# Design Document

## Overview

The Security Module provides comprehensive security governance through a self-contained `security/` directory structure. This design establishes automated vulnerability tracking, structured remediation planning, and real-time security documentation. The module operates as a cohesive system where findings, remediation plans, and historical data are automatically synchronized and maintained.

## Architecture

### Final Directory Structure

```
security/
├── findings/
│   ├── SECURITY_FINDINGS.md          # Live findings document (auto-generated)
│   ├── remediation-plans.yml         # Structured remediation datastore
│   └── history/                      # Historical tracking and audit trails
│       ├── findings-changelog.md     # Chronological finding changes
│       └── remediation-timeline.yml  # Remediation progress tracking
├── reports/
│   ├── latest/                       # Current scan outputs
│   │   ├── pip-audit.json
│   │   ├── bandit.json
│   │   ├── secrets-scan.json
│   │   └── dependency-tree.txt
│   └── archived/                     # Historical scan data
│       └── YYYY-MM-DD/              # Date-organized archives
├── scripts/
│   ├── update-findings.py            # Core automation engine
│   ├── sync-remediation.py           # Remediation synchronization
│   └── generate-reports.py           # Document generation
├── config/
│   ├── scanner-settings.yml          # Scanner configurations
│   ├── findings-template.md          # Document templates
│   └── remediation-defaults.yml      # Default remediation values
└── README.md                         # Module documentation and usage
```

### Migration Strategy

The system includes migration capabilities to transition from existing security infrastructure:

#### Legacy Integration
- **Existing Reports**: Migrate `security-reports/` content to `security/reports/archived/`
- **Script Updates**: Update `scripts/security_check.sh` to use new directory paths
- **Workflow Integration**: Modify CI/CD workflows to output to `security/reports/latest/`
- **Data Preservation**: Maintain historical scan data and findings during transition

#### Migration Process
1. **Data Migration**: Move existing security reports to appropriate archived locations
2. **Path Updates**: Update all scripts and workflows to use new `security/` structure
3. **Validation**: Verify migrated data accessibility and script functionality
4. **Cleanup**: Remove legacy directories after successful migration

### System Behavior

```mermaid
graph TD
    A[Security Scanners] --> B[security/reports/latest/]
    B --> C[update-findings.py]
    C --> D[security/findings/SECURITY_FINDINGS.md]
    C --> E[security/findings/remediation-plans.yml]
    E --> F[Manual Remediation Updates]
    F --> E
    C --> G[security/findings/history/]
    
    H[CI/CD Workflow] --> A
    H --> I[Check for Changes]
    I --> J[Automated Git Commit]
    J --> K[Push to Repository]
    
    L[Legacy security-reports/] -.-> M[Migration Script]
    M --> N[security/reports/archived/]
    M --> O[Update scripts/security_check.sh]
    P[Manual Security Review] --> F
```

## Components and Interfaces

### Live Findings Document

**Location**: `security/findings/SECURITY_FINDINGS.md`

**Structure**:
```markdown
# Security Findings Summary

**Last Updated**: 2025-10-24 14:30:00 UTC  
**Total Active Findings**: 3  
**Days Since Last Scan**: 0  

## Current Findings

### High Severity

#### CVE-2025-8869 - Arbitrary File Overwrite in pip
- **Package**: pip 25.2
- **Source**: pip-audit
- **Discovered**: 2025-10-24 (1 days active)
- **Description**: Fallback extraction path allows malicious sdist to escape target directory and overwrite arbitrary files during pip install
- **Impact**: Arbitrary file overwrite outside build directory, potential code execution
- **Reference**: https://github.com/advisories/GHSA-4xh5-x5gv-qwph
- **Fix Available**: Yes (pip 25.3 planned)
- **Remediation**: Waiting for upstream release
- **Planned Fix Date**: 2025-11-15
- **Assigned To**: security-team
- **Workaround**: None - requires malicious sdist installation

### Medium Severity

#### CVE-2022-42969 - ReDoS in py Library
- **Package**: py 1.11.0  
- **Source**: pip-audit
- **Discovered**: 2025-10-23 (2 days active)
- **Description**: Regular expression Denial of Service attack via crafted Subversion repository info data
- **Impact**: Remote DoS attack, service disruption
- **Reference**: https://nvd.nist.gov/vuln/detail/CVE-2022-42969
- **Fix Available**: No (library deprecated)
- **Remediation**: Replace with modern alternative
- **Planned Fix Date**: 2025-10-30
- **Assigned To**: dev-team
- **Workaround**: Limit Subversion repository access

## Remediation Summary

**Planned Actions**: 2  
**Awaiting Upstream**: 1  
**In Progress**: 1  
**Completed This Month**: 0  
```

