analyzing-linux-kernel-rootkits

mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026

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$npx skills install mukul975/Anthropic-Cybersecurity-Skills/analyzing-linux-kernel-rootkits
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summary

Detect kernel-level rootkits in Linux memory dumps using Volatility3 linux plugins (check_syscall, lsmod, hidden_modules), rkhunter system scanning, and /proc vs /sys discrepancy analysis to identify hooked syscalls, hidden kernel modules, and tampered system structures.

skill.md
name
analyzing-linux-kernel-rootkits
description
Detect kernel-level rootkits in Linux memory dumps using Volatility3 linux plugins (check_syscall, lsmod, hidden_modules), rkhunter system scanning, and /proc vs /sys discrepancy analysis to identify hooked syscalls, hidden kernel modules, and tampered system structures.
domain
cybersecurity
subdomain
digital-forensics
tags
- rootkit - linux - kernel - volatility3 - memory-forensics - malware-analysis - rkhunter - forensics
version
'1.0'
author
mahipal
license
Apache-2.0
nist_csf
- RS.AN-01 - RS.AN-03 - DE.AE-02 - RS.MA-01

Analyzing Linux Kernel Rootkits

Overview

Linux kernel rootkits operate at ring 0, modifying kernel data structures to hide processes, files, network connections, and kernel modules from userspace tools. Detection requires either memory forensics (analyzing physical memory dumps with Volatility3) or cross-view analysis (comparing /proc, /sys, and kernel data structures for inconsistencies). This skill covers using Volatility3 Linux plugins to detect syscall table hooks, hidden kernel modules, and modified function pointers, supplemented by live system scanning with rkhunter and chkrootkit.

When to Use

  • When investigating security incidents that require analyzing linux kernel rootkits
  • 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

  • Volatility3 installed (pip install volatility3)
  • Linux memory dump (acquired via LiME, AVML, or /proc/kcore)
  • Volatility3 Linux symbol table (ISF) matching the target kernel version
  • rkhunter and chkrootkit for live system scanning
  • Reference known-good kernel image for comparison

Steps

Step 1: Acquire Memory Dump

Capture Linux physical memory using LiME kernel module or AVML for cloud instances.

Step 2: Analyze with Volatility3

Run linux.check_syscall, linux.lsmod, linux.hidden_modules, and linux.check_idt plugins to detect rootkit artifacts.

Step 3: Cross-View Analysis

Compare module lists from /proc/modules, lsmod, and /sys/module to identify modules hidden from one view but present in another.

Step 4: Live System Scanning

Run rkhunter and chkrootkit to detect known rootkit signatures, suspicious files, and modified system binaries.

Expected Output

JSON report containing detected syscall hooks, hidden kernel modules, modified IDT entries, suspicious /proc discrepancies, and rkhunter findings.

Example Output

$ sudo python3 rootkit_analyzer.py --memory /evidence/linux-mem.lime --profile Ubuntu2204

Linux Kernel Rootkit Analysis Report
=====================================
Memory Image: /evidence/linux-mem.lime
Kernel Version: 5.15.0-91-generic (Ubuntu 22.04 LTS)
Analysis Time: 2024-01-18 09:15:32 UTC

[+] Scanning syscall table for hooks...
    Syscall Table Base: 0xffffffff82200300
    Total syscalls checked: 449

    HOOKED SYSCALLS DETECTED:
    ┌─────────┬──────────────────┬──────────────────────┬──────────────────────┐
    │ NR      │ Syscall          │ Expected Address     │ Current Address      │
    ├─────────┼──────────────────┼──────────────────────┼──────────────────────┤
    │ 0       │ sys_read         │ 0xffffffff8139a0e0   │ 0xffffffffc0a12000   │
    │ 2       │ sys_open         │ 0xffffffff8139b340   │ 0xffffffffc0a12180   │
    │ 78      │ sys_getdents64   │ 0xffffffff813f5210   │ 0xffffffffc0a12300   │
    │ 62      │ sys_kill         │ 0xffffffff8110c4a0   │ 0xffffffffc0a12480   │
    └─────────┴──────────────────┴──────────────────────┴──────────────────────┘
    WARNING: 4 syscall hooks detected - rootkit behavior confirmed

