The Project
Most security tooling is a drawer full of single-purpose blades — one tool to enumerate, another to exploit, a third to escalate, and a human stitching the whole chain together by memory and habit. I wanted something that held the whole engagement in its head: the methodology, the running notes, the dead ends already tried, and the discipline to verify a finding before claiming it.
So I built one. Internally I call it Harkonnen; on the outside it's the Bug Bounty Bot — an autonomous AI security operator that takes a sanctioned target from first contact through to a written, reproducible report. I run it on practice ranges, capture-the-flag events, security hackathons, and live bug-bounty programs.
This is not a script that runs a scanner and pastes the output. It's a reasoning loop with judgment, a memory, and guardrails — and a human operator (me) signing off on anything that touches a real system.
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What It Does
Works an engagement end to end — recon and enumeration, attack-surface mapping, exploitation, post-exploitation and privilege escalation, and finally a clear writeup. Each phase feeds the next instead of starting cold.
Remembers — the operator keeps structured, durable notes across a session: what was tried, what worked, what was ruled out, and why. Coming back to a target a day later means picking up the thread, not re-deriving it.
Verifies before it claims — a finding isn't "found" until there's evidence to back it. The system is built to distrust its own optimism, which is the difference between a real report and a pile of false positives.
Keeps a human in the loop — anything loud, destructive, or pointed at a live asset is surfaced for explicit human approval before it runs. Autonomy where it's safe; a hand on the wheel where it counts.
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Where I've Run It
- Sanctioned practice ranges and CTFs — full attack chains against intentionally vulnerable machines, including realistic enterprise identity and certificate-services scenarios and container-escape paths.
- Security hackathons — as a force multiplier under time pressure, where the bottleneck is usually how fast you can pivot, not how hard you can type.
- Live bug-bounty programs — against in-scope, authorized targets only, with the human-approval gate firmly in place.
(Methodology, internal architecture, and the specifics of the model and toolchain behind it are kept private — both because they're a competitive edge and because responsible disclosure norms apply.)
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Skills Demonstrated
- Offensive security — practical, hands-on exploitation across web, identity, and infrastructure attack surfaces, taken from recon all the way to a reproducible report
- AI agent engineering — building an autonomous reasoning loop with persistent memory, phased workflow, and self-verification rather than a one-shot prompt
- Safety and governance under autonomy — a human-approval gate on consequential actions, so the system is aggressive on practice targets and disciplined on real ones
- Operational judgment — knowing scope, staying in-bounds, and treating "authorized only" as a hard constraint, not a footnote
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Why It Matters
The interesting question in security right now isn't "can an AI hack things" — it's "can an AI do it responsibly, with memory, with verification, and with a human owning the consequential decisions." That's the line between a novelty and an operator you'd actually let near a real engagement.
The Bug Bounty Bot is my answer to that question, and I keep sharpening it on real, sanctioned work — labs, CTFs, hackathons, and live programs — because that's the only place the answer holds up. The capability is genuine; the restraint around it is the point.