A few days before leHack 2026 I gave a talk at the HackTheBox Meetup on AI pipelines for bug bounty. Here is the whole deck, plus a detailed walkthrough of every technical slide with code, sourced numbers, and links to the research behind each idea.
In Part I, I caught secrets by knowing the shape of secretsdump, of netexec, of hashcat. A reader could fairly ask: are you doing research, or are you writing regexes forever? They would be right. This part inverts the whole thing. You cannot enumerate every secret format in the world, and some secrets have no format at all, so stop trying. Keep only what is provably generic (dictionary words, numbers, dates, protocol constants) and tokenize the rest by default. I measure it live on HTB Shibuya through a full root chain, then across ten Active Directory machines, with no per-tool rules at all, and I situate it against the literature, which turns out to have a clear closest neighbor and a clear gap.
I want an AI agent that can do offensive and defensive security work without ever leaking a credential, a hostname, an IP or a domain to the model provider, and to keep that property no matter which provider sits behind the API. This is Part I of the research. It covers the threat model, the state of the art, the core mechanism (bidirectional tokenization with host-side resolution), and four experiments that run on real HackTheBox machines, including an autonomous agent that drives a real domain controller while seeing nothing but opaque tokens.
Two weeks ago I published a deep dive on prompt engineering for security research. This article is about everything that lives one layer above the prompt: the hooks, MCPs, subagents, scope guards, and validators that make those prompts viable in a real bug bounty workflow. Six axes, sourced numbers, and an honest before-and-after between my first attempt (27 slash commands, a 74k-vuln knowledge base, one monolithic configuration) and the rewrite (8 to 12 skills, no embeddings, hard caps everywhere, a deterministic validator MCP at the gate).
Most people use LLMs for security wrong. They ask ‘find all bugs’ and get noise. This article breaks down the empirical research behind what actually works: structured prompting, adversarial self-verification, CWE-specialized chains, context engineering, and the full composite prompt template that gets you from noise to actionable findings. With numbers.
