abusing-dpapi-for-cred…
Extract and decrypt Windows DPAPI-protected secrets (Credential Manager, browser logins/cookies, Wi-Fi credentials, KeePass keys)…
Use Sysinternals Autoruns to systematically enumerate and analyze malware
Claude Code
$ npx -y skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-malware-persistence-with-autoruns --agent claude-code
Codex
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-malware-persistence-with-autoruns -a codex -y
Cursor
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-malware-persistence-with-autoruns -a cursor -y
Gemini CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-malware-persistence-with-autoruns -a gemini-cli -y
GitHub Copilot
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-malware-persistence-with-autoruns -a github-copilot -y
OpenCode
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-malware-persistence-with-autoruns -a opencode -y
Windsurf
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-malware-persistence-with-autoruns -a windsurf -y
name: analyzing-malware-persistence-with-autoruns description: Use Sysinternals Autoruns to systematically enumerate and analyze malware persistence mechanisms across Windows registry run keys, scheduled tasks, services, drivers, and startup locations. Use when hunting for persistence during Windows incident response, triaging a compromised endpoint, or validating that malware autostart entries have been fully identified and removed. domain: cybersecurity subdomain: malware-analysis tags: - autoruns - persistence - malware-analysis - sysinternals - windows - registry - startup - incident-response mitre_attack: - T1547.001 - T1543.003 - T1053.005 - T1574.001 - T1037.001 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}")817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io standard · Works with Claude Code, GitHub Copilot, Codex CLI, Cursor, Gemini CLI & 20+ platforms · 29 security domains · Apache 2.0
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