[+] Checking for hidden kernel modules...
    Loaded modules (lsmod):         147
    Modules in kobject list:        149
    HIDDEN MODULES:
      - "netfilter_helper" at 0xffffffffc0a10000 (size: 12288)
      - "kworker_sched"    at 0xffffffffc0a14000 (size: 8192)

[+] Scanning /proc for discrepancies...
    Processes in task_struct list: 234
    Processes visible in /proc:   231
    HIDDEN PROCESSES:
      - PID 31337  cmd: "[kworker/0:3]"   (disguised as kernel thread)
      - PID 31442  cmd: "rsyslogd"         (fake, real rsyslogd is PID 892)
      - PID 31500  cmd: ""                 (unnamed process)

[+] Checking IDT entries...
    IDT entries scanned: 256
    Modified entries: 0 (clean)

[+] Running rkhunter scan...
    Checking for known rootkits:        68 variants checked
    Diamorphine rootkit:                WARNING - signatures match
    System binary checks:
      /usr/bin/ps:     MODIFIED (SHA-256 mismatch)
      /usr/bin/netstat: MODIFIED (SHA-256 mismatch)
      /usr/bin/ls:     MODIFIED (SHA-256 mismatch)
      /usr/sbin/ss:    OK

[+] Network analysis...
    Hidden connections (not in /proc/net/tcp):
      ESTABLISHED  0.0.0.0:0 -> 198.51.100.47:4443 (PID 31337)
      ESTABLISHED  0.0.0.0:0 -> 198.51.100.47:8080 (PID 31442)

Summary:
  Rootkit Type:         Loadable Kernel Module (LKM)
  Probable Family:      Diamorphine variant
  Syscall Hooks:        4 (read, open, getdents64, kill)
  Hidden Modules:       2
  Hidden Processes:     3
  Hidden Connections:   2 (C2: 198.51.100.47)
  Modified Binaries:    3 (/usr/bin/ps, netstat, ls)
  Risk Level:           CRITICAL
how to use analyzing-linux-kernel-rootkits

How to use analyzing-linux-kernel-rootkits on Cursor

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1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add analyzing-linux-kernel-rootkits
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills install mukul975/Anthropic-Cybersecurity-Skills/analyzing-linux-kernel-rootkits

The skills CLI fetches analyzing-linux-kernel-rootkits from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/analyzing-linux-kernel-rootkits

Reload or restart Cursor to activate analyzing-linux-kernel-rootkits. Access the skill through slash commands (e.g., /analyzing-linux-kernel-rootkits) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

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general reviews

Ratings

4.528 reviews
  • Ganesh Mohane· Dec 4, 2024

    Useful defaults in analyzing-linux-kernel-rootkits — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Sakshi Patil· Nov 23, 2024

    analyzing-linux-kernel-rootkits is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Chaitanya Patil· Oct 14, 2024

    Keeps context tight: analyzing-linux-kernel-rootkits is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Aanya Robinson· Sep 25, 2024

    Solid pick for teams standardizing on skills: analyzing-linux-kernel-rootkits is focused, and the summary matches what you get after install.

  • Piyush G· Sep 5, 2024

    analyzing-linux-kernel-rootkits has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Evelyn Perez· Sep 1, 2024

    I recommend analyzing-linux-kernel-rootkits for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Shikha Mishra· Aug 24, 2024

    Solid pick for teams standardizing on skills: analyzing-linux-kernel-rootkits is focused, and the summary matches what you get after install.

  • Yuki Taylor· Aug 20, 2024

    Useful defaults in analyzing-linux-kernel-rootkits — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Ishan Lopez· Aug 16, 2024

    analyzing-linux-kernel-rootkits has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Rahul Santra· Jul 15, 2024

    We added analyzing-linux-kernel-rootkits from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

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