Zero-Day Vulnerability Assessment Framework

Zero-Day Lifecycle:

Zero-Day Assessment Framework Phases:

Phase 1: Asset Discovery & Inventory
├── Network mapping and service enumeration
├── Software inventory and version tracking
├── Third-party component identification
└── Attack surface analysis

Phase 2: Vulnerability Research
├── Static code analysis and review
├── Dynamic testing and fuzzing
├── Behavioral analysis and monitoring
└── Threat intelligence correlation

Phase 3: Risk Assessment
├── Vulnerability impact analysis
├── Exploitability assessment
├── Business risk quantification
└── Priority scoring and ranking

Phase 4: Detection & Monitoring
├── Early warning system development
├── Anomaly detection implementation
├── Threat hunting procedures
└── Incident response preparation

Phase 5: Mitigation & Response
├── Compensating controls implementation
├── Incident response execution
├── Recovery and remediation
└── Lessons learned integration

Zero-Day Risk Scoring Matrix:

Risk Score = (Impact × Likelihood × Exploitability) / Mitigation Factor

Impact Factors:
- Confidentiality Impact (1-5)
- Integrity Impact (1-5)
- Availability Impact (1-5)
- Business Process Impact (1-5)

Likelihood Factors:
- Asset Exposure (1-5)
- Threat Actor Interest (1-5)
- Historical Attack Patterns (1-5)

Exploitability Factors:
- Attack Complexity (1-5)
- Required Privileges (1-5)
- User Interaction Required (1-5)

Mitigation Factor:
- Existing Controls (0.1-1.0)
- Detection Capability (0.1-1.0)
- Response Readiness (0.1-1.0)

Risk Levels:
Critical: 80-100    High: 60-79
Medium: 40-59      Low: 20-39
Info: 0-19

# Static Analysis Workflow

# 1. Source Code Scanning
sonarqube-scanner \
  -Dsonar.projectKey=myproject \
  -Dsonar.sources=./src \
  -Dsonar.host.url=http://localhost:9000

# 2. Binary Analysis
ida-pro analysis.exe
ghidra-headless /projects project_name -import binary.exe -postScript analyze.py

# 3. Dependency Scanning
npm audit --audit-level high
safety check --json requirements.txt
snyk test --severity-threshold=high

# 4. Custom Rule Development
# SemGrep custom rules for zero-day patterns
rules:
  - id: potential-buffer-overflow
    patterns:
      - pattern: strcpy($DEST, $SRC)
    message: Potential buffer overflow vulnerability
    severity: HIGH
    languages: [c, cpp]

# 5. Configuration Analysis
checkov -f dockerfile
kube-score deployment.yaml

# Dynamic Analysis Tools and Techniques

# 1. Application Fuzzing
# AFL++ for binary fuzzing
afl-gcc -o target target.c
afl-fuzz -i input_dir -o output_dir ./target @@

# Web application fuzzing
ffuf -w wordlist.txt -u https://target.com/FUZZ -mc 200

# 2. Memory Analysis
# Valgrind for memory error detection
valgrind --tool=memcheck --leak-check=full ./target

# AddressSanitizer
gcc -fsanitize=address -g -o target target.c

# 3. Runtime Monitoring
# Strace for system call monitoring
strace -f -o trace.log ./target

# DTrace for advanced tracing
dtrace -n 'syscall:::entry { printf("%s", execname); }'

# 4. Behavioral Analysis
# Process Monitor (Windows)
procmon.exe /BackingFile output.pml /Runtime 300

# 5. Network Traffic Analysis
# Wireshark automated analysis
tshark -i eth0 -w capture.pcap -a duration:3600
tcpdump -i any -w network.pcap host target.com

# Threat Hunting Queries for Zero-Day Detection

# 1. Splunk Queries
# Unusual process creation patterns
index=windows EventCode=4688
| stats count by Process_Name, Process_Command_Line
| where count < 5
| eval risk_score=case(match(Process_Command_Line, "powershell.*-enc"), 5, 1)

# 2. ELK Stack Queries
# Detecting code injection attempts
GET /winlogbeat-*/_search
{
  "query": {
    "bool": {
      "must": [
        {"term": {"event.code": "10"}},
        {"range": {"@timestamp": {"gte": "now-1h"}}},
        {"regexp": {"process.name": ".*\.(exe|dll)"}}
      ]
    }
  }
}

# 3. YARA Rules for Zero-Day Patterns
rule Potential_Zero_Day_Exploit {
    meta:
        description = "Potential zero-day exploit indicators"
        author = "Security Team"
    strings:
        $shellcode = { 31 c0 50 68 2f 2f 73 68 }
        $rop_gadget = { 5d c3 }
        $heap_spray = "AAAA" nocase
    condition:
        2 of them
}

# 4. Sigma Rules
title: Suspicious Process Creation
logsource:
    product: windows
    service: security
detection:
    selection:
        EventID: 4688
        ProcessName|contains:
            - 'rundll32.exe'
            - 'regsvr32.exe'
        CommandLine|contains:
            - 'javascript:'
            - 'vbscript:'
    condition: selection

