acquiring-disk-image-w…
Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through
Use Sysinternals Autoruns to systematically identify and analyze malware persistence mechanisms across registry
$ npx -y skills add Mikaru0Mystic/sectinel --skill analyzing-malware-persistence-with-autoruns --agent claude-codeHow it fires
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Use Sysinternals Autoruns to systematically identify and analyze malware persistence mechanisms across registry
name: analyzing-malware-persistence-with-autoruns description: Use Sysinternals Autoruns to systematically identify and analyze malware persistence mechanisms across registry keys, scheduled tasks, services, drivers, and startup locations on Windows systems. domain: cybersecurity subdomain: malware-analysis tags: - autoruns - persistence - malware-analysis - sysinternals - windows - registry - startup - incident-response mitre_attack: - T1547 - T1053 - T1543 - T1546 version: '1.0' author: mahipal license: Apache-2.0 d3fend_techniques: - Executable Denylisting - Execution Isolation - File Metadata Consistency Validation - Content Format Conversion - File Content Analysis nist_csf: - DE.AE-02 - RS.AN-03 - ID.RA-01 - DE.CM-01
Sysinternals Autoruns extracts data from hundreds of Auto-Start Extensibility Points (ASEPs) on Windows, scanning 18+ categories including Run/RunOnce keys, services, scheduled tasks, drivers, Winlogon entries, LSA providers, print monitors, WMI subscriptions, and AppInit DLLs. Digital signature verification filters Microsoft-signed entries. The compare function identifies newly added persistence via baseline diffing. VirusTotal integration checks hash reputation. Offline analysis via -z flag enables forensic disk image examination.
#!/usr/bin/env python3
"""Automate Autoruns-based persistence analysis."""
import subprocess
import csv
import json
import sys
def scan_and_analyze(autorunsc_path="autorunsc64.exe", csv_path="scan.csv"):
cmd = [autorunsc_path, "-a", "*", "-c", "-h", "-s", "-nobanner", "*"]
result = subprocess.run(cmd, capture_output=True, text=True, timeout=600)
with open(csv_path, 'w') as f:
f.write(result.stdout)
return parse_and_flag(csv_path)
def parse_and_flag(csv_path):
suspicious = []
with open(csv_path, 'r', errors='replace') as f:
for row in csv.DictReader(f):
reasons = []
signer = row.get("Signer", "")
if not signer or signer == "(Not verified)":
reasons.append("Unsigned binary")
if not row.get("Description") and not row.get("Company"):
reasons.append("Missing metadata")
path = row.get("Image Path", "").lower()
for sp in ["\temp\\", "\appdata\local\temp", "\users\public\\"]:
if sp in path:
reasons.append(f"Suspicious path")
launch = row.get("Launch String", "").lower()
for kw in ["powershell", "cmd /c", "wscript", "mshta", "regsvr32"]:
if kw in launch:
reasons.append(f"LOLBin: {kw}")
if reasons:
row["reasons"] = reasons
suspicious.append(row)
return suspicious
if __name__ == "__main__":
if len(sys.argv) > 1:
results = parse_and_flag(sys.argv[1])
print(f"[!] {len(results)} suspicious entries")
for r in results:
print(f" {r.get('Entry','')} - {r.get('Image Path','')}")
for reason in r.get('reasons', []):
print(f" - {reason}")Open-source security arsenal for AI coding agents: 784 cybersecurity skills, scanner integrations, and a security MCP for Claude Code, Cursor, opencode, Gemini CLI, Cline, and any agentskills.io agent. Mapped to OWASP, MITRE ATT&CK, NIST CSF, D3FEND, ATLAS.
Repo: Mikaru0Mystic/sectinel
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