cyberviser/glasseye-ai-os

GlasseyeOS AI - Autonomous Bug Bounty Intelligence Platform

Version: 1.0.0
Status: Production-Ready Intelligence Platform

---

🎯 Mission

GlasseyeOS AI is an autonomous intelligence platform for ethical bug bounty research. It assists human security researchers with reconnaissance, vulnerability hypothesis generation, and tool creation while maintaining strict compliance with bug bounty program rules and safe harbor protections.

CRITICAL: This is an INTELLIGENCE platform, not an autonomous hacking tool. All exploitation requires human approval.

---

🏗️ Architecture

Core Components

1. Master Intelligence Engine (glasseye_core.py)

   • Autonomous reconnaissance planning
   • CVE feed monitoring (NVD, GitHub Security Advisories)
   • Attack surface analysis from public documentation
   • Vulnerability hypothesis generation
   • Campaign orchestration with human approval gates

2. Compliance Guardian (compliance_enforcer.py)

   • Autonomous safety system
   • Verify all actions against safe harbor protections
   • Auto-block out-of-scope testing
   • Prevent PII access attempts
   • Emergency stop mechanism
   • Complete audit trail

3. Knowledge Base (knowledge_base.py)

   • SQLite-based intelligence database
   • CVE intelligence and patterns
   • Disclosed bug bounty findings
   • Auto-generated tools and skills
   • Active campaign state
   • Learned attack patterns

4. Self-Update Framework (self_updater.py)

   • Monitor security research repositories
   • Download and analyze new CVE data
   • Update vulnerability patterns database
   • Generate new testing strategies
   • Self-upgrade capability

5. Agent Orchestrator (agent_orchestrator.py)

   • Multi-agent coordination
   • Spawn specialized sub-agents
   • Distribute fuzzing workload
   • Aggregate findings
   • Prioritize work by expected ROI

---

🚀 Quick Start

Installation

cd /home/x/GlasseyeOS-AI/

# Install dependencies
pip install -r requirements.txt

# Initialize knowledge base
python knowledge_base.py

# Run tests
python tests/test_all.py

# Run demo
python demo.py

Requirements

Create requirements.txt:

requests>=2.31.0
pyyaml>=6.0

Basic Usage

from glasseye_core import GlasseyeAI, Target

# Initialize AI engine
ai = GlasseyeAI()

# Define target
target = Target(
    name="GitHub Copilot",
    base_url="https://copilot.github.com",
    program_scope=["*.github.com", "copilot.github.com"],
    out_of_scope=["github.com/blog"],
    safe_harbor={
        "researcher_owned_accounts": True,
        "no_dos": True
    },
    researcher_resources=["test@researcher.com"]
)

# Analyze target (passive reconnaissance)
analysis = ai.analyze_target(target)

print(f"Attack surfaces: {len(analysis['attack_surfaces'])}")
print(f"Hypotheses: {len(analysis['hypotheses'])}")
print(f"Recommended tools: {analysis['recommended_tools']}")

# Generate reconnaissance plan
recon_plan = ai.generate_reconnaissance_plan(target)

# Monitor CVE feeds
cves = ai.monitor_cve_feeds(keywords=["Copilot", "AI"])

ai.close()

---

🔒 Safety Features

Human Approval Gates

ALL exploitation requires human approval:

from compliance_enforcer import Action, RiskLevel

action = Action(
    action_type="sql_injection_test",
    target="test@researcher.com",
    description="Test SQL injection on login form",
    risk_level=RiskLevel.HIGH,
    requires_human_approval=True,
    scope_verified=True,
    researcher_owned=True
)

# Approval workflow
approved, reason = ai.compliance.approve_action(
    action, program_scope, researcher_resources
)

if approved and action.requires_human_approval:
    # Wait for human approval
    human_approved = ai.request_human_approval(action, risk_assessment)
    
    if human_approved:
        # Execute test
        pass
    else:
        print("Human denied approval - test cancelled")

