual, to ensure that it works, reliably, at all. All this testing and experimentation would naturally force a certain depth of exploration for the approach, the LLM prompts, etc across a variety of usecases. A mostly AI-written codebase of this size would've required much less testing to get it to "doesn't crash and runs reliably", and so this depth is not a given anymore.