### Remediation Registry

**Location**: `security/findings/remediation-plans.yml`

**Structure**:
```yaml
findings:
  CVE-2025-8869:
    finding_id: "CVE-2025-8869"
    package: "pip"
    version: "25.2"
    severity: "high"
    remediation:
      status: "awaiting_upstream"
      planned_action: "Upgrade to pip 25.3 when released"
      target_date: "2025-11-15"
      assigned_to: "security-team"
      notes: "Official fix planned for pip 25.3 release"
      workaround: "None - vulnerability requires malicious sdist"
    
  CVE-2022-42969:
    finding_id: "CVE-2022-42969"
    package: "py"
    version: "1.11.0"
    severity: "medium"
    remediation:
      status: "in_progress"
      planned_action: "Replace py library with modern alternative"
      target_date: "2025-10-30"
      assigned_to: "dev-team"
      notes: "Library is deprecated, evaluating pytest-xdist migration"
      workaround: "Limit Subversion repository access"

defaults:
  new_finding:
    status: "new"
    planned_action: "Under evaluation"
    target_date: null
    assigned_to: "security-team"
    notes: "Newly discovered - assessment in progress"
    workaround: "None identified"
```

### Reports Storage

**Location**: `security/reports/`

**Organization**:
- `latest/` - Current scan outputs, overwritten on each run
- `archived/YYYY-MM-DD/` - Historical scan data for trend analysis
- Standardized filenames: `pip-audit.json`, `bandit.json`, `secrets-scan.json`

## Data Models

### Finding Record

```python
@dataclass
class SecurityFinding:
    finding_id: str          # CVE-2025-8869, GHSA-xxx, etc.
    package: str             # Affected package name
    version: str             # Vulnerable version
    severity: str            # high, medium, low, info
    source_scanner: str      # pip-audit, bandit, secrets
    discovered_date: date    # When first detected
    description: str         # Vulnerability description
    impact: str              # Business/technical impact
    fix_available: bool      # Whether fix exists
    fix_version: Optional[str] # Version that fixes issue
    cvss_score: Optional[float] # CVSS severity score
    reference_url: Optional[str] # Link to detailed vulnerability information
```

### Remediation Plan

```python
@dataclass
class RemediationPlan:
    finding_id: str          # Links to SecurityFinding
    status: str              # new, in_progress, awaiting_upstream, completed
    planned_action: str      # Description of planned fix
    target_date: Optional[date] # Expected completion
    assigned_to: str         # Responsible team/person
    notes: str               # Additional context
    workaround: str          # Temporary mitigation if any
    priority: str            # critical, high, medium, low
    business_impact: str     # Impact on business operations
```

## Automated Repository Updates

### Git Integration with Race Condition Prevention

**CRITICAL UPDATE**: The security findings system now implements proper Git rebase logic to prevent race conditions, as defined in the Rebase Fix Phase.

The security findings system automatically commits changes to maintain repository state synchronization:

#### Change Detection
- **File monitoring**: Check for modifications in `security/findings/` and `security/reports/`
- **Content comparison**: Use git diff to detect actual changes vs. timestamp updates
- **Selective commits**: Only commit when substantive changes are detected

#### Race Condition Prevention
- **Workflow coordination**: Use GitHub Actions concurrency groups to prevent simultaneous execution
- **Git rebase logic**: Always use `git pull --rebase origin main` before pushing changes
- **Conflict resolution**: Automatically resolve timestamp-only conflicts in security files
- **Linear history**: Maintain clean commit history without merge commits