# Custom Zero-Day Assessment Tool Example

#!/usr/bin/env python3
import requests
import subprocess
import hashlib
import json
from datetime import datetime

class ZeroDayAssessment:
    def __init__(self, target):
        self.target = target
        self.results = {"timestamp": datetime.now().isoformat(),
                       "target": target, "findings": []}
    
    def check_cve_correlation(self, service, version):
        """Check if service version has known CVEs"""
        # Query CVE databases
        pass
    
    def analyze_response_patterns(self, url):
        """Analyze HTTP responses for anomalous patterns"""
        response = requests.get(url)
        
        # Check for unusual headers
        suspicious_headers = []
        for header, value in response.headers.items():
            if any(keyword in value.lower() for keyword in 
                   ['debug', 'internal', 'test', 'dev']):
                suspicious_headers.append(f"{header}: {value}")
        
        # Analyze response timing
        if response.elapsed.total_seconds() > 5:
            self.add_finding("slow_response", 
                           f"Unusually slow response: {response.elapsed}")
        
        return suspicious_headers
    
    def check_binary_signatures(self, binary_path):
        """Analyze binary for suspicious patterns"""
        with open(binary_path, 'rb') as f:
            content = f.read()
        
        # Check for common exploit patterns
        patterns = [
            b'\x90' * 10,  # NOP sled
            b'\x41' * 100,  # Buffer overflow pattern
            b'\xcc\xcc',   # Debug breaks
        ]
        
        for pattern in patterns:
            if pattern in content:
                self.add_finding("suspicious_binary_pattern",
                               f"Found pattern: {pattern}")
    
    def add_finding(self, finding_type, description):
        self.results["findings"].append({
            "type": finding_type,
            "description": description,
            "timestamp": datetime.now().isoformat()
        })
    
    def generate_report(self):
        return json.dumps(self.results, indent=2)

# Usage example
assessment = ZeroDayAssessment("example.com")
print(assessment.generate_report())

Zero-Day Assessment Team Structure:

┌─────────────────────────────────────┐
│           CISO / Security VP        │
└─────────────┬───────────────────────┘
              │
┌─────────────▼───────────────────────┐
│     Zero-Day Program Manager        │
└─────────────┬───────────────────────┘
              │
    ┌─────────┼─────────┐
    │         │         │
┌───▼───┐ ┌──▼──┐ ┌────▼────┐
│Research│ │Hunt │ │Response │
│ Team   │ │Team │ │  Team   │
└────────┘ └─────┘ └─────────┘

Research Team:
├── Security Researchers (2-3)
├── Malware Analysts (2)
├── Reverse Engineers (2)
└── Tool Developers (1-2)

Hunt Team:
├── Threat Hunters (3-4)
├── SOC Analysts (4-6)
├── Data Scientists (1-2)
└── Intelligence Analysts (2)

Response Team:
├── Incident Responders (4-5)
├── Forensics Specialists (2-3)
├── Communications Lead (1)
└── Legal Liaison (1)

# Early Warning System Architecture

# 1. Honeypot Network
# Deploy honeypots to detect unknown attack patterns
docker run -d --name honeypot-web -p 8080:80 \
  cowrie/cowrie:latest

# Monitor honeypot logs for zero-day indicators
tail -f /var/log/cowrie/cowrie.log | \
  grep -E "(unknown_command|shell_injection|buffer_overflow)"

# 2. Threat Intelligence Feeds
# Aggregate multiple intelligence sources
feeds = [
    "https://feodotracker.abuse.ch/downloads/ipblocklist.txt",
    "https://reputation.alienvault.com/reputation.data",
    "https://rules.emergingthreats.net/open/suricata/rules/"
]

# 3. Behavioral Monitoring
# Suricata rules for anomaly detection
alert tcp any any -> any any (msg:"Potential Zero-Day Exploit"; 
  content:"unusual_pattern"; sid:1000001; rev:1;)

# 4. Machine Learning Pipeline
# Anomaly detection using isolation forest
from sklearn.ensemble import IsolationForest
import pandas as pd

def detect_anomalies(network_data):
    model = IsolationForest(contamination=0.1)
    model.fit(network_data)
    anomalies = model.predict(network_data)
    return network_data[anomalies == -1]

# Threat Intelligence Integration

# 1. STIX/TAXII Implementation
from stix2 import Indicator, Malware, Bundle
from taxii2client.v20 import Server

# Create threat intelligence indicators
indicator = Indicator(
    pattern="[file:hashes.MD5 = 'd41d8cd98f00b204e9800998ecf8427e']",
    labels=["malicious-activity"],
    created="2024-01-01T00:00:00.000Z",
    modified="2024-01-01T00:00:00.000Z"
)

# 2. MISP Integration
import pymisp

misp = pymisp.PyMISP(url, key, ssl=False)
event = misp.new_event(distribution=0, threat_level_id=2,
                       analysis=0, info="Zero-Day Vulnerability")

# 3. Custom Intelligence Pipeline
class ThreatIntelProcessor:
    def __init__(self):
        self.sources = []
        self.indicators = {}
    
    def add_source(self, source_url, source_type):
        self.sources.append({"url": source_url, "type": source_type})
    
    def process_indicators(self):
        for source in self.sources:
            # Fetch and process indicators
            pass
    
    def correlate_with_logs(self, log_data):
        # Correlate threat intel with security logs
        pass

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