Compliance Enforcement

The Compliance Guardian enforces:

✅ Safe Harbor Protections

• Only test in-scope targets
• No DoS attacks
• No social engineering
• Researcher-owned test accounts only
• No PII access

✅ Automatic Blocking

• Out-of-scope targets
• Destructive commands (DROP TABLE, rm -rf)
• PII-containing data
• Non-researcher-owned resources

✅ Emergency Stop

• Triggers on multiple violations
• Halts all operations immediately
• Requires admin password to reset

✅ Audit Trail

• All actions logged to logs/compliance_audit.jsonl
• Timestamp, action, target, approval status
• Immutable append-only log

---

🧠 Intelligence Capabilities

1. CVE Monitoring

Auto-monitor vulnerability feeds:

from self_updater import SelfUpdater

updater = SelfUpdater()

# Daily CVE updates
daily_summary = updater.run_daily_updates()
print(f"New CVEs: {daily_summary['updates']['cves']}")

# Weekly disclosed bounty updates
weekly_summary = updater.run_weekly_updates()
print(f"New bounties: {weekly_summary['updates']['bounties']}")

Sources:

• NVD (National Vulnerability Database)
• GitHub Security Advisories
• HackerOne disclosed reports
• PortSwigger research

2. Vulnerability Hypothesis Generation

AI-powered hypothesis generation based on:

• Known Copilot CVE patterns
• Learned patterns from disclosed bounties
• Attack surface analysis
• Historical success rates

# Generate hypotheses
hypotheses = ai.generate_vulnerability_hypotheses(attack_surfaces, target)

for h in hypotheses:
    print(f"Type: {h.vulnerability_type}")
    print(f"Confidence: {h.confidence}")
    print(f"Expected bounty: ${h.expected_bounty}")
    print(f"PoC template: {h.poc_template}")

3. Attack Surface Analysis

Passive attack surface mapping:

analysis = ai.analyze_target(target)

for surface in analysis['attack_surfaces']:
    print(f"Endpoint: {surface['endpoint']}")
    print(f"Potential vulns: {surface['potential_vulnerabilities']}")

4. Tool Auto-Generation

Generate custom tools for discovered vulnerabilities:

from knowledge_base import GeneratedTool

# AI generates fuzzer for specific vulnerability
tool = GeneratedTool(
    tool_id="fuzzer_jwt_001",
    tool_type="fuzzer",
    target_vulnerability="JWT Authentication Bypass",
    code="""<generated Python fuzzer code>""",
    tests="""<generated test suite>"""
)

kb.add_generated_tool(tool)

---

🤖 Multi-Agent Orchestration

Coordinate Parallel Reconnaissance

from agent_orchestrator import AgentOrchestrator

orchestrator = AgentOrchestrator()

# Coordinate full reconnaissance campaign
campaign = orchestrator.coordinate_reconnaissance(
    target="api.example.com",
    scope=["*.example.com"]
)

print(f"Phases: {len(campaign['phases'])}")
print(f"Findings: {len(campaign['findings'])}")

Distribute Fuzzing Workload

endpoints = [f"/api/endpoint{i}" for i in range(100)]

# Distribute across multiple fuzzing agents
fuzzer_agents = orchestrator.distribute_fuzzing_workload(
    endpoints, 
    fuzzing_strategy="comprehensive"
)

print(f"Spawned {len(fuzzer_agents)} parallel fuzzers")

Prioritize by ROI

bounty_estimates = {
    "authentication_bypass": 5000,
    "IDOR": 3000,
    "XSS": 1500
}

prioritized_tasks = orchestrator.prioritize_work(tasks, bounty_estimates)

---

📊 Knowledge Base

Schema

-- CVE intelligence
CREATE TABLE cves (
    cve_id TEXT PRIMARY KEY,
    cvss_score REAL,
    attack_vector TEXT,
    vulnerability_type TEXT,
    affected_products TEXT,
    learned_pattern TEXT
);

-- Disclosed bug bounty findings
CREATE TABLE disclosed_bounties (
    report_id TEXT PRIMARY KEY,
    program TEXT,
    title TEXT,
    severity TEXT,
    bounty_amount INTEGER,
    attack_pattern TEXT,
    lessons_learned TEXT
);

-- Generated tools
CREATE TABLE generated_tools (
    tool_id TEXT PRIMARY KEY,
    tool_type TEXT,
    target_vulnerability TEXT,
    code TEXT,
    tests TEXT
);

-- Active campaigns
CREATE TABLE active_campaigns (
    campaign_id TEXT PRIMARY KEY,
    target TEXT,
    status TEXT,
    hypotheses_count INTEGER,
    findings_count INTEGER,
    estimated_bounty INTEGER
);

-- Learned vulnerability patterns
CREATE TABLE vulnerability_patterns (
    pattern_id TEXT PRIMARY KEY,
    pattern_name TEXT,
    pattern_description TEXT,
    detection_method TEXT,
    exploitation_template TEXT,
    success_rate REAL,
    avg_bounty INTEGER,
    learned_from TEXT
);

-- Attack surfaces
CREATE TABLE attack_surfaces (
    surface_id TEXT PRIMARY KEY,
    target TEXT,
    endpoint TEXT,
    method TEXT,
    authentication_required INTEGER,
    parameters TEXT,
    potential_vulnerabilities TEXT
);

Usage

from knowledge_base import KnowledgeBase, CVE, DisclosedBounty

kb = KnowledgeBase()

# Add CVE
cve = CVE(
    cve_id="CVE-2024-12345",
    cvss_score=8.5,
    attack_vector="Network",
    vulnerability_type="Authentication Bypass",
    affected_products="GitHub Copilot",
    learned_pattern="JWT algorithm confusion"
)
kb.add_cve(cve)

# Search CVEs
auth_cves = kb.search_cves(vulnerability_type="Authentication", min_cvss=7.0)

# Get statistics
stats = kb.get_stats()
print(f"Total CVEs: {stats['total_cves']}")
print(f"Total bounties: {stats['total_bounties']}")
print(f"Generated tools: {stats['total_tools']}")

kb.close()

---

🛡️ Compliance & Ethics

Safe Harbor Compliance

GlasseyeOS AI enforces strict compliance with bug bounty program rules:

1. Scope Verification

   • All targets verified against program scope
   • Out-of-scope targets automatically blocked
   • Wildcard subdomain matching

2. Researcher-Owned Resources

   • Testing only on researcher-controlled accounts
   • API keys and test data owned by researcher
   • No testing on production user data

3. No Destructive Actions

   • Forbidden patterns automatically blocked
   • Emergency stop on suspicious activity
   • Audit logging for accountability

4. Human Approval Required

   • All HIGH and CRITICAL risk actions require human approval
   • Risk assessment displayed before approval
   • Safe default: DENY

Audit Trail

All actions logged to logs/compliance_audit.jsonl:

{
  "timestamp": "2024-01-15T10:30:00Z",
  "action_type": "sql_injection_test",
  "target": "api.example.com",
  "description": "Test SQL injection on login",
  "risk_level": "high",
  "approved": true,
  "reason": "All compliance checks passed - HUMAN APPROVAL REQUIRED"
}

---

🔧 Configuration

Safe Harbor Rules (config/safe_harbor_rules.yaml)

risk_levels:
  HIGH:
    description: "Exploitation attempts"
    human_approval_required: true
    researcher_owned_required: true

thresholds:
  max_violations: 3
  emergency_stop_enabled: true

CVE Feeds (config/cve_feeds.yaml)

nvd:
  url: "https://services.nvd.nist.gov/rest/json/cves/2.0"
  update_frequency: "daily"
  filters:
    keywords: ["AI", "LLM", "Copilot"]
    min_cvss: 7.0

Agent Configuration (config/agent_configs.yaml)

osint_agent:
  agent_type: "glasseye-osint-intelligence"
  max_concurrent_tasks: 3
  timeout_minutes: 30

orchestration:
  max_total_concurrent_agents: 10
  queue_strategy: "priority"

---

📁 Directory Structure

GlasseyeOS-AI/
├── glasseye_core.py           # Main AI engine
├── compliance_enforcer.py     # Safety guardian
├── knowledge_base.py          # Intelligence DB
├── self_updater.py            # Auto-update system
├── agent_orchestrator.py      # Multi-agent coordination
├── demo.py                    # Demonstration script
├── knowledge_base.db          # SQLite database
├── requirements.txt           # Python dependencies
├── README.md                  # This file
├── config/
│   ├── safe_harbor_rules.yaml  # Compliance rules
│   ├── cve_feeds.yaml          # Data sources
│   └── agent_configs.yaml      # Sub-agent configs
├── tools/                      # Auto-generated tools
├── skills/                     # Auto-generated skills
├── logs/                       # Audit trails & logs
│   ├── compliance.log
│   ├── compliance_audit.jsonl
│   ├── glasseye_ai.log
│   ├── self_updater.log
│   └── agent_orchestrator.log
└── tests/
    └── test_all.py             # Unit tests

---

🧪 Testing

Run comprehensive test suite:

python tests/test_all.py

Test Coverage:

• Compliance enforcement (safe harbor, scope, PII)
• Knowledge base operations (CRUD, search)
• AI intelligence engine (recon, hypotheses, CVE monitoring)
• Agent orchestration (spawning, coordination, aggregation)

---

🎬 Demo

Run the interactive demo:

python demo.py

Demo Features:

1. Compliance enforcement with multiple scenarios
2. CVE monitoring and pattern learning
3. Target analysis and reconnaissance planning
4. Vulnerability hypothesis generation
5. Multi-agent coordination
6. Knowledge base statistics

---

🔄 Integration with Existing Infrastructure

Connect to Existing Bug Bounty Tools

# Integration with existing vulnerability DB
existing_db = "/root/bounty-research/azure-ai-integration/vuln_intelligence.db"

# Import existing findings
import sqlite3
conn = sqlite3.connect(existing_db)
# ... import logic

Compliance Checklist Integration

# Load existing compliance checklist
checklist_path = "/root/bounty-research/compliance-checklist-copilot-agent.md"

# Verify compliance before testing
with open(checklist_path) as f:
    compliance_rules = parse_compliance_checklist(f.read())

---

🚨 Emergency Stop

If violations detected or manual stop required:

from compliance_enforcer import ComplianceEnforcer

enforcer = ComplianceEnforcer()

# Trigger emergency stop
enforcer.trigger_emergency_stop("Manual emergency stop requested")

# Reset (requires admin password)
enforcer.reset_emergency_stop("RESET_EMERGENCY_STOP")

---

📈 Monitoring & Metrics

Knowledge Base Statistics

stats = kb.get_stats()
# {
#   'total_cves': 42,
#   'total_bounties': 15,
#   'total_tools': 8,
#   'total_campaigns': 3,
#   'total_patterns': 25,
#   'total_surfaces': 67,
#   'total_estimated_bounty': 45000
# }

Orchestrator Status

status = orchestrator.get_status()
# {
#   'active_agents': 5,
#   'queued_tasks': 12,
#   'completed_tasks': 38
# }

---

🤝 Contributing

This is a security research tool. Contributions must:

1. Maintain strict compliance enforcement
2. Preserve human approval gates
3. Enhance safety features
4. Include comprehensive tests
5. Document ethical usage

---

⚖️ Legal & Ethics

Permitted Use

✅ Authorized bug bounty research
✅ Security testing on owned/permitted systems
✅ Vulnerability research and disclosure
✅ Educational security research

Prohibited Use

❌ Unauthorized penetration testing
❌ Malicious hacking or computer crimes
❌ Bypassing security controls without permission
❌ Testing outside bug bounty program scope

Disclaimer

This tool is for AUTHORIZED security research only. Users are responsible for:

• Obtaining proper authorization
• Following bug bounty program rules
• Complying with all applicable laws
• Ethical disclosure of vulnerabilities

The authors assume no liability for misuse.

