Research-driven advisories and reproducible security analysis targeting implementation flaws, parsing inconsistencies, signature verification edge cases, algorithm confusion, and supply-chain risk across high-impact ecosystems.
FVIL focuses on vulnerabilities that create real-world risk in security-critical systems: verification bypasses, split-brain behaviors, inconsistent parsing across APIs, catastrophic backtracking, resource exhaustion, and key-handling failures.
Security systems fail at boundaries: parsing, validation ordering, and cross-library assumptions. FVIL maps those boundaries to business impact — fraud enablement, policy bypass, and reliability failures.
Research-grade advisory format designed to be citeable, reproducible, and defensible for engineering, security, and audit audiences.
Comparative security analysis of verification behaviors across major Node.js JWT libraries, mapping inconsistent acceptance paths, spec violations, and high-impact edge cases.
Research stream for identifying “high-risk crypto paths” in npm dependency graphs with CI gating signals.
Systematic test matrix for JOSE behaviors: header ambiguity, canonicalization, key selection, critical header handling, and cross-library compatibility risks.
Domains are chosen for maximum downstream impact: identity verification, cryptographic correctness, dependency trust, and failure modes that break real security controls.
Verification order, key selection, header ambiguity, and spec-compliance edge cases.
Parsing inconsistencies, canonicalization, policy bypass between APIs and libraries.
High-risk crypto paths, wrappers, verification utilities, and dependency graph risks.
Catastrophic backtracking, resource exhaustion, and abuseable validation pipelines.
Mismatched semantics between components creating verification / authorization gaps.
Defensible evidence: integrity hashing, signing, provenance, audit-ready artifacts.
A deterministic pipeline designed to produce reproducible security claims — not “best practices” advice.
Define semantic expectations (spec + real systems) and build a cross-library acceptance matrix for inputs and edge cases.
Build minimal harnesses that generate controlled mutations: header ambiguity, canonicalization breaks, DER edge cases, and timing/DoS paths.
Produce proof artifacts: inputs, outputs, logs, versions, environment, and a short reproduction script for maintainers.
A defensible, audit-ready chain of events for coordinated disclosure — from initial report to remediation and public write-up.
Need a disclosure-ready report template (H1/GitHub) with timeline, reproducibility, and executive impact framing?
FVIL follows coordinated disclosure when appropriate, prioritizing user safety while maintaining reproducible scientific records.
For coordinated disclosure, verification questions, or advisory clarifications: