analyzing-malware-persistence-with-autoruns 精选
Use Sysinternals Autoruns to systematically enumerate and analyze malware
安装方式
cp -r skills/analyzing-malware-persistence-with-autoruns ~/.claude/skills/analyzing-malware-persistence-with-autoruns把该 skill 目录放进客户端 skills 目录。不同客户端的目录可能不同,Claude Code 默认是 ~/.claude/skills/
/plugin marketplace add mukul975/Anthropic-Cybersecurity-Skills⚠️ 该命令形态随客户端版本变化,仅在检测到 .claude-plugin/marketplace.json 时展示;不可用时请用「目录复制」。
https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/54a798831d2266a3ca61ce68a7acb80b81160d57/skills/analyzing-malware-persistence-with-autoruns/SKILL.md下载后另存为 ~/.claude/skills/analyzing-malware-persistence-with-autoruns/SKILL.md。链接锁定采集时的 commit,内容与本站展示一致。
整仓一次性获取:git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git
依赖与权限
- 需要 API Key —— 使用前需配置相应的密钥环境变量
- 需要联网访问外部服务
- 依赖外部命令:
pythonpython3 - 以上为按正文与 frontmatter 自动推断的信号,仅供参考;安装前建议先阅读原文。
SKILL.md 正文
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
质量与来源
- 所属仓库
- mukul975/Anthropic-Cybersecurity-Skills
- 仓库星标
- 3.4 万
- 最近更新
- 1 个月前
- 采集时间
- 今天
- commit
- 54a798831d22
- 内容指纹
- sha256:0c2313ca57d31bc
- 文件路径
- skills/analyzing-malware-persistence-with-autoruns/SKILL.md