docs: comprehensive security review findings for AI-powered educational platform

[bigweaverbeta]

Overview

This PR adds a comprehensive security findings report (SECURITY_FINDINGS_REPORT.md) documenting a thorough security assessment of the Course-tutor-DEV AI-powered educational platform.

Security Review Scope

The security review analyzed 14 critical security domains specific to LLM-based educational platforms:

Core Security Areas

• ✅ Authentication and authorization mechanisms (AWS Cognito, JWT, RBAC)
• ✅ Input validation and sanitization (LLM inputs/outputs)
• ✅ API security and endpoint protection
• ✅ Data handling and storage security (student data, course content)
• ✅ Session management and user access controls

LLM & Document Processing

• ✅ LLM-specific security risks (prompt injection, data leakage, model poisoning)
• ✅ Document processing security vulnerabilities
• ✅ File upload/download security

Infrastructure & Compliance

• ✅ Database security and SQL injection risks
• ✅ XSS and CSRF vulnerabilities
• ✅ Secrets management and credential exposure
• ✅ Infrastructure and deployment security configurations
• ✅ Dependency vulnerabilities and outdated packages
• ✅ Privacy compliance for educational data (FERPA/GDPR)

Key Findings Summary

Total Findings: 42 security issues identified

Severity	Count	Impact
🔴 CRITICAL	3	Immediate action required
🟠 HIGH	12	Priority remediation needed
🟡 MEDIUM	18	Should be addressed soon
🟢 LOW	9	Best practice improvements

Critical & High Priority Issues

Critical (Immediate Action Required)

1. CORS Misconfiguration - Wildcard "*" allows any origin, creating CSRF vulnerabilities
2. LLM Prompt Injection - No protection against malicious prompts or jailbreak attempts
3. Privacy Compliance Gap - Missing GDPR/FERPA compliance measures for educational data

High Priority

• Insufficient input validation for LLM interactions
• No malware scanning for uploaded documents
• Missing file size and type validation
• Outdated dependencies with potential CVEs
• Excessive JWT token expiration (30 days)
• No rate limiting on sensitive endpoints
• Missing CSP headers for XSS protection
• Inadequate logging for security events
• No output sanitization from LLM responses
• Client-side role checks without server validation

Report Contents

The comprehensive report includes:

• Detailed vulnerability descriptions for each finding
• Severity ratings (Critical, High, Medium, Low)
• Affected components with specific file locations
• Proof of concept code examples
• Remediation recommendations with code samples
• Priority levels for addressing findings
• Security strengths identified in the codebase
• Compliance considerations for educational data

Recommendations

Immediate Actions

1. Fix CORS configuration to whitelist specific origins
2. Implement prompt injection protection for LLM interactions
3. Add GDPR/FERPA compliance measures
4. Enable rate limiting on authentication and LLM endpoints
5. Implement malware scanning for file uploads

Short-term Actions

1. Reduce JWT token expiration to 1-4 hours
2. Update all outdated dependencies
3. Add comprehensive input validation
4. Implement CSP headers
5. Add output sanitization for LLM responses

Long-term Improvements

1. Implement comprehensive audit logging
2. Add automated security scanning in CI/CD
3. Create incident response procedures
4. Regular security training for developers
5. Establish security review cadence

Testing & Validation

This security review was conducted through:

• Static code analysis of all frontend and backend components
• Review of infrastructure-as-code (CDK) configurations
• Dependency vulnerability scanning
• API endpoint analysis
• Authentication and authorization flow review
• LLM integration security assessment
• Privacy compliance gap analysis

Documentation

The report is located at the repository root:

• File: SECURITY_FINDINGS_REPORT.md
• Format: Markdown with structured sections
• Size: Comprehensive 3000+ line report

Impact

This PR adds documentation only - no code changes are included. The report serves as:

• A baseline security assessment for the platform
• A prioritized roadmap for security improvements
• Reference documentation for future security reviews
• Compliance evidence for security due diligence

Next Steps

After merging this PR, the development team should:

1. Review and prioritize critical and high-severity findings
2. Create tracking issues for each vulnerability
3. Schedule remediation work in upcoming sprints
4. Establish security review procedures for new features
5. Set up automated security scanning tools

Notes

This security review aligns with the team's established practice of conducting dedicated security reviews as part of the development workflow. The findings and recommendations follow security best practices for AWS-based LLM applications and educational platforms handling sensitive student data.

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Review Checklist:

• ✅ Comprehensive security assessment completed
• ✅ All 14 security domains analyzed
• ✅ Findings documented with severity ratings
• ✅ Remediation steps provided with code examples
• ✅ Compliance considerations included
• ✅ Report follows documentation standards