mirror of
https://github.com/Vyntral/god-eye.git
synced 2026-02-12 16:52:45 +00:00
14c26dc726
- Implement 8 specialized AI agents (XSS, SQLi, Auth, API, Crypto, Secrets, Headers, General) - Add fast type-based routing for finding classification - Include OWASP-aligned knowledge bases per agent - Add agent handoff logic for cross-vulnerability detection - Optimize timeouts and parallelism for local LLM - Add new modules: cache, network, fingerprint, secrets, cloud, API, discovery - Update documentation with multi-agent feature
435 lines
11 KiB
Markdown
435 lines
11 KiB
Markdown
# God's Eye - AI Integration Examples
|
|
|
|
## 🎯 Real-World Usage Examples
|
|
|
|
### Example 1: Bug Bounty Recon
|
|
|
|
```bash
|
|
# Initial reconnaissance with AI analysis
|
|
./god-eye -d target.com --enable-ai -o recon.json -f json
|
|
|
|
# Filter high-severity AI findings
|
|
cat recon.json | jq '.[] | select(.ai_severity == "critical" or .ai_severity == "high")'
|
|
|
|
# Extract subdomains with CVEs
|
|
cat recon.json | jq '.[] | select(.cve_findings | length > 0)'
|
|
|
|
# Get AI-detected admin panels
|
|
cat recon.json | jq '.[] | select(.admin_panels | length > 0)'
|
|
```
|
|
|
|
### Example 2: Pentesting Workflow
|
|
|
|
```bash
|
|
# Fast scan for initial scope
|
|
./god-eye -d client.com --enable-ai --no-brute --active
|
|
|
|
# Deep analysis on interesting findings
|
|
./god-eye -d client.com --enable-ai --ai-deep -c 500
|
|
|
|
# Generate report for client
|
|
./god-eye -d client.com --enable-ai -o client_report.txt
|
|
```
|
|
|
|
### Example 3: Security Audit
|
|
|
|
```bash
|
|
# Comprehensive audit with all checks
|
|
./god-eye -d company.com --enable-ai
|
|
|
|
# Focus on specific issues
|
|
./god-eye -d company.com --enable-ai --active | grep -E "AI:CRITICAL|CVE"
|
|
|
|
# Export for further analysis
|
|
./god-eye -d company.com --enable-ai -o audit.csv -f csv
|
|
```
|
|
|
|
### Example 4: Quick Triage
|
|
|
|
```bash
|
|
# Super fast scan (no brute-force, cascade enabled)
|
|
time ./god-eye -d target.com --enable-ai --no-brute
|
|
|
|
# Should complete in ~30-60 seconds for small targets
|
|
```
|
|
|
|
### Example 5: Development Environment Check
|
|
|
|
```bash
|
|
# Find exposed dev/staging environments
|
|
./god-eye -d company.com --enable-ai | grep -E "dev|staging|test"
|
|
|
|
# AI will identify debug mode, error messages, etc.
|
|
```
|
|
|
|
---
|
|
|
|
## 📊 Expected Output Examples
|
|
|
|
### Without AI
|
|
|
|
```
|
|
═══════════════════════════════════════════════════
|
|
● api.example.com [200] ⚡156ms
|
|
IP: 93.184.216.34
|
|
Tech: nginx, React
|
|
FOUND: Admin: /admin [200]
|
|
JS SECRET: api_key: "sk_test_123..."
|
|
═══════════════════════════════════════════════════
|
|
```
|
|
|
|
### With AI Enabled
|
|
|
|
```
|
|
═══════════════════════════════════════════════════
|
|
● api.example.com [200] ⚡156ms
|
|
IP: 93.184.216.34
|
|
Tech: nginx, React
|
|
FOUND: Admin: /admin [200]
|
|
JS SECRET: api_key: "sk_test_123..."
|
|
AI:CRITICAL: Hardcoded Stripe test API key exposed in main.js
|
|
Authentication bypass possible via admin parameter
|
|
React version 16.8.0 has known XSS vulnerability
|
|
Missing rate limiting on /api/v1/users endpoint
|
|
(1 more findings...)
