Bump werkzeug from 1.0.0 to 3.1.6 in /apps/securetrails-vulnerable#5
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…ards
✨ Repository Reorganization:
- Moved all exercises from .github/skills/ to top-level docs/ folder
- Reorganized vulnerable apps into apps/ directory
- Created professional root-level README with comprehensive guide
- Added docker-compose.yml for local reproducibility
- Added CONTRIBUTING.md for collaboration guidelines
- Added LICENSE (MIT) for open-source clarity
📁 New Structure:
docs/ (All workshop exercises)
├── 0-prereqs.md
├── 1-agent-security-review.md
├── 2-mcp-supply-chain.md
├── 3-secret-scanner-agent.md
├── 4-sdlc-policy-agents.md
├── resources/ (Reference materials)
└── images/ (Screenshots & diagrams)
apps/ (Sample vulnerable applications)
└── securetrails-vulnerable/
.github/agents/ (Custom security agents)
.github/workflows/ (CI/CD pipelines)
Root files:
├── README.md (Professional main documentation)
├── docker-compose.yml (Local environment)
├── CONTRIBUTING.md (Contribution guidelines)
└── LICENSE (MIT License)
📖 Improvements:
- Enhanced README with story, objectives, prerequisites table
- Added exercise difficulty ratings and time estimates
- Included repository structure documentation
- Added multiple learning paths (beginner/intermediate/advanced)
- Comprehensive resource links and quick-start guide
- Professional styling matching industry workshop standards
🎯 Maintained:
- All exercise content and technical accuracy
- Agent implementations and workflows
- Vulnerable application setup
- Learning progression and flow
Follows best practices from:
- CanarysAutomations/app-modernization-workshop
- GitHub Copilot workshop standards
- Professional open-source conventions
…oach ❌ REMOVED (theoretical/non-existent): - gh copilot agent register command - Fake SDK-based agent chaining - Aspirational multi-agent framework ✅ REPLACED WITH (real working patterns): - Direct Python script execution shows actual vulnerability detection - File-based JSON data passing between agents - Real orchestration via shell scripts - GitHub Actions workflow integration - Pre-commit hook configuration Changes: - Agents now shown as standalone Python executables - Context passing via findings.json files instead of abstract SDK calls - Run orchestration with shell scripts (Unix pipeline approach) - Pre-commit hook setup for local blocking - More honest about current (2026) Copilot capabilities Impact: Exercise 3 now demonstrates what actually works today while showing how multi-agent orchestration COULD work with file/JSON-based coordination.
…s workflow ❌ REMOVED (theoretical): - Complex YAML policy file definitions - Aspirational multi-agent SDKframework - Fictional policy enforcement engine ✅ REPLACED WITH (real working patterns): - GitHub Actions workflow orchestration - JSON file-based data passing between agents - Real exit code decision logic (block, warn, pass) - Actual PR commenting and issue creation - Enterprise workflow architecture diagram Changes: - Agents now shown running via GitHub Actions jobs - Real conditional logic based on findings.json - Policy decisions implemented as exit codes (0=pass, 1=block) - Practical enterprise patterns that work today - Test script showing local workflow simulation Impact: Exercise 4 now demonstrates production-ready automation using only GitHub-native features + coordinated Python agents
Replace hardcoded issue template with 3 flexible options: ❌ REMOVED: - Hardcoded 7 vulnerabilities in issue template - Assumes all candidates find same issues - Non-realistic for actual CLI interactions ✅ ADDED (3 approaches): 1. AUTO-GENERATE (Recommended): - Python script parses agent output dynamically - Creates issue with actual findings - Works for any number of vulnerabilities - Groups by severity automatically 2. MANUAL FALLBACK: - Simple approach if script fails - References actual agent output 3. REFERENCE OPTION: - Understand what agent found first - Manual issue creation with real data Key improvements: - Acknowledges findings vary per candidate - No false expectations about exact results - Each candidate gets honest assessment of THEIR findings - Works whether they find 3, 5, 7, or different vulnerabilities - Better reflects real CLI interaction patterns This addresses the feedback that hardcoded templates don't match actual agent output which varies between runs and environments.
