A security researcher starts with data flow, not a payload checklist

In a GitHub interview, security researcher r31n describes tracing attacker-controlled input through transformations and trust boundaries into sensitive operations. They favor understanding one target deeply, reproducing techniques and automating specific repetitive tasks. GitHub’s bounty team emphasizes working demonstrations and clear impact. The advice is a research workflow, not a new vulnerability disclosure or evidence that a particular tool finds more bugs.

GitHub · Oct 22, 2026

Why it matters

The same data-flow thinking helps engineers review integrations and AI features before a report arrives.

More context

Practice on an authorized lab: identify input sources, sensitive operations and the controls between them, then create a minimal reproducible test. The researcher also treats model output as untrusted input when another system consumes it.

Keep in mind

This is one practitioner’s account and GitHub’s program perspective. Work within the target’s published scope; a plausible chain still needs validation before it becomes a finding.

Original read