|
|
model: deepseek-r1:1.5b→qwen2.5-coder:7b
|
|
CVE: React: CVE-2020-15168 - XSS vulnerability in development mode
|
|
═══════════════════════════════════════════════════
|
|
```
|
|
|
|
### AI Report Section
|
|
|
|
```
|
|
🧠 AI-POWERED ANALYSIS (cascade: deepseek-r1:1.5b + qwen2.5-coder:7b)
|
|
Analyzing findings with local LLM
|
|
|
|
AI:C api.example.com → 4 findings
|
|
AI:H admin.example.com → 2 findings
|
|
AI:H dev.example.com → 3 findings
|
|
AI:M staging.example.com → 5 findings
|
|
|
|
✓ AI analysis complete: 14 findings across 4 subdomains
|
|
|
|
📋 AI SECURITY REPORT
|
|
|
|
## Executive Summary
|
|
Analysis identified 14 security findings across 4 subdomains, with 1 critical
|
|
and 2 high-severity issues requiring immediate attention. Key concerns include
|
|
hardcoded credentials and exposed development environments.
|
|
|
|
## Critical Findings
|
|
|
|
[CRITICAL] api.example.com:
|
|
- Hardcoded Stripe API key in main.js (test key exposed)
|
|
- Authentication bypass via admin parameter
|
|
- React XSS vulnerability (CVE-2020-15168)
|
|
CVEs:
|
|
- React: CVE-2020-15168
|
|
|
|
[HIGH] admin.example.com:
|
|
- Basic auth with default credentials detected
|
|
- Directory listing enabled on /uploads/
|
|
|
|
[HIGH] dev.example.com:
|
|
- Django debug mode enabled with stack traces
|
|
- Source code exposure via .git directory
|
|
- Database connection string in error messages
|
|
|
|
## Recommendations
|
|
1. IMMEDIATE: Remove hardcoded API keys and rotate credentials
|
|
2. IMMEDIATE: Disable debug mode in production environments
|
|
3. IMMEDIATE: Remove exposed .git directory
|
|
4. HIGH: Update React to latest stable version
|
|
5. HIGH: Implement proper authentication on admin panel
|
|
6. MEDIUM: Disable directory listing on sensitive paths
|
|
7. MEDIUM: Configure proper error handling to prevent information disclosure
|
|
```
|
|
|
|
---
|
|
|
|
## 🤖 Multi-Agent Examples
|
|
|
|
### Example 6: Multi-Agent Deep Analysis
|
|
|
|
```bash
|
|
# Enable 8 specialized AI agents for comprehensive analysis
|
|
./god-eye -d target.com --enable-ai --multi-agent --no-brute
|
|
|
|
# Combine with active filter
|
|
./god-eye -d target.com --enable-ai --multi-agent --active
|
|
```
|
|
|
|
### Multi-Agent Output
|
|
|
|
```
|
|
🤖 MULTI-AGENT ANALYSIS
|
|
──────────────────────────────────────────────────
|
|
Routing findings to specialized AI agents...
|
|
✓ Multi-agent analysis complete: 4 critical, 34 high, 0 medium
|
|
Agent usage:
|
|
headers: 10 analyses (avg confidence: 50%)
|
|
crypto: 17 analyses (avg confidence: 50%)
|
|
xss: 3 analyses (avg confidence: 50%)
|
|
api: 2 analyses (avg confidence: 50%)
|
|
secrets: 3 analyses (avg confidence: 50%)
|
|
!! Weak CSP directives: headers agent
|
|
!! CORS allows all origins: headers agent
|
|
! Missing HSTS: headers agent
|
|
! Cookie without Secure flag: headers agent
|
|
```
|
|
|
|
### Agent-Specific Analysis
|
|
|
|
Each agent provides domain-specific findings:
|
|
|
|
| Agent | Sample Finding |
|
|
|-------|----------------|
|
|
| Headers | Missing CSP, HSTS, X-Frame-Options, cookie flags |
|
|
| Secrets | Hardcoded API keys, tokens, passwords in JS |
|
|
| XSS | DOM sinks, innerHTML, unsafe event handlers |