…eal agent execution ❌ REMOVED (theoretical/non-existent): - MCP server configuration setup (.copilot/mcp-config.json) - gh copilot agent register with agent-path - gh copilot agent run commands - Fake MCP tool discovery (search_code, search_issues, etc.) - Aspirational agent chaining via SDK - SBOM generation via MCP queries ✅ CLARIFIED: - File types: .md (instructions) vs .py (agent code) - Exercise files are .md for you to READ - Agent files are .py - actual Python executables - This is a MARKDOWN file with INSTRUCTIONS to follow - The agents are Python scripts you RUN, not register ✅ REPLACED WITH (real working approach): - Direct Python agent execution: python .github/agents/dependency-scout.py - Real CVE detection patterns in the .py agent file - Actual SBOM generation from agent output - JSON-based findings - Real GitHub issue creation from findings - No fake CLI commands or SDK calls Key improvements: - Honest about what works: Python agents as standalone executables - Clear distinction: .md exercises you read, .py agents you run - Realistic approach: Candidates just run Python scripts - No confusion about non-existent Copilot SDK features - Same real vulnerability detection, different approach This fixes the same issue as Exercise 3 had - removed aspirational SDK features and showed what actually works in 2026.
… suggest - Replace 3 complex options (auto-script, fallback, manual) with single workflow - New approach: Run agent → Show Copilot findings → Copilot generates gh issue command - Much simpler UX: No helper scripts, just conversation - Aligns with realistic workflows: Humans + AI collaboration - Uses actual participant findings (no templates)
…e mode (v0.0.414+) - Replace outdated 'gh copilot suggest/explain' commands with current 'copilot' interactive CLI - Show slash commands: /login, /model, /autopilot, /exit, /help - Demonstrate real interactive conversation pattern (Steps 1-5) - Update Step 6 issue creation to use Copilot to generate gh issue create command - Update acceptance criteria and troubleshooting for new CLI - Fix Learning Objectives to reflect current tool capabilities - Update resources with links to current Copilot CLI repo and docs (v0.0.414) - All commands now show ACTUAL working tools, not aspirational vaporware - Users can follow exact workflow: copilot → ask → analyze → generate → run
Exercise 2 (Supply Chain): - Condensed learning objectives to 3 key items - Removed verbose file type explanations - Simplified scenario to one sentence - Reduced expected output examples - Consolidated 5 steps into 3 focused steps - Removed ~200 lines of unnecessary explanation Exercise 3 (Secret Detection): - Condensed from 637 lines to ~120 lines - Removed verbose 'SDK' references (incorrect - it's Python agents) - Cut out 10+ steps of complex workflows - Kept only essential agent execution - Removed pre-commit hook complexity - Focus on core security scanning functionality Exercise 4 (SDLC Automation): - Completely rewritten from 1217 lines to ~180 lines - Removed excessive workflow examples and diagrams - Cut hundreds of lines of verbose explanations - Kept only essential orchestration pattern - Focus on GitHub Actions + agent coordination - Added clear pattern visualization RESULT: All exercises now: ✅ Precise and crisp (no fluff) ✅ Focus on hands-on execution ✅ Use current working tools (Copilot CLI, Python agents, GitHub Actions) ✅ Easy to follow for busy practitioners ✅ Complete in actual stated time (15-20 min each)
ISSUE DIAGNOSED & FIXED: User feedback: 'You're forgetting the main idea behind this workshop' Problem: Over-condensing exercises lost the agent creation/modification focus Previous state: Exercises only showed how to RUN agents, not BUILD them New state: Exercises teach USE → UNDERSTAND → MODIFY → BUILD agents EXERCISES COMPLETELY REDESIGNED: ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Exercise 1 (Security Review) - ENHANCED: ✅ Added 'Agent Deep Dive' showing baseline-checker.py internals ✅ Explained regex patterns for SQL injection, XSS, secrets ✅ Added 'Hands-On: Modify Patterns' to enhance agent detection ✅ Show participants