About The Project (description, Duration, Stage)

Hands-on AI-for-Security engagement with a regulated iGaming / online-gaming group. The client's security team is genuinely advanced: they already run an AI-driven offensive-security capability — continuous external-perimeter scanning feeding an LLM agent that plans exploitation, sources and validates exploits, and executes them in sandboxed environments — plus a runtime anomaly-detection layer watching for intrusion and privilege-escalation patterns across their products. They built this themselves and have explicitly asked us to challenge and improve it, not just rubber-stamp it.

What You'll Actually Do (example Tasks)

- Join joint working sessions with the client's hands-on security engineers;
challenge and harden their AI-driven offensive pipeline end-to-end (recon → verification → AI-planned exploitation → sandboxed execution).




- Design and refine the exploitation agent: how the LLM plans attack paths, selects and validates exploits, and orchestrates parallel sandboxes safely and reproducibly.
- Optimise cost-per-finding of the existing exploitation pipeline: benchmark local / sovereign open models (Kimi, GPT-OSS, MiniMax, DeepSeek) against frontier models for the recon, exploitation and analysis loops;
quantify accuracy / latency / cost trade-offs and recommend hardware sizing.
- Shape the runtime anomaly-detection layer: define which intrusion / privilege-escalation precursor patterns are worth collecting (signal over raw-log volume), and design the missing pieces — automated response (kill a malicious process / disable an

📌 Ai Security Architect For Offensive Automation (Valencia)
🏢 Neurons Lab
📍 Valencia