#### Automated Commits with Rebase
- **Commit scope**: Include all security findings and reports in single atomic commit
- **Rebase before push**: Sync with latest changes using rebase logic
- **Conflict handling**: Automatically resolve timestamp conflicts, fail on content conflicts
- **Commit messages**: Use structured format identifying automated security updates
- **Author attribution**: Use GitHub Actions service account for automated commits
- **Branch targeting**: Commit directly to main branch after successful rebase

#### Commit Message Format
```
chore(security): automated security findings update

- Updated security findings document
- Updated remediation plans  
- Archived previous scan results
- Generated by security-scan workflow with rebase fix
```

### Workflow Integration with Coordination

The automated commit process integrates seamlessly with existing CI/CD while preventing race conditions:

1. **Workflow coordination check** (ensure no other security workflow is running)
2. **Security scans execute** (scheduled, push-triggered, or manual)
3. **Findings automation runs** (`update-findings.py`)
4. **Change detection occurs** (git diff analysis)
5. **Git rebase logic executes** (sync with remote, resolve conflicts)
6. **Automated commit and push** (if changes detected and rebase successful)
7. **Repository state updated** (findings reflect current security posture)

## Error Handling

### Graceful Degradation

1. **Missing scan files**: Use previous scan data, mark as stale
2. **Malformed scan output**: Log error, continue with valid findings
3. **Remediation file corruption**: Regenerate from defaults, preserve manual edits where possible
4. **Permission errors**: Fail gracefully with clear error messages
5. **Git commit failures**: Log error, continue with artifact uploads for manual review

### Data Integrity

- **Atomic updates**: Use temporary files and atomic moves
- **Backup preservation**: Keep previous version before updates
- **Validation**: Verify YAML structure and required fields
- **Rollback capability**: Restore from backup on critical errors
- **Commit verification**: Ensure git operations complete successfully before workflow completion

## Testing Strategy

### Automated Testing

1. **Unit Tests**: Individual component functionality
   - Finding parsing from scanner outputs
   - Remediation synchronization logic
   - Document generation accuracy

2. **Integration Tests**: End-to-end workflow
   - Complete scan-to-document pipeline
   - Remediation plan synchronization
   - Historical data preservation

3. **Data Validation Tests**: Content accuracy
   - Finding ID consistency across components
   - Remediation plan completeness
   - Document formatting compliance

### Manual Testing

1. **Copy-paste deployment**: Verify module works in fresh project
2. **Remediation workflow**: Test manual plan updates and synchronization
3. **Historical tracking**: Verify audit trail accuracy over time

## Implementation Phases

### Phase 1: Core Infrastructure
- Create `security/` directory structure
- Implement basic finding parsing and document generation
- Establish remediation registry with default values

### Phase 2: Automation Integration
- Integrate with existing CI/CD workflows
- Implement automatic synchronization
- Add historical tracking and audit trails

### Phase 3: Enhanced Features
- Add compliance reporting capabilities
- Implement advanced remediation tracking
- Create deployment documentation for reuse

## Dependencies

### External Dependencies
- **PyYAML**: For remediation registry parsing
- **Jinja2**: For document template rendering (optional)

### Internal Dependencies
- **Existing security workflows**: Must integrate with current CI/CD
- **Scanner outputs**: Depends on pip-audit, bandit, secrets scanning tools
- **File system permissions**: Requires write access to `security/` directory

## Performance Considerations

### Optimization Targets
- **Document generation**: <2 seconds for typical finding volumes
- **Synchronization**: <1 second for remediation updates
- **Historical archival**: Minimal impact on CI/CD runtime

### Scalability
- **Finding volume**: Designed for 10-100 active findings
- **Historical data**: Efficient archival prevents unbounded growth
- **Multi-project deployment**: Isolated per-project state

## Security Considerations

### Access Control
- **Remediation plans**: Protect against unauthorized modifications
- **Historical data**: Preserve audit trail integrity
- **Scanner outputs**: Secure storage of vulnerability data

### Data Sensitivity
- **Vulnerability details**: May contain sensitive security information
- **Remediation plans**: Could reveal internal security processes
- **Access logging**: Track who modifies remediation plans

This design establishes a comprehensive, self-contained security module that provides automated vulnerability management while maintaining flexibility for manual remediation planning and historical tracking.