HOW agents work (not just that they work) Exercise 2 (Supply Chain) - ENHANCED: ✅ Added 'Agent Deep Dive' showing dependency-scout.py CVE logic ✅ Explained vulnerability database architecture ✅ Added hands-on: extend CVE database with new entries ✅ Taught participants to CUSTOMIZE agent behavior Exercise 3 (Secrets) - ENHANCED: ✅ Added 'Agent Deep Dive' showing entropy calculation + patterns ✅ Explained two detection methods: Pattern matching + Entropy ✅ Added hands-on: add new credential patterns (GitHub token, Stripe) ✅ Participants learn credential detection techniques Exercise 4 (SDLC Automation) - COMPLETELY REWRITTEN: ✅ Added 'Agent Orchestration Pattern' showing data flow ✅ Explained JSON data passing between agents ✅ Added hands-on: modify policy to block HIGH severity ✅ Showed real agent chaining architecture (not theory) Exercise 5 (NEW - BONUS) - TEACHES AGENT CREATION: ✅ Complete agent template with explanations ✅ Step-by-step: build database-encryption-checker agent ✅ Teach agent anatomy: input → detect → output → exit code ✅ Teach composition: how agents call other agents ✅ Show advanced: composite agents orchestrating 3+ agents ✅ Demonstrates patterns that work in production README & LEARNING PATHS - UPDATED: ✅ Fixed objectives to emphasize BUILDING agents (not using) ✅ Added Exercise 5 to table + clear difficulty progression ✅ Created 3 learning paths: - Beginner: Learn to USE agents (60 min) - Intermediate: Understand & MODIFY agents (100 min) - Advanced: BUILD YOUR OWN agents (150 min) ✅ Added 'What You'll Build' section showing agent pattern ✅ Clarified this teaches CUSTOM PYTHON AGENTS (not aspirational frameworks) CORE VALUE PROPOSITION RESTORED: ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Workshop now teaches: 1. How to USE Copilot CLI for analysis (Ex 1) 2. How agents DETECT vulnerabilities (Ex 1-3 deep dives) 3. How to MODIFY detection patterns (Ex 1-3 hands-on) 4. How to BUILD custom agents (Ex 5) 5. How to ORCHESTRATE agents in CI/CD (Ex 4-5) This is what participants getting: - ✅ Real working Python agents (not vaporware) - ✅ Agent internals explained with real code - ✅ Hands-on modification of existing agents - ✅ Complete template for building new agents - ✅ Production-ready patterns for agent composition WORKSHOP QUALITY METRICS: ✅ Honest about what actually works (Copilot CLI, Python, GitHub Actions) ✅ Each exercise shows source code to participants ✅ Hands-on in every exercise (not just reading) ✅ Learn by doing + understanding internals ✅ Extensible - participants can build their own
Addressed user question: 'So far agents as .md, you created .py - does it work?' CLARITY PROVIDED: ✅ YES - Agents ARE real Python executable scripts (.py files) ✅ YES - They DO work (tested: python baseline-checker.py works) ✅ YES - Copilot CLI CAN help create them via prompts ✅ YES - They output valid JSON for CI/CD orchestration CLARIFIED THE PATTERN: Not a complex 'agent framework' or SDK: Input → Scan (regex/logic) → JSON → Exit code → GitHub Actions UPDATED EXERCISE 5: - Added 'Important Clarity' section explaining what agents really are - Changed Step 1 from 'copy-paste code' to 'Use Copilot CLI to generate' - Show workflow: Launch copilot → prompt → get code → save → test - This teaches participants to CREATE agents, not copy templates PROVEN WORKING: - Ran baseline-checker.py on SecureTrails vulnerable app - Agent successfully detected vulnerabilities - Output valid JSON with findings - Ready for GitHub Actions integration KEY INSIGHT: This is better than aspirational SDK frameworks because: ✓ Simple Python scripts anyone can read/modify ✓ No framework/vendor lock-in ✓ Easy for Copilot to generate/improve ✓ Production-proven pattern ✓ Composable with simple piping
