vulnerability-csv-reporting

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# Vulnerability CSV Report Generation

This skill provides guidance on generating structured CSV reports from vulnerability scan data—a common format for security audits and compliance reporting.

## Overview

CSV (Comma-Separated Values) is a widely-used format for security reports because it's:
- **Human-readable**: Can be opened in Excel, Google Sheets
- **Machine-parseable**: Easy to process programmatically
- **Universal**: Supported by all data analysis tools
- **Lightweight**: Smaller than JSON/XML formats

## When to Use CSV Reports

### Ideal Use Cases
- Compliance audits requiring tabular data
- Executive summaries for non-technical stakeholders
- Integration with ticketing systems (Jira, ServiceNow)
- Automated vulnerability tracking pipelines
- Data analysis in spreadsheet tools

### Limitations
- No hierarchical data (flat structure only)
- Limited support for nested information
- No standard for binary data

**Alternative formats**: JSON (for APIs), PDF (for formal reports), HTML (for dashboards)

## CSV Schema Design for Security Reports

### Essential Fields

A well-designed vulnerability report CSV should include:

| Field | Type | Description | Example |
|-------|------|-------------|---------|
| **Package** | String | Vulnerable package name | `express` |
| **Version** | String | Installed version | `4.17.1` |
| **CVE_ID** | String | Vulnerability identifier | `CVE-2022-24999` |
| **Severity** | Enum | Risk level | `CRITICAL`, `HIGH` |
| **CVSS_Score** | Float/String | Numeric severity score | `9.8` or `N/A` |
| **Fixed_Version** | String | Patched version | `4.18.0` or `N/A` |
| **Title** | String | Brief description | `XSS in Express.js` |
| **Url** | String | Reference link | `https://nvd.nist.gov/...` |

### Design Principles

1. **Use descriptive column names**: `Package` not `pkg`, `CVE_ID` not `id`
2. **Handle missing data**: Use `N/A` for unavailable fields, not empty strings
3. **Consistent data types**: Ensure all rows have same format
4. **Include metadata**: Consider adding scan date, target, tool version

## Python CSV Generation with DictWriter

### Why DictWriter?

Python's `csv.DictWriter` is ideal for structured reports:
- **Type-safe**: Column names defined upfront
- **Readable**: Use dictionary keys instead of indices
- **Maintainable**: Easy to add/remove columns
- **Automatic header generation**: No manual header writing

### Basic Usage

```python
import csv

# Define schema
headers = ["Package", "Version", "CVE_ID", "Severity", "CVSS_Score", 
           "Fixed_Version", "Title", "Url"]

# Prepare data
vulnerabilities = [
    {
        "Package": "minimist",
        "Version": "1.2.5",
        "CVE_ID": "CVE-2021-44906",
        "Severity": "CRITICAL",
        "CVSS_Score": 9.8,
        "Fixed_Version": "1.2.6",
        "Title": "Prototype Pollution",
        "Url": "https://avd.aquasec.com/nvd/cve-2021-44906"
    }
]

# Write CSV
with open('security_audit.csv', 'w', newline='', encoding='utf-8') as f:
    writer = csv.DictWriter(f, fieldnames=headers)
    writer.writeheader()  # Write column names
    writer.writerows(vulnerabilities)  # Write all rows
```

**Important parameters**:
- `newline=''`: Prevents extra blank lines on Windows
- `encoding='utf-8'`: Handles special characters in descriptions

## Severity-Based Filtering

### Why Filter by Severity?

Security teams prioritize based on risk. Filtering ensures reports focus on critical issues:

| Severity | Action Required | Typical SLA |
|----------|----------------|-------------|
| **CRITICAL** | Immediate patch | 24 hours |
| **HIGH** | Urgent patch | 7 days |
| **MEDIUM** | Scheduled patch | 30 days |
| **LOW** | Optional patch | 90 days |

### Implementation

```python
def filter_high_severity(vulnerabilities, min_severity=['HIGH', 'CRITICAL']):
    """
    Filter vulnerabilities by severity level.
    
    Args:
        vulnerabilities: List of vulnerability dictionaries
        min_severity: List of severity levels to include

From benchflow-ai/skillsbench, Apache-2.0.

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