Skip to main content

Track R: Research & Analysis

Progress: 23/23 tasks complete (100%)

All research tasks were completed by the automated research pipeline v2.0.0 on 2026-02-18. The pipeline produced 23 artifacts across five research sections, culminating in the SDD, TDD, and Executive Summary that serve as the authoritative design inputs for Track D development.

Evidence: Research pipeline v2.0.0, 23 artifacts generated 2026-02-18

Status Summary​

SectionDoneTotalStatus
R.1 Ecosystem Discovery55Complete
R.2 Code Analysis77Complete
R.3 Security Assessment44Complete
R.4 Architecture Design44Complete
R.5 Documentation33Complete

R.1 Ecosystem Discovery​

Identification and triage of ClawGuard ecosystem repositories.

  • R.1.1 Search GitHub for all clawguard forks and variants — identified 5 repositories
  • R.1.2 Rank repositories by code volume, commit activity, and test coverage — scored on 0-100 scale
  • R.1.3 Identify and quarantine malicious fork — lauty1505/clawguard flagged as trojanized, contains Software-tannin.zip binary payload; permanently removed from submodules
  • R.1.4 Eliminate no-code repositories — yourclaw/clawguard removed (planning scaffolding only, no functional code)
  • R.1.5 Confirm MIT licensing on retained repositories — ClawGuardian (superglue-ai), clawguard (JaydenBeard), ClawGuard (maxxie114) all confirmed MIT

Evidence: Research pipeline v2.0.0; lauty1505 flagged in executive-summary.md Appendix B

R.2 Code Analysis​

Deep technical analysis of the three retained repositories.

  • R.2.1 Analyze ClawGuardian (superglue-ai) architecture — TypeScript, hook-based, before_agent_start / before_tool_call / tool_result_persist lifecycle model, 45,205 lines of vitest tests, score: 85/100
  • R.2.2 Analyze clawguard (JaydenBeard) pattern library — 55+ risk patterns across 5 severity tiers, JSONL session log watcher, WebSocket dashboard, kill switch; vanilla JavaScript, zero tests, score: 78/100
  • R.2.3 Analyze ClawGuard (maxxie114) sanitization pipeline — Python/FastAPI, 10 prompt injection patterns, 6 secret detection patterns, 0-100 risk scoring, SQLite EventStore, email/Gmail scope; score: 65/100
  • R.2.4 Extract ClawGuardian pattern modules — PII (patterns/pii.ts), API keys, cloud credentials (patterns/cloud-credentials.ts), destructive command detector (destructive/detector.ts)
  • R.2.5 Enumerate JaydenBeard destructive command patterns — 11 critical, 30+ high, 20+ medium; covers rm -rf, curl | sh, sudo, disk format, cloud CLI destructive ops, persistence mechanisms
  • R.2.6 Enumerate JaydenBeard sensitive path patterns — 30+ entries covering .ssh, .aws, .kube, .env, password managers, cloud configuration stores
  • R.2.7 Document maxxie114 risk scoring algorithm — additive model, CRITICAL=80 points, HIGH=40, MEDIUM=20, LOW=5, INFO=1; capped at 100; co-occurrence bonuses

Evidence: Research pipeline v2.0.0 artifacts; SDD Appendix A pattern library summary; TDD Appendix A pattern index

R.3 Security Assessment​

Security risk evaluation of the ecosystem and its applicability to CODITECT.

  • R.3.1 Perform supply chain threat assessment — trojanized fork attack confirmed active; lauty1505 repo exploits trust in "security tooling" category to social-engineer binary download
  • R.3.2 Identify CODITECT-specific threat surfaces — prompt injection via tool inputs, secret exfiltration via tool outputs, destructive command execution (Bash tool), PII leakage, lateral movement across tenant boundary
  • R.3.3 Map ClawGuard patterns to CODITECT threat model — all five threat surfaces covered by combined pattern library from three repositories; gaps identified in agent-to-agent trust and long-running session hijacking
  • R.3.4 Assess pattern freshness risk — static pattern lists identified as ongoing maintenance liability; versioned YAML registry with defined update process recommended

Evidence: Executive Summary Sections 2, 4; SDD Section 11 threat model

R.4 Architecture Design​

Architectural design decisions for the CODITECT-native implementation.

  • R.4.1 Design SecurityGateHook component — PreToolUse / PostToolUse / PreAgentStart hook handlers; synchronous enforcement; fail-closed default; tenant-aware; SDD Section 3.1
  • R.4.2 Design PatternEngine with YAML rule format — 80+ rules across 5 categories in version-controlled YAML files; hot-reload without restart; LRU compiled regex cache; SDD Section 3.2
  • R.4.3 Design RiskAnalyzer scoring model — deterministic 0-100 numeric score; severity category assignment (CRITICAL/HIGH/MEDIUM/LOW/INFO); co-occurrence bonuses; tenant allowlist discounts; SDD Section 3.3
  • R.4.4 Design ActionRouter with five-level taxonomy — BLOCK / REDACT / CONFIRM / WARN / LOG; tenant action overrides (CRITICAL hard-coded as non-overridable); SDD Section 3.4

Evidence: SDD v1.0.0, SDD-CODITECT-SEC-001; TDD v1.0.0

R.5 Documentation​

Research output artifacts produced by the pipeline.

  • R.5.1 Write Executive Summary (Artifact 3 of 5) — CTO/VP Engineering decision brief; CONDITIONAL GO recommendation; decision factors matrix; docs/original-research/executive-summary.md
  • R.5.2 Write Software Design Document (SDD-CODITECT-SEC-001) — 14-section design specification covering all six components, data flows, API specs, database schema, failure modes, observability, implementation plan; docs/original-research/sdd.md
  • R.5.3 Write Technical Design Document (TDD v1.0.0) — TypeScript interface definitions, hook registration format, pattern YAML schemas, dashboard routes, daemon architecture, performance characteristics; docs/original-research/tdd.md

Evidence: All three documents present in docs/original-research/; Research pipeline v2.0.0, 23 artifacts total