analyzing-linux-kernel-rootkits
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.
- License 许可
- Apache-2.0
- 最近更新
- 2026-10-08
- Stars
- ★ 11
- Forks
- 3
01 前言 Front Matter
- name
- analyzing-linux-kernel-rootkits
- tags
- [ "rootkit", "linux", "kernel", "volatility3", "memory-forensics", "malware-analysis", "rkhunter", "forensics" ]
- author
- mahipal
- domain
- cybersecurity
- license
- Apache-2.0
- version
- 1.0
- nist_csf
- [ "RS.AN-03", "DE.AE-02", "RS.MA-01" ]
- subdomain
- digital-forensics
- 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.
- mitre_attack
- [ "T1014", "T1547.006", "T1564.001" ]
02 资源文件 1
- 参考文档references/api-reference.md2.5 KB
03 SKILL.md 正文
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
04 同仓库更多 Skills
ci-cd-and-automation
Automates CI/CD pipeline setup. Use when setting up or modifying build and deployment pipelines. Use when you need to automate quality gates, configure test runners in CI, or establish deployment strategies.
chrome-devtools
Uses Chrome DevTools via MCP for efficient debugging, troubleshooting and browser automation. Use when debugging web pages, automating browser interactions, analyzing performance, or inspecting network requests. This skill does not apply to `--slim` mode (MCP configuration).
chrome-cdp
Drive a real Chrome via the Chrome DevTools Protocol (CDP). Ensures a debuggable Chrome is running (launching it if needed) so the agent can navigate, read page content, inspect the DOM, run JavaScript, and take screenshots — without the user typing any launch command. Includes a first-class "review a live running app" flow: point Chrome at the app under development and inspect/screenshot the REAL page (what a static-artifact tool cannot do). Backed by Glimpse and the chrome-devtools MCP server. Use when asked to "use chrome", "open chrome", "drive my browser", "read the page in chrome", "review the running app", "screenshot the app", or "/chrome-cdp".
check-identity-pack
Run an AFP 100-point or AUSTRAC safe-harbour identity check over a set of documents, and report exactly what's missing. Use when the user asks to check identity documents, verify someone's ID for onboarding, or assess whether a document pack satisfies Australian identity requirements.
changelog
Auto-generates a changelog from git commits, sprint data, and design documents. Produces both internal and player-facing versions.
cellxgene-census
Query the CZ CELLxGENE Census programmatically for versioned public single-cell and spatial transcriptomics data. Use when you need population-scale cell metadata, gene expression slices, Census summary counts, source H5AD URIs/downloads, embeddings, spatial Census data, or reference atlas comparisons across organisms, tissues, diseases, assays, and cell types. For analyzing your own local single-cell data use scanpy, anndata, or scvi-tools.