Analyzing Malware Persistence With Autoruns
- 272 installs
- 27.3k repo stars
- Updated August 2, 2026
- mukul975/anthropic-cybersecurity-skills
Structure Windows malware persistence reviews with Autoruns findings, extracted IOCs, and remediation steps in a consistent report.
About
Analyzing Malware Persistence With Autoruns is an agent skill that supplies a structured security analysis report template for documenting persistence discovered via Sysinternals Autoruns-style review. Solo builders and small security-minded teams use it when they need defensible, repeatable write-ups after suspicious startup entries, scheduled tasks, or lateral footholds on Windows endpoints—not a substitute for running Autoruns itself, but the editorial shell around what you found. The template forces explicit sample provenance, tabulated findings by severity, IOC tables for threat intel handoff, and ordered recommendations so an agent or analyst does not ship a vague narrative. It suits indie operators shipping internal tools, consultants producing client deliverables, or developers validating a compromised machine before restore. Pair it with your actual forensic commands and log pulls; the skill standardizes the markdown artifact your coding agent can fill incrementally as analysis proceeds.
- Pre-built analysis report with sample metadata, SHA-256, TLP:AMBER classification row
- Findings table pairing severity with narrative details for persistence mechanisms
- Dedicated IOCs Extracted section (type, value, context)
- Numbered Recommendations list for follow-up containment and cleanup
- Apache 2.0–licensed template aligned with enterprise security reporting norms
Analyzing Malware Persistence With Autoruns by the numbers
- 272 all-time installs (skills.sh)
- +17 installs in the week ending Aug 4, 2026 (Skillselion tracking)
- Ranked #651 of 2,203 Security skills by installs in the Skillselion catalog
- Security screen: LOW risk (skills.sh audit)
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 272 |
|---|---|
| repo stars | ★ 27.3k |
| Security audit | 3 / 3 scanners passed |
| Last updated | August 2, 2026 |
| Repository | mukul975/anthropic-cybersecurity-skills ↗ |
What it does
Structure Windows malware persistence reviews with Autoruns findings, extracted IOCs, and remediation steps in a consistent report.
Files
Analyzing Malware Persistence with Autoruns
Overview
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.
When to Use
- When investigating security incidents that require analyzing malware persistence with autoruns
- When building detection rules or threat hunting queries for this domain
- When SOC analysts need structured procedures for this analysis type
- When validating security monitoring coverage for related attack techniques
Prerequisites
- Sysinternals Autoruns (GUI) and Autorunsc (CLI)
- Administrative privileges on target system
- Python 3.9+ for automated analysis
- VirusTotal API key for reputation checks
- Clean baseline export for comparison
Workflow
Step 1: Automated Persistence Scanning
#!/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}")Validation Criteria
- All ASEP categories scanned and cataloged
- Unsigned entries flagged for investigation
- Suspicious paths and LOLBin launch strings highlighted
- Baseline comparison identifies new persistence mechanisms
References
Analysis Report Template - analyzing-malware-persistence-with-autoruns
Sample Information
| Field | Value |
|---|---|
| SHA-256 | |
| File Type | |
| Analysis Date | |
| Analyst | |
| Classification | TLP:AMBER |
Findings
| Finding | Severity | Details |
|---|---|---|
IOCs Extracted
| Type | Value | Context |
|---|---|---|
Recommendations
1. 2. 3.
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API Reference: Autoruns Persistence Analysis
Autoruns CLI (autorunsc.exe)
autorunsc.exe -a * -c -h -s -v -vt -o autoruns.csv| Flag | Description |
|---|---|
-a * | All autostart categories |
-c | CSV output |
-h | Show file hashes |
-s | Verify digital signatures |
-v | Verify signatures against catalog |
-vt | Check VirusTotal |
-o | Output file |
CSV Columns
| Column | Description |
|---|---|
| Time | Entry timestamp |
| Entry Location | Registry key or path |
| Entry | Entry name |
| Enabled | enabled/disabled |
| Category | Autoruns category |
| Description | File description |
| Company | Publisher name |
| Image Path | Full binary path |
| Launch String | Complete command line |
| MD5 / SHA-1 / SHA-256 | File hashes |
| Signer | Code signing status |
| VT detection | VirusTotal ratio (e.g., "5/72") |
Autostart Categories
| Category | Examples |
|---|---|
| Logon | Run/RunOnce keys, Startup folder |
| Services | Windows services |
| Drivers | Kernel drivers |
| Scheduled Tasks | Task Scheduler entries |
| Winlogon | Shell, Userinit, Notify |
| WMI | Event subscriptions |
| AppInit | AppInit_DLLs |
| Boot Execute | BootExecute values |
| Image Hijacks | IFEO debugger entries |
| LSA Providers | Authentication packages |
Suspicious Indicators
| Indicator | Significance |
|---|---|
| VT detection > 0 | Known malware |
| Unsigned binary | Potential unsigned malware |
| LOLBin in launch string | Living-off-the-land |
| Path in %TEMP% or %PUBLIC% | Staging location |
| Missing company info | Suspicious unsigned entry |
MITRE ATT&CK Persistence
- T1547.001 - Registry Run Keys / Startup Folder
- T1053.005 - Scheduled Task
- T1543.003 - Windows Service
- T1546.003 - WMI Event Subscription
Standards Reference - analyzing-malware-persistence-with-autoruns
Applicable Standards
- MITRE ATT&CK Framework
- NIST SP 800-83 Guide to Malware Incident Prevention
- NIST SP 800-86 Guide to Integrating Forensic Techniques
Related MITRE ATT&CK Techniques
See SKILL.md for specific technique mappings.
