Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification.
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Deploys deception-based honeytokens in Active Directory including fake privileged accounts with AdminCount=1, fake SPNs for Kerberoasting detection (honeyroasting), decoy GPOs with cpassword traps, and fake BloodHound paths. Monitors Windows Security Event IDs 4769, 4625, 4662, 5136 for honeytoken interaction. Use when implementing AD deception defenses for detecting lateral movement, credential theft, and reconnaissance.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Traditional signature-based detection misses novel attack techniques. Honeytokens provide high-fidelity detection with near-zero false positives because any interaction with a decoy object is inherently suspicious. In Active Directory:
| Event ID | Description | Honeytoken Use |
|---|---|---|
| 4769 | Kerberos TGS ticket requested | Detect Kerberoast against honey SPN |
| 4625 | Failed logon attempt | Detect use of fake credentials from decoy GPO |
| 4662 | Directory service object accessed | Detect DACL read on honeytoken user |
| 5136 | Directory service object modified | Detect modification of decoy GPO |
| 5137 | Directory service object created | Detect GPO creation mimicking decoy |
| 4768 | Kerberos TGT requested | Detect AS-REP roasting of honey account |
Per Trimarc Security research, effective honeytokens must appear legitimate:
Create a honeytoken account that mimics a legacy privileged service account.
# Import the deployment module
Import-Module .\scripts\Deploy-ADHoneytokens.ps1
# Create a honeytoken admin account
$honeyAdmin = New-HoneytokenAdmin `
-SamAccountName "svc_sqlbackup_legacy" `
-DisplayName "SQL Backup Service (Legacy)" `
-Description "Legacy SQL Server backup service account - DO NOT DELETE" `
-OU "OU=Service Accounts,DC=corp,DC=example,DC=com" `
-PasswordLength 128 `
-SetAdminCount $true
Write-Host "Honeytoken admin created: $($honeyAdmin.DistinguishedName)"
Assign a realistic but fake SPN to the honeytoken account. Any TGS request for this SPN is definitively malicious (honeyroasting).
# Add fake SPN to honeytoken account
$honeySPN = Add-HoneytokenSPN `
-SamAccountName "svc_sqlbackup_legacy" `
-ServiceClass "MSSQLSvc" `
-Hostname "sql-legacy-bak01.corp.example.com" `
-Port 1433
Write-Host "Honey SPN registered: $($honeySPN.SPN)"
Write-Host "Monitor Event ID 4769 for TGS requests targeting this SPN"
Create a fake GPO in SYSVOL with an embedded cpassword (Group Policy Preference password). Attackers using tools like Get-GPPPassword or gpp-decrypt will find and attempt to use these credentials, triggering detection.
# Create decoy GPO with cpassword trap
$decoyGPO = New-DecoyGPO `
-GPOName "Server Maintenance Policy (Legacy)" `
-DecoyUsername "admin_maintenance" `
-DecoyDomain "CORP" `
-SYSVOLPath "\\corp.example.com\SYSVOL\corp.example.com\Policies" `
-EnableAuditSACL $true
Write-Host "Decoy GPO created: $($decoyGPO.GPOGuid)"
Write-Host "SACL audit enabled - any read attempt will generate Event ID 4663"
Set ACL permissions that create fake attack paths visible to BloodHound/SharpHound reconnaissance, leading attackers toward monitored honeytokens.
# Create fake BloodHound attack path
$deceptivePath = New-DeceptiveBloodHoundPath `
-HoneytokenSamAccount "svc_sqlbackup_legacy" `
-TargetHighValueGroup "Domain Admins" `
-IntermediateOU "OU=Service Accounts,DC=corp,DC=example,DC=com"
Write-Host "Deceptive path created: $($deceptivePath.PathDescription)"
Set up SIEM detection rules to alert on any honeytoken interaction.
# Using the Python detection agent
from agent import ADHoneytokenMonitor
monitor = ADHoneytokenMonitor(config_path="honeytoken_config.json")
# Register all honeytokens for monitoring
monitor.register_honeytoken("svc_sqlbackup_legacy", token_type="admin_account")
monitor.register_honeytoken("MSSQLSvc/sql-legacy-bak01.corp.example.com:1433", token_type="spn")
monitor.register_honeytoken("admin_maintenance", token_type="gpo_credential")
# Generate SIEM detection rules
splunk_rules = monitor.generate_detection_rules(siem="splunk")
sentinel_rules = monitor.generate_detection_rules(siem="sentinel")
sigma_rules = monitor.generate_detection_rules(siem="sigma")
for rule in sigma_rules:
print(f"Rule: {rule['title']}")
print(f" Detection: {rule['detection_logic']}")
Test the honeytokens to ensure detection fires correctly.
# Validate honeytoken deployment
$validation = Test-HoneytokenDeployment `
-SamAccountName "svc_sqlbackup_legacy" `
-ValidateAdminCount `
-ValidateSPN `
-ValidateGPODecoy `
-ValidateAuditPolicy
$validation | Format-Table Check, Status, Details -AutoSize
Import-Module .\scripts\Deploy-ADHoneytokens.ps1
# Deploy complete honeytoken suite
$deployment = Deploy-FullHoneytokenSuite `
-Environment "Production" `
-ServiceAccountOU "OU=Service Accounts,DC=corp,DC=example,DC=com" `
-SYSVOLPath "\\corp.example.com\SYSVOL\corp.example.com\Policies" `
-TokenCount 3 `
-IncludeSPN $true `
-IncludeGPODecoy $true `
-IncludeBloodHoundPath $true `
-SIEMType "Splunk"
# Output deployment report
$deployment.Tokens | Format-Table Name, Type, SPN, DetectionRule -AutoSize
$deployment | Export-Csv "honeytoken_deployment_report.csv" -NoTypeInformation
index=wineventlog EventCode=4769 ServiceName="svc_sqlbackup_legacy"
| eval alert_severity="critical"
| eval alert_type="honeytoken_kerberoast"
| table _time, src_ip, Account_Name, ServiceName, Ticket_Encryption_Type
| sort - _time
SecurityEvent
| where EventID == 4769
| where ServiceName in ("svc_sqlbackup_legacy", "svc_exchange_legacy")
| extend AlertType = "Honeytoken Kerberoast Detected"
| project TimeGenerated, Computer, Account, ServiceName, IpAddress, TicketEncryptionType
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Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification.
中文简介正在准备中。当前显示原文说明。
Detect dangerous ACL misconfigurations in Active Directory using ldap3 to identify GenericAll, WriteDACL, and WriteOwner abuse paths
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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.
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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.
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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.
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Queries Azure Monitor activity logs and sign-in logs via azure-monitor-query to detect suspicious administrative operations, impossible travel, privilege escalation, and resource modifications. Builds KQL queries for threat hunting in Azure environments. Use when investigating suspicious Azure tenant activity or building cloud SIEM detections.
中文简介正在准备中。当前显示原文说明。