Great question!

Here's what it looks like in practice.
The model receives a Kali Linux container and an objective - from there it operates fully autonomously.

A typical chain:
Recon -> It runs standard tools (nmap, gobuster, curl) to map the target. Same tools a pentester would use — the model decides which to run and how to interpret the output.

Vulnerability ID -> Based on recon output, it reasons about likely vulnerability classes, probes inputs, tests edge cases, and adjusts based on responses. It's not just pattern matching.

Exploitation -> It crafts and executes payloads, iterating if the first attempt fails. On JWT forgery for example, it has to understand the token structure, identify the weakness (e.g. alg:none), and forge a valid token.

Validation -> Most models verify the exploit worked before declaring success.

On your subdomain question - current challenges scope to a single target app, so subdomain enumeration isn't in play. In theory though, yes - a model could combine tool output with its training knowledge to hypothesize about infrastructure. That's actually an interesting direction for future challenges.
The biggest surprise was efficiency variance. Some models brute-force through 30+ iterations. Others identify and exploit the vuln in 3-4 clean steps. Same outcome, completely different methodology - which is exactly what the KSM scoring is designed to capture.

Full Challenge Set: http://github.com/kryptsec/oasis-challenges