Advertisement
Advertisement
⚡ Community Insights
Discussion Sentiment
38% Positive
Analyzed from 667 words in the discussion.
Trending Topics
#tools#software#model#project#runs#command#scope#phone#network#against
Discussion Sentiment
Analyzed from 667 words in the discussion.
Trending Topics
Discussion (19 Comments)Read Original on HackerNews
What an irony. I cant publish a attack surface mapping / pentesting tool i wrote which runs fully deterministic and really controlable due to "dual use" legal problems - but llm driven tools hit public space......
sorry for the rant....
Metasploit is one example: https://github.com/rapid7/metasploit-framework
The problem is that they are formulated in a way that it is super easy to have your software being possible "dual use" and that a judge has to decide if its fine or not. Making it worse it also states your "intention" which well is impossible to proof - if the judge says he doesn't believe your intentions are only good, you can literally get massively sued.
So ye i could move to another country and than publish it - apart from that i can let it rot on my hdd (which is prolly what will happen).
Edit: Additionally mentioned, it is not just the publishing in germany, even the facilitating already which is why i don't even have an article about it (any more).
I'm asking cuz I started devloping a c2+agent+BOF kind of thing with custom bytecode vm for the lulz (to learn how stuff works nowadays) and it's on tangled and github :/
The project started with a question: how much of a real pentesting workflow could I run locally on relatively old mobile hardware, without relying on a cloud model or API?
Nightcrawler runs a 1.2B-parameter model locally on the Adreno GPU of a OnePlus 8. The model chooses targets and tools, while a separate scope-enforcement proxy validates every command before execution. The system maintains per-host memory in SQLite, rotates between targets, matches detected versions against a local CVE database, executes multi-step playbooks, and generates a structured report.
A few implementation details that may be interesting:
Local inference runs at roughly 115 prompt tokens/sec and 13 generated tokens/sec. The small model only produces a usable command around 50% of the time, so much of the engineering is recovery logic, duplicate detection, persistent memory, and deterministic playbooks. Every command passes through a separate scope and safety layer rather than trusting the model to remain in scope. The project includes a dry-run mode, so the agent loop can be tested without executing real network commands or owning the phone hardware. I've had it running on my home network for the past 3 months uninterrupted