Analyzing Linux System Artifacts logo

Analyzing Linux System Artifacts

CommunityPopular
mukul975
analyzing-linux-system-artifacts

Examine Linux system artifacts (auth logs, cron/systemd persistence, shell history, SSH keys, and system configuration) to uncover evidence of compromise, detect rootkits or backdoors, and reconstruct user/attacker activity. Use when investigating a compromised Linux server or workstation, hunting for persistence mechanisms, or scoping a Linux-based breach during incident response.

Overview

Publishermukul975
RepositoryAnthropic-Cybersecurity-Skills
Skill nameanalyzing-linux-system-artifacts
Stars
32.9K
Forks
4K
Bundled files
2
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by mukul975 on GitHub. Read the source before you install it.

Installation

Install the Analyzing Linux System Artifacts AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git /tmp/Anthropic-Cybersecurity-Skills
mkdir -p .claude/skills
cp -r /tmp/Anthropic-Cybersecurity-Skills/skills/analyzing-linux-system-artifacts .claude/skills/analyzing-linux-system-artifacts
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Analyzing Linux System Artifacts in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Analyzing Linux System Artifacts on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Analyzing Linux System Artifacts is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Analyzing Linux System Artifacts

When to Use

  • When investigating a compromised Linux server or workstation
  • For identifying persistence mechanisms (cron, systemd, SSH keys)
  • When tracing user activity through shell history and authentication logs
  • During incident response