Bug-Hunter Automation

Turn an agentic coding assistant into a disciplined discovery team that works from the first commit through to the running asset — source analysis on the left, autonomous runtime testing on the right, and a validation gate in the middle that files findings instead of noise.

One continuous loop, not three disconnected tools. Most teams bolt on security that never talks to itself: a static scanner in CI, a scanner-of-the-week against production, and a folder of disclosed-vulnerability write-ups nobody re-reads. This wires three open, capable engines into a single pipeline with clean handoffs, and puts an AI methodology layer in the middle that decides what to look for, whether a finding is real, and how it gets recorded.

Three engines, three points on the lifecycle. A source CRS doing static analysis, AI-guided fuzzing and multi-agent auto-patching before code ships. A runtime engine doing autonomous multi-agent testing of the live asset with a sandboxed toolset and cross-run memory that compounds per asset. And between them the methodology layer — the skill bundle that decides what to look for and whether a finding is real. They do not compete for one slot; the value is in the handoffs.

The part you are actually buying is the judgment, not the tool. The method is adapted from the recent wave of agentic-coding security skill bundles — loadable packs that turn a general assistant into a specialist that stays in scope. The guide uses one open-source bundle as its reference, then teaches the part that matters: how to strip it down and re-point it at your stack instead of someone else's enterprise.

It tells you where the tools stop working. Two honest limits make the automation trustworthy enough to run against your own estate. Guided fuzzers earn their reputation on memory-safety bugs in compiled languages — on a modern JavaScript/TypeScript edge stack there is almost nothing to fuzz, so the guide reserves that engine for native modules and reuses its patching pattern instead, rather than pretending a C fuzzer is testing your Node app. And external means external: to test what an internet attacker sees, the runtime engine has to run from outside your network, not from your office egress next to your admin identity — so the system splits into an operator plane and an external execution plane.

Ten sections: the core idea, the three-engine model described by capability rather than brand, dissecting a skill bundle into keep/adapt/drop, the six-phase loop (Scope → Recon → Hunt → Validate → Capture → Record), the validation gate, asset tiering for a hundred assets you cannot test equally, deployment topology, a seven-step prove-one-then-widen build path, operating principles, and rules of engagement.

Eight working appendices: the findings register SQL schema, the seven-question gate worksheet, the asset tiering matrix, a keep/adapt/drop worksheet, a deployment topology checklist, a fillable authorization record, a pre-flight safety checklist, and sources.

Authorization is a hard requirement, not a footnote. This describes offensive-capable automation. The validation gate at its core begins by asking whether the target is yours. Pointing these techniques at systems you do not own or are not contracted to test is a crime, and the guide includes the authorization record and pre-flight checklist to keep you inside the lines.

Sits between Pillar 02 — where findings come to rest — and Pillar 03, which proves the deployed asset. Part of the DIY builds of the CISO Marketplace AI Security Department; the managed version is delivered inside that program.