---

📞 Support

Documentation: This README
Tests: python tests/test_all.py
Demo: python demo.py
Logs: logs/ directory
Config: config/ directory

---

🏆 Acknowledgments

Built for ethical security research.
Inspired by responsible disclosure and bug bounty best practices.

Remember: With great power comes great responsibility. Use wisely.

---

📝 License

For authorized security research use only.
See program terms and safe harbor agreements.

---

Version 1.0.0 | Last Updated: 2024-01-15
Status: Production-Ready Intelligence Platform ✅

🛠️ Autonomous Tool Generator

Overview

The Tool Generator autonomously creates security testing tools based on discovered attack surfaces. It uses a template-based architecture with compliance enforcement and automatic code validation.

Quick Start

# Generate a JSON-RPC fuzzer
python3 tool_generator.py --fuzzer "JSON-RPC 2.0" --target copilot-mcp-server

# Generate a protocol analyzer
python3 tool_generator.py --analyzer "HTTP API" --target github-api

# Generate an MCP skill
python3 tool_generator.py --skill "api-security-scanner" --target my-target

# Generate from attack surface
python3 demo_tool_generator.py

Tool Generation Workflow

from tool_generator import ToolGenerator, AttackSurface

# Define attack surface
attack_surface = AttackSurface(
    name='github-copilot-mcp',
    protocol='JSON-RPC 2.0',
    transport='STDIO',
    authentication='OAuth',
    attack_vectors=['command injection', 'auth bypass'],
    endpoints=['/tools/list', '/tools/call']
)

# Generate tools
gen = ToolGenerator()
tools = gen.generate_tool_from_attack_surface(attack_surface)

# Tools are saved to generated_tools/ directory

Available Templates

Fuzzers:

• json_rpc_fuzzer_template.py - JSON-RPC 2.0 protocol fuzzer
• http_api_fuzzer_template.py - HTTP API fuzzer (planned)
• websocket_fuzzer_template.py - WebSocket fuzzer (planned)

Analyzers:

• protocol_analyzer_template.py - Generic protocol analyzer
• config_analyzer_template.py - Configuration file analyzer (planned)
• dependency_scanner_template.py - Dependency vulnerability scanner (planned)

PoC Generators:

• command_injection_poc_template.py - Command injection PoC
• auth_bypass_poc_template.py - Authorization bypass PoC (planned)
• ssrf_poc_template.py - SSRF PoC (planned)

Evidence Collectors:

• network_recorder_template.py - Network traffic capture
• log_aggregator_template.py - Log collection (planned)
• screenshot_automation_template.py - Screenshot capture (planned)

MCP Skills:

• mcp_skill_template_template.py - Foundry/VS Code skill template

Customizing Templates

Templates use {placeholder} syntax for variable substitution:

# Example template
"""
Target: {target}
Protocol: {protocol}
Generated: {timestamp}

class {target}_Fuzzer:
    def __init__(self):
        self.target = "{target}"
        self.protocol = "{protocol}"
"""

Parameters are automatically substituted during generation.

Generated Tool Features

All generated tools include:

• ✅ Full error handling and logging
• ✅ Command-line argument parsing
• ✅ Dry-run mode for safe testing
• ✅ Compliance notices and authorization checks
• ✅ Unit test stubs
• ✅ Comprehensive documentation
• ✅ Executable permissions

MCP Skill Generation

Generated MCP skills are compatible with Foundry and VS Code:

python3 tool_generator.py --skill "github-security-scanner" --target github-api

Creates:

generated_skills/github-security-scanner/
├── skill.yaml          # MCP manifest
├── skill.py            # Implementation
└── README.md           # Documentation

Testing Generated Tools

# Run unit tests for tool generator
python3 tests/test_tool_generator.py

# Test a generated tool (dry-run)
./generated_tools/copilot-mcp-demo_json_rpc_fuzzer.py --dry-run --target test.example.com

