external/anthropic-cybersecurity-skills/skills/analyzing-linux-kernel-rootkits/SKILL.md
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.
npx skillsauth add seikaikyo/dash-skills analyzing-linux-kernel-rootkitsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
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.
Capture Linux physical memory using LiME kernel module or AVML for cloud instances.
Run linux.check_syscall, linux.lsmod, linux.hidden_modules, and linux.check_idt plugins to detect rootkit artifacts.
Compare module lists from /proc/modules, lsmod, and /sys/module to identify modules hidden from one view but present in another.
Run rkhunter and chkrootkit to detect known rootkit signatures, suspicious files, and modified system binaries.
JSON report containing detected syscall hooks, hidden kernel modules, modified IDT entries, suspicious /proc discrepancies, and rkhunter findings.
$ 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
tools
Conduct comprehensive GDPR compliance assessments by evaluating data processing activities against EU Regulation 2016/679, including Article 30 records of processing, lawful basis validation, data subject rights implementation, Data Protection Impact Assessments (DPIAs) under Article 35, breach notification procedures, international transfer safeguards (SCCs, adequacy decisions), and technical/organizational measures under Article 32. Use when processing personal data of EU residents, preparing for supervisory authority audits, implementing privacy-by-design for new systems, scoping compliance gaps for M&A due diligence, assessing third-party processors, or responding to data subject access requests at scale. Incorporates 2026 guidance from ICO, EDPB, and post-Data (Use and Access) Act 2025 UK-GDPR considerations. Do not use for implementing specific Article 32 controls — use implementing-gdpr-data-protection-controls; or for DSAR automation — use implementing-gdpr-data-subject-access-request.
tools
Parse Windows forensic artifacts—$MFT/$J (MFTECmd), Prefetch (PECmd), registry hives (RECmd), shellbags, and Amcache—into normalized CSV/JSON with Eric Zimmerman's EZ Tools, then load results into Timeline Explorer for analysis. Use during DFIR/incident-response investigations, after triage collection (e.g. with KAPE), to establish program execution, file/folder access, and persistence evidence from acquired forensic images.
development
Build automated multi-turn adversarial attacks against conversational LLM targets using Microsoft PyRIT's RedTeamingOrchestrator, CrescendoOrchestrator (gradual escalation), and TreeOfAttacksWithPruningOrchestrator (adaptive branching), with scorer feedback loops and persisted conversation memory. Use when single-shot LLM scanning is insufficient and you need multi-turn, scorer-driven AI red-team campaigns against a chatbot or agent.
testing
Stand up MISP, enable and cache curated threat feeds (CIRCL, abuse.ch, Feodo Tracker), apply warninglists to suppress false positives, query indicators with PyMISP, and export attributes as auto-generated Suricata/Sigma/Wazuh detection rules. Use when maturing a MISP instance to actively drive detection, curating threat feeds with quality controls, or automating IOC-to-detection pipelines for the SIEM/IDS.