Part 1 of Industrializing Security Research .
In What’s Left When the World Becomes Agentic? , I left one sentence hanging:
The agent (well, it’s no longer one agent, but that’s its own series of posts coming up) reads the archive, learns the shape of it, and reuses it.
This is the beginning of that series.
My first attempt was not a multi-agent system. It was a suite of skills that one agent could invoke during an interactive security session.
The suite covered the full research lifecycle:
context-building mapped the attack surface.crash-surface generated node-crash and denial-of-service hypotheses.consensus-invariants looked for ways honest nodes could disagree.exploitation turned a candidate into a proof.adversarial-review, deduplication, and scope triage challenged the completed finding.Every technique was useful. Making all of them available to every researcher was not.
A focused prompt did not restrict which skills the agent could use. A node-crash hunter could:
Steps 1 and 3 support the crash hunt. Step 2 changes the research technique. Step 4 crosses into post-processing. Each transition looked reasonable on its own, but the sequence turned a focused crash hunter into a general researcher reviewing its own work.
The difference from coding is structural.
A hunter must build the proof. Without it, the agent has found a suspicion, not a vulnerability.
I was not trying to make an LLM deterministic. Different runs should explore different paths and may find different bugs.
I wanted a higher probability that the work I requested was the work performed:
The hunter roles can run in parallel. Triage is a conditional next step: it starts only if those researchers submit hypotheses to check.
Each role needs a bounded context, the techniques required for its job, and a clear evidence standard. The result can vary. The method should remain recognizable.
Unexpected work is not free exploration when it leaves the intended attack surface uncovered.