analyzing-linux-elf-malware

analyzing-linux-elf-malware is a skill for Claude Code from killvxk/cybersecurity-skills-zh. It costs 117 tokens per session (3,516 once invoked), scanned B, original, Apache-2.0.

A guide for examining Linux ELF files, the executable format used by Linux programs. It covers static inspection, runtime tracing, and reverse engineering of malware on x86-64, ARM, and MIPS systems.

In plain words
What is it for?
Use it to examine botnets, cryptocurrency miners, ransomware, rootkits, and malicious kernel modules, including malware running in Docker or Kubernetes.
Why use it?
It helps investigate suspicious programs found on Linux servers, containers, or cloud systems. It also makes clear that Windows PE files are outside its scope.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the cybersecurity-skills-zh plugin — 58 skills shipped together

Good fit Use it to examine botnets, cryptocurrency miners, ransomware, rootkits, and malicious kernel modules, including malware running in Docker or Kubernetes.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/killvxk/cybersecurity-skills-zh/analyzing-linux-elf-malware
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add killvxk/cybersecurity-skills-zh --skill analyzing-linux-elf-malware
Clone the repo
git clone --depth 1 https://github.com/killvxk/cybersecurity-skills-zh

Made for: Claude Code.

Or install cybersecurity-skills-zh, the plugin that ships this one along with the rest of its 58 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for analyzing-linux-elf-malware

README.md
[![agentmods](https://agentmods.dev/badge/skills/killvxk/cybersecurity-skills-zh/analyzing-linux-elf-malware.svg)](https://agentmods.dev/skills/killvxk/cybersecurity-skills-zh/analyzing-linux-elf-malware)
Your own site
<a href="https://agentmods.dev/skills/killvxk/cybersecurity-skills-zh/analyzing-linux-elf-malware"><img src="https://agentmods.dev/badge/skills/killvxk/cybersecurity-skills-zh/analyzing-linux-elf-malware.svg" alt="Measured on agentmods" height="20"></a>
Per session 117 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,516 The whole file, excluding the scripts and references it only reads on demand.
Security scan B 2 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00117 $0.03516
Opus 5 $0.00059 $0.01758
Sonnet 5 $0.00023 $0.00703
Haiku 4.5 $0.00012 $0.00352

Measured 8d ago against content hash 58dcf90acf24, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade B, and why

analyzing-linux-elf-malware scanned grade B with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/agent.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Reaches for credential filesmediumPrivilege escalation

SSH keys, cloud credentials, git-credentials, .npmrc, /etc/shadow: reading these is how a config file becomes a credential leak.

[2] SSH 密钥添加到 /root/.ssh/authorized_keys

Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

grep -iE "(bash|sh|wget|curl|chmod|/tmp/|/dev/)" strings_output.txt
skills/analyzing-linux-elf-malware/SKILL.md · 330 lines

How it starts

The opening of the file, as written. The whole thing — 330 lines — stays where its author put it; the contents beside it link to each section on GitHub.

分析 Linux ELF 恶意软件

适用场景

  • Linux 服务器或容器被攻陷,发现可疑 ELF 二进制文件
  • 分析 Linux 僵尸网络(Mirai、Gafgyt、XorDDoS)、挖矿程序或勒索软件
  • 调查针对云基础设施、Docker 容器或 Kubernetes Pod 的恶意软件
  • 逆向工程 Linux rootkit 和内核模块
  • 分析为 Linux x86_64、ARM 或 MIPS 架构编译的跨平台恶意软件

不适用于 Windows PE 二进制文件分析;请使用 PEStudio、Ghidra 或 IDA 分析 Windows 恶意软件。

前置条件

  • Ghidra 或支持 Linux ELF 的 IDA,用于反汇编和反编译
  • Linux 分析虚拟机(推荐 Ubuntu 22.04),已安装开发工具
  • strace、ltrace 和 GDB,用于动态分析和调试
  • GNU binutils 中的 readelf、objdump 和 nm,用于静态检查
  • Radare2,用于快速二进制分类和脚本化分析
  • Docker,用于隔离的基于容器的恶意软件执行

工作流程

步骤 1:识别 ELF 二进制文件属性

检查 ELF 头部和基本属性:

# 文件类型识别
file suspect_binary

# 详细 ELF 头部分析
readelf -h suspect_binary

# 节头部
readelf -S suspect_binary

# 程序头部(段)
readelf -l suspect_binary

# 符号表(如未去符号表)
readelf -s suspect_binary
nm suspect_binary 2>/dev/null

# 动态链接信息
readelf -d suspect_binary
ldd suspect_binary 2>/dev/null  # 仅在匹配架构上使用!

# 计算哈希值
md5sum suspect_binary
sha256sum suspect_binary

# 检查打包/UPX
upx -t suspect_binary
# 基于 Python 的 ELF 分析
from elftools.elf.elffile import ELFFile
import hashlib

with open("suspect_binary", "rb") as f:
    data = f.read()
    sha256 = hashlib.sha256(data).hexdigest()

with open("suspect_binary", "rb") as f:
    elf = ELFFile(f)

    print(f"SHA-256:      {sha256}")
    print(f"类型:          {elf.elfclass}-bit")
    print(f"字节序:        {'小端' if elf.little_endian else '大端'}")
    print(f"机器架构:      {elf.header.e_machine}")
    print(f"文件类型:      {elf.header.e_type}")
    print(f"入口点:        0x{elf.header.e_entry:X}")

    # 检查是否去符号表
    symtab = elf.get_section_by_name('.symtab')
    print(f"已去符号表:    {'是' if symtab is None else '否'}")

    # 节熵值分析
    import math
    from collections import Counter
    for section in elf.iter_sections():
        data = section.data()
        if len(data) > 0:
            entropy = -sum((c/len(data)) * math.log2(c/len(data))
                          for c in Counter(data).values() if c > 0)
            if entropy > 7.0:
                print(f"  [!] 高熵节:{section.name}({entropy:.2f})")

Read the full file on GitHub · 330 lines

Files

What ships with it

3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 8d ago First seen · 330 lines · 117 tokens per session scan B 58dcf90acf24

Subscribe to this mod's changes

analyzing-linux-elf-malware is a skill published in the GitHub repository killvxk/cybersecurity-skills-zh (44 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 117 tokens to every session and 3,516 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it B with 2 findings (reaches for credential files, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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