…ystem Approach USER DECISION: Option 3 - Show the complete security automation ecosystem NEW WORKSHOP FOCUS: ✅ GitHub NATIVE Security (GHAS, CodeQL, secret scanning, Dependabot) ✅ Copilot CLI Interactive Analysis (for real-time security review) ✅ Custom Detection Tools (Python-based extensions when GHAS insufficient) ✅ GitHub Actions Orchestration (bringing all three together) WHAT THIS MEANS: - Be HONEST about what's GitHub's built-in vs what we're building - Stop using 'agents' terminology for custom Python scripts - Clear 'NATIVE' vs 'CUSTOM' labeling throughout workshops - Teach when to use each tool in real security scenarios EXERCISES WILL BE REDESIGNED: Ex0: Setup Ex1: GitHub NATIVE GHAS features (CodeQL, secrets, Dependabot ONLY) Ex2: Copilot CLI for INTERACTIVE analysis (real conversations) Ex3: Custom detection tools (be honest these are extensions) Ex4: GitHub Actions integration (orchestrate all components) Ex5: Real-world ecosystem scenario (all 4 working together) RATIONALE: This approach: ✓ Shows what GitHub provides free (enterprise-ready) ✓ Shows how Copilot CLI adds interactive analysis dimension ✓ Teaches building extensions when native insufficient ✓ Integrates everything into production workflow ✓ Is completely honest - no aspirational frameworks ✓ Teaches real security automation that works in 2026 STATUS: ✓ Direction finalized ✓ Redesign plan saved to memory ✓ README updated with honest direction ⏳ Next: Rewrite exercises following ecosystem pattern
MAJOR CHANGE: Exercise 1 now focuses ONLY on GitHub's built-in GHAS features SCOPE CHANGE: ❌ REMOVED: Copilot CLI interactive analysis (moved to Exercise 2) ❌ REMOVED: Custom Python agents/detection tools (moved to Exercise 3) ✅ ADDED: CodeQL automatic scanning ✅ ADDED: Secret Scanning built-in patterns ✅ ADDED: Dependabot vulnerability alerts ✅ ADDED: GitHub Security tab navigation NEW EXERCISE STRUCTURE: Step 1: Enable GitHub Advanced Security (GHAS) Step 2: Run CodeQL Analysis (finds code vulnerabilities) Step 3: Review CodeQL Results (SQL injection, XSS, weak crypto) Step 4: Review Secret Scanning (hardcoded credentials) Step 5: Review Dependabot Alerts (vulnerable packages) Step 6: Create GitHub issue from all findings FINDINGS SHOWN (Real, from SecureTrails vulnerable app): - CodeQL: SQL injection, XSS, weak cryptography (GITHUB FINDS THESE) - Secrets: GitHub tokens, AWS keys, DB passwords (GITHUB FINDS THESE) - Dependabot: Flask 1.1.0 (2 CVEs), SQLAlchemy 1.3.0 (1 CVE), requests 2.24.0 (1 CVE) KEY MESSAGE: 'GitHub provides these security features FOR FREE. Understand what's native before building custom tools. This is Exercise 1 - GitHub capabilities alone.' TERMINOLOGY: ✅ No 'agents' - no custom tools yet ✅ No Copilot CLI - that's Exercise 2 ✅ Pure GitHub features - what comes built-in NEXT EXERCISES: Exercise 2: Copilot CLI interactive analysis (real-time, conversational) Exercise 3: Custom detection tools (when GitHub GHAS isn't enough) Exercise 4: GitHub Actions orchestration (combine all three) Exercise 5: End-to-end ecosystem (real scenario)
…Ecosystem) structure
…ent client confusion
…via Copilot CLI to fix vulnerabilities
… Single source of truth in docs/
…curetrails-vulnerable/
…nstallation links
…, efficient prompts for optimal output
…ne 'copilot' CLI tool (v0.0.414+)
…stallation, remove gh extension references
… GHAS walkthrough
… made key learnings generic
…named Step 2 for Code Quality insights
…a/quick-fix/troubleshooting
…ls context), removed unnecessary details
Bumps [werkzeug](https://github.com/pallets/werkzeug) from 1.0.0 to 3.1.6. - [Release notes](https://github.com/pallets/werkzeug/releases) - [Changelog](https://github.com/pallets/werkzeug/blob/main/CHANGES.rst) - [Commits](pallets/werkzeug@1.0.0...3.1.6) --- updated-dependencies: - dependency-name: werkzeug dependency-version: 3.1.6 dependency-type: direct:production ... Signed-off-by: dependabot[bot] <support@github.com>
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Bumps werkzeug from 1.0.0 to 3.1.6.
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04da1b5release version 3.1.6f407712Merge commit from forkf54fe98safe_join prevents Windows special device names in multi-segment pathsd005985start version 3.1.68565c2cdocument rule priority (#3102)3febc7edocument rule priority2525b82remove state machine docs4abfbd5rewrite build docstring (#3097)161c18brewrite build docstring86e11c2release version 3.1.5 (#3085)You can trigger a rebase of this PR by commenting
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