Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification.
日本語の概要は準備中です。原文の説明を表示しています。
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.
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
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
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概要と使いどころ
Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification.
日本語の概要は準備中です。原文の説明を表示しています。
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
日本語の概要は準備中です。原文の説明を表示しています。
Detect dangerous ACL misconfigurations in Active Directory using ldap3 to identify GenericAll, WriteDACL, and WriteOwner abuse paths
日本語の概要は準備中です。原文の説明を表示しています。
Perform static analysis of Android APK malware samples using apktool for decompilation, jadx for Java source recovery, and androguard for permission analysis, manifest inspection, and suspicious API call detection.
日本語の概要は準備中です。原文の説明を表示しています。
Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect BOLA/IDOR attacks, rate limit bypass, credential scanning, and injection attempts. Uses pandas for statistical analysis of request patterns and anomaly detection. Use when investigating API abuse or building API-specific threat detection rules.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze advanced persistent threat (APT) group techniques using MITRE ATT&CK Navigator to create layered heatmaps of adversary TTPs for detection gap analysis and threat-informed defense.
日本語の概要は準備中です。原文の説明を表示しています。