# Test with verbose logging
./generated_tools/copilot-mcp-demo_json_rpc_fuzzer.py --verbose --target test.example.com

Code Validation

Generated code is automatically validated for:

• ✅ Python syntax correctness
• ⚠️ Dangerous pattern detection (eval, os.system, etc.)
• ✅ Compliance with safe harbor policies
• ✅ Proper error handling

Integration with GlasseyeOS Core

from glasseye_core import GlasseyeAI
from tool_generator import ToolGenerator

ai = GlasseyeAI()
gen = ToolGenerator()

# Analyze target and generate tools
target_analysis = ai.analyze_target("example.com")
attack_surfaces = target_analysis['attack_surfaces']

for surface in attack_surfaces:
    tools = gen.generate_tool_from_attack_surface(surface)
    print(f"Generated {len(tools)} tools for {surface.name}")

Advanced Usage

Custom template directories:

gen = ToolGenerator(
    templates_dir='/custom/templates',
    output_dir='/custom/output',
    skills_dir='/custom/skills'
)

Attack surface analysis only:

tools_needed = gen.analyze_attack_surface(attack_surface)
# Returns: ['json_rpc_fuzzer', 'protocol_analyzer', ...]

Generate specific tool types:

# Fuzzer
fuzzer_path = gen.generate_fuzzer(
    protocol='JSON-RPC 2.0',
    transport='STDIO',
    target='my-server',
    endpoints=['/api/v1']
)

# Protocol analyzer
analyzer_path = gen.generate_protocol_analyzer({
    'name': 'MCP Protocol',
    'target': 'copilot',
    'message_format': 'JSON',
    'authentication': 'Bearer tokens'
})

# PoC template
poc_path = gen.generate_poc_template(
    vulnerability_type='command_injection',
    target='demo-app',
    details={'endpoint': '/api/exec', 'payload': '; id'}
)

# MCP skill
skill_path = gen.generate_mcp_skill(
    capability='security-audit',
    description='Run security audits',
    tools_list=['fuzzer', 'scanner', 'analyzer']
)

Demo

Run the interactive demo:

python3 demo_tool_generator.py

This demonstrates:

1. Template system overview
2. Code validation
3. Fuzzer generation
4. Protocol analyzer generation
5. MCP skill generation
6. Attack surface analysis with multi-tool generation

---

🆕 Enhanced Self-Updater v2.0

GlasseyeOS AI now features an autonomous learning and update system that continuously improves by monitoring security research and bug bounty disclosures.

New Capabilities

1. GitHub Security Advisory Monitor - Auto-fetch and learn from GitHub advisories
2. HackerOne Disclosed Learning - Extract attack patterns from real-world exploits
3. Vulnerability Pattern Extraction - NLP-based pattern recognition from CVE text
4. Self-Code Updates - Auto-update with human approval gates
5. Security Research Monitor - Track arXiv, Black Hat, DEF CON papers
6. Program Rule Monitor - Auto-detect bug bounty scope/rule changes
7. Automated Tool Updates - Regenerate tools with new techniques

Quick Start

# Run enhanced self-updater
python3 self_updater.py

# Or programmatically
from self_updater import EnhancedSelfUpdater
u = EnhancedSelfUpdater()
u.run_daily_updates()
u.close()

Documentation

• Complete Guide: ENHANCED_SELF_UPDATER_GUIDE.md
• Deployment: DEPLOYMENT_CHECKLIST.md
• Mission Report: SELF_UPDATE_MISSION_COMPLETE.md
• Tests: tests/test_enhanced_self_updater.py

Knowledge Base Growth

The system autonomously learns from:

• Daily: NVD CVEs, GitHub advisories (5-10 new patterns/day)
• Weekly: HackerOne reports, arXiv papers (20-30 new patterns/week)
• Continuous: Bug bounty program rule monitoring

Expected Growth: 10x knowledge base size in first 30 days 📈