Analysis Workflows - analyzing-malware-persistence-with-autoruns
Primary Workflow
[Sample Collection] --> [Static Analysis] --> [Dynamic Analysis] --> [IOC Extraction]
|
v
[Report Generation]See SKILL.md for detailed step-by-step procedures.
#!/usr/bin/env python3
"""Autoruns Persistence Analysis Agent - Analyzes Windows autostart entries for malware persistence."""
import json
import csv
import re
import logging
import argparse
from datetime import datetime
from collections import Counter
logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
logger = logging.getLogger(__name__)
SUSPICIOUS_PATHS = [
r"\\temp\\", r"\\tmp\\", r"\\appdata\\local\\temp",
r"\\public\\", r"\\programdata\\", r"\\users\\default",
r"\\recycler\\", r"\\windows\\debug",
]
SUSPICIOUS_COMMANDS = [
"powershell", "cmd.exe /c", "wscript", "cscript", "mshta",
"regsvr32", "rundll32", "certutil", "bitsadmin",
"schtasks", "msiexec /q", "forfiles",
]
KNOWN_PERSISTENCE_LOCATIONS = [
"HKLM\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Run",
"HKCU\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Run",
"HKLM\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\RunOnce",
"HKLM\\SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\Winlogon",
"HKLM\\SYSTEM\\CurrentControlSet\\Services",
"Task Scheduler",
"Startup Folder",
"WMI",
]
def parse_autoruns_csv(csv_file):
"""Parse Autoruns CSV export file."""
entries = []
with open(csv_file, "r", encoding="utf-8-sig", errors="ignore") as f:
reader = csv.DictReader(f, delimiter=",")
for row in reader:
entries.append({
"time": row.get("Time", ""),
"entry_location": row.get("Entry Location", ""),
"entry": row.get("Entry", ""),
"enabled": row.get("Enabled", ""),
"category": row.get("Category", ""),
"profile": row.get("Profile", ""),
"description": row.get("Description", ""),
"company": row.get("Company", ""),
"image_path": row.get("Image Path", ""),
"version": row.get("Version", ""),
"launch_string": row.get("Launch String", ""),
"md5": row.get("MD5", ""),
"sha1": row.get("SHA-1", ""),
"sha256": row.get("SHA-256", ""),
"signer": row.get("Signer", ""),
"vt_detection": row.get("VT detection", ""),
})
logger.info("Parsed %d autoruns entries from %s", len(entries), csv_file)
return entries
def analyze_entry(entry):
"""Analyze a single autoruns entry for suspicious indicators."""
findings = []
image_path = (entry.get("image_path") or "").lower()
launch_string = (entry.get("launch_string") or "").lower()
signer = entry.get("signer") or ""
vt = entry.get("vt_detection") or ""
company = entry.get("company") or ""
for pattern in SUSPICIOUS_PATHS:
if re.search(pattern, image_path, re.IGNORECASE):
findings.append({"type": "Suspicious file path", "severity": "high", "detail": image_path})
break
for cmd in SUSPICIOUS_COMMANDS:
if cmd.lower() in launch_string:
findings.append({"type": "LOLBin in launch string", "severity": "high", "detail": cmd})
break
if signer in ("(Not verified)", "") or "(Not verified)" in signer:
findings.append({"type": "Unsigned binary", "severity": "medium", "detail": signer})
if vt and "/" in vt:
try:
detections, total = vt.split("/")
if int(detections.strip()) > 0:
findings.append({"type": "VirusTotal detections", "severity": "critical", "detail": vt})
except (ValueError, AttributeError):
pass
if not company and entry.get("enabled") == "enabled":
findings.append({"type": "No company info", "severity": "low", "detail": "Enabled entry without publisher"})
return findings
def analyze_all_entries(entries):
"""Analyze all autoruns entries and generate findings."""
all_findings = []
for entry in entries:
entry_findings = analyze_entry(entry)
if entry_findings:
all_findings.append({
"entry": entry.get("entry"),
"location": entry.get("entry_location"),
"category": entry.get("category"),
"image_path": entry.get("image_path"),
"findings": entry_findings,
"max_severity": max((f["severity"] for f in entry_findings), key=lambda s: {"critical": 4, "high": 3, "medium": 2, "low": 1}.get(s, 0)),
})
return all_findings
def generate_report(entries, findings):
"""Generate persistence analysis report."""
categories = Counter(e.get("category", "Unknown") for e in entries)
critical = [f for f in findings if f["max_severity"] == "critical"]
report = {
"timestamp": datetime.utcnow().isoformat(),
"total_entries": len(entries),
"enabled_entries": len([e for e in entries if e.get("enabled") == "enabled"]),
"suspicious_entries": len(findings),
"critical_entries": len(critical),
"category_breakdown": dict(categories.most_common()),
"findings": findings,
}
print(f"AUTORUNS REPORT: {len(entries)} entries, {len(findings)} suspicious, {len(critical)} critical")
return report
def main():
parser = argparse.ArgumentParser(description="Autoruns Persistence Analysis Agent")
parser.add_argument("--csv-file", required=True, help="Autoruns CSV export file")
parser.add_argument("--output", default="autoruns_report.json")
args = parser.parse_args()
entries = parse_autoruns_csv(args.csv_file)
findings = analyze_all_entries(entries)
report = generate_report(entries, findings)
with open(args.output, "w") as f:
json.dump(report, f, indent=2)
logger.info("Report saved to %s", args.output)
if __name__ == "__main__":
main()
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Is Analyzing Malware Persistence With Autoruns safe to install?
skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.