Vulnerability Disclosure Program (VDP) / Bug Bounty Readiness (Internal)

Purpose:

• Stand up a predictable process for receiving, triaging, and fixing security reports.
• Run an internal “dry run” before you open anything to the public.

Repository policy baseline:

• SECURITY.md (reporting channels and response targets)

Inputs (Before You Start)

• A single intake channel with access controls:
  • GitHub Security Advisories (preferred) or a dedicated email queue
• A triage team roster with on-call coverage
• A severity rubric tailored to election risk:
  • integrity (outcome correctness)
  • availability (ability to vote / verify)
  • privacy (deanonymization / correlation)
  • disenfranchisement risk (false rejects, unfair rate limiting)
• A disclosure policy:
  • embargo windows
  • coordinated disclosure rules
  • what gets published when

What To Do

1. Define Scope and Safe Harbor (Even If Private)

Document what is in scope for testing:

• staging environments, demo environments, specific endpoints
• explicit exclusions (production, third-party dependencies, social engineering limits)

Safe harbor language should state:

• authorized good-faith testing within scope will not be pursued legally
• researchers must not exfiltrate real sensitive data

Expected output:

• vdp-policy.md (internal) that can later be adapted for public release.

2. Define SLAs and Roles

At minimum:

• acknowledgment SLA
• triage decision SLA
• patch ETA expectation by severity
• who can approve emergency changes

Expected output:

• triage-runbook.md with a single owner per step.

3. Internal Dry Run

Have internal engineers act as “external reporters”:

• submit reports in the intake channel with:
  • clear repro steps (safe)
  • impact statement
  • suggested fix

Triage team should:

• reproduce and confirm
• assign severity using the rubric
• create remediation tasks with deadlines
• communicate status updates on schedule

Expected output:

• a complete end-to-end rehearsal of intake -> fix -> disclosure.

4. Build a Fix-and-Retest Muscle

For each confirmed issue:

• patch with a regression test
• add a conformance vector if it was spec-related
• retest in staging
• write a short “lessons learned”

Expected output:

• a backlog that trends downward, with measurable time-to-fix.

What To Expect

• Duplicates, false positives, and low-quality reports (plan for it).
• High-severity findings that are operational (misconfig, key handling) rather than code.
• Reports that are “the spec is ambiguous” (treat as real work).

”Bypass” Mindset (Safe Guidance)

Assume reporters will probe:

• replay and race conditions
• parsing/canonicalization drift
• rate limiting and overload paths
• key rotation and trust chain weaknesses
• privacy/correlation fields in logs and receipts

Focus on building deterministic defenses and clear error behavior.

How To Patch

1. Patch the enforcement point, not the symptom:
   • if replay is possible, fix challenge lifecycle and binding, not just error text
2. Add a regression test or vector
3. Update documentation:
   • PRD / conformance / runbooks as needed
4. Track to closure with a retest timestamp and evidence link

What To Hand Off To Third Parties

• VDP policy + severity rubric
• Triage runbooks and SLAs
• A sanitized “recent findings” report (classes of issues, not sensitive details)
• Evidence that you can fix and retest quickly