|
|
| API | CORS misconfiguration, rate limiting issues |
|
|
| Auth | IDOR, session fixation, JWT problems |
|
|
| Crypto | Weak TLS, expired certs, self-signed issues |
|
|
|
|
---
|
|
|
|
## 🎭 Scenario-Based Examples
|
|
|
|
### Scenario 1: Found a Suspicious Subdomain
|
|
|
|
```bash
|
|
# Initial scan found dev.target.com
|
|
# Let AI analyze it in detail
|
|
|
|
./god-eye -d target.com --enable-ai --ai-deep
|
|
|
|
# AI might find:
|
|
# - Debug mode enabled
|
|
# - Test credentials in source
|
|
# - Exposed API documentation
|
|
# - Missing security headers
|
|
```
|
|
|
|
### Scenario 2: JavaScript Heavy Application
|
|
|
|
```bash
|
|
# SPA with lots of JavaScript
|
|
./god-eye -d webapp.com --enable-ai
|
|
|
|
# AI excels at:
|
|
# ✓ Analyzing minified/obfuscated code
|
|
# ✓ Finding hidden API endpoints
|
|
# ✓ Detecting auth bypass logic
|
|
# ✓ Identifying client-side security issues
|
|
```
|
|
|
|
### Scenario 3: API-First Platform
|
|
|
|
```bash
|
|
# Multiple API subdomains
|
|
./god-eye -d api-platform.com --enable-ai --ai-deep
|
|
|
|
# AI will identify:
|
|
# ✓ API version mismatches
|
|
# ✓ Unprotected endpoints
|
|
# ✓ CORS issues
|
|
# ✓ Rate limiting problems
|
|
```
|
|
|
|
### Scenario 4: Legacy Application
|
|
|
|
```bash
|
|
# Old PHP/WordPress site
|
|
./god-eye -d old-site.com --enable-ai
|
|
|
|
# AI checks for:
|
|
# ✓ Known CVEs in detected versions
|
|
# ✓ Common WordPress vulns
|
|
# ✓ Outdated library versions
|
|
# ✓ Exposed backup files
|
|
```
|
|
|
|
---
|
|
|
|
## 💡 Pro Tips
|
|
|
|
### Tip 1: Combine with Other Tools
|
|
|
|
```bash
|
|
# God's Eye → Nuclei pipeline
|
|
./god-eye -d target.com --enable-ai --active -s | nuclei -t cves/
|
|
|
|
# God's Eye → httpx pipeline
|
|
./god-eye -d target.com --enable-ai -s | httpx -tech-detect
|
|
|
|
# God's Eye → Custom script
|
|
./god-eye -d target.com --enable-ai -o scan.json -f json
|
|
python analyze.py scan.json
|
|
```
|
|
|
|
### Tip 2: Incremental Scans
|
|
|
|
```bash
|
|
# Day 1: Initial recon
|
|
./god-eye -d target.com --enable-ai -o day1.json -f json
|
|
|
|
# Day 2: Update scan
|
|
./god-eye -d target.com --enable-ai -o day2.json -f json
|
|
|
|
# Compare findings
|
|
diff <(jq '.[] | .subdomain' day1.json) <(jq '.[] | .subdomain' day2.json)
|
|
```
|
|
|
|
### Tip 3: Filter by AI Severity
|
|
|
|
```bash
|
|
# Only show critical findings
|
|
./god-eye -d target.com --enable-ai -o scan.json -f json
|
|
cat scan.json | jq '.[] | select(.ai_severity == "critical")'
|
|
|
|
# Count findings by severity
|
|
cat scan.json | jq -r '.[] | .ai_severity' | sort | uniq -c
|
|
```
|
|
|
|
### Tip 4: Custom Wordlist with AI
|
|
|
|
```bash
|
|
# AI can help identify naming patterns
|
|
# First run to learn patterns
|
|
./god-eye -d target.com --enable-ai --no-brute
|
|
|
|
# AI identifies pattern: api-v1, api-v2, api-v3
|
|
# Create custom wordlist:
|
|
echo -e "api-v4\napi-v5\napi-staging\napi-prod" > custom.txt
|
|
|
|
# Second run with custom wordlist
|
|
./god-eye -d target.com --enable-ai -w custom.txt
|
|
```
|
|
|
|
### Tip 5: Monitoring Setup
|
|
|
|
```bash
|
|
#!/bin/bash
|
|
# monitor-target.sh - Daily AI-powered monitoring
|
|
|
|
TARGET="target.com"
|
|
DATE=$(date +%Y%m%d)
|
|
OUTPUT="scans/${TARGET}_${DATE}.json"
|
|
|
|
./god-eye -d $TARGET --enable-ai --active -o $OUTPUT -f json
|
|
|
|
# Alert on new critical findings
|
|
CRITICAL=$(cat $OUTPUT | jq '.[] | select(.ai_severity == "critical")' | wc -l)
|
|
if [ $CRITICAL -gt 0 ]; then
|
|
echo "ALERT: $CRITICAL critical findings for $TARGET"
|
|
cat $OUTPUT | jq '.[] | select(.ai_severity == "critical")'
|
|
fi
|
|
```
|
|
|
|
---
|
|
|
|
## 🧪 Testing AI Features
|
|
|
|
### Test 1: Verify AI is Working
|
|
|
|
```bash
|
|
# Should show AI analysis section
|
|
./god-eye -d example.com --enable-ai --no-brute -v
|
|
|
|
# Look for:
|
|
# ✓ "🧠 AI-POWERED ANALYSIS"
|
|
# ✓ Model names in output
|
|
# ✓ AI findings if vulnerabilities detected
|
|
```
|
|
|
|
### Test 2: Compare AI vs No-AI
|
|
|
|
```bash
|
|
# Without AI
|
|
time ./god-eye -d target.com --no-brute -o noai.json -f json
|
|
|
|
# With AI
|
|
time ./god-eye -d target.com --no-brute --enable-ai -o ai.json -f json
|
|
|
|
# Compare
|
|
echo "Findings without AI: $(cat noai.json | jq length)"
|
|
echo "Findings with AI: $(cat ai.json | jq length)"
|
|
echo "New AI findings: $(cat ai.json | jq '[.[] | select(.ai_findings != null)] | length')"
|
|
```
|
|
|
|
### Test 3: Benchmark Different Modes
|
|
|
|
```bash
|
|
# Cascade (default)
|
|
time ./god-eye -d target.com --enable-ai --no-brute
|
|
|
|
# No cascade
|
|
time ./god-eye -d target.com --enable-ai --ai-cascade=false --no-brute
|
|
|
|
# Deep mode
|
|
time ./god-eye -d target.com --enable-ai --ai-deep --no-brute
|
|
```
|
|
|
|
---
|
|
|
|
## 📈 Performance Optimization
|
|
|
|
### For Large Targets (>100 subdomains)
|
|
|
|
```bash
|
|
# Reduce concurrency to avoid overwhelming Ollama
|
|
./god-eye -d large-target.com --enable-ai -c 500
|
|
|
|
# Use fast model only (skip deep analysis)
|
|
./god-eye -d large-target.com --enable-ai --ai-cascade=false \
|
|
--ai-deep-model deepseek-r1:1.5b
|
|
|
|
# Disable AI for initial enumeration, enable for interesting findings
|
|
./god-eye -d large-target.com --no-brute -s > subdomains.txt
|
|
cat subdomains.txt | head -20 | while read sub; do
|
|
./god-eye -d $sub --enable-ai --no-brute
|
|
done
|
|
```
|
|
|
|
### For GPU Acceleration
|
|
|
|
```bash
|
|
# Ollama automatically uses GPU if available
|
|
# Check GPU usage:
|
|
nvidia-smi # Linux/Windows with NVIDIA
|
|
ollama ps # Should show GPU model
|
|
|
|
# With GPU, you can use larger models:
|
|
./god-eye -d target.com --enable-ai \
|
|
--ai-deep-model deepseek-coder-v2:16b
|
|
```
|
|
|
|
---
|
|
|
|
## 🎓 Learning from AI Output
|
|
|
|
### Example: Understanding AI Findings
|
|
|
|
**Input:** JavaScript code with potential issue
|
|
```javascript
|
|
const API_KEY = "sk_live_51H...";
|
|
fetch(`/api/user/${userId}`);
|
|
```
|
|
|
|
**AI Output:**
|
|
```
|
|
AI:CRITICAL: Hardcoded production API key detected
|
|
Unsanitized user input in URL parameter
|
|
Missing authentication on API endpoint
|
|
```
|
|
|
|
**What to Do:**
|
|
1. Verify the API key is active
|
|
2. Test the userId parameter for injection
|
|
3. Check if /api/user requires authentication
|
|
4. Report to bug bounty program or client
|
|
|
|
---
|
|
|
|
**Happy Hunting with AI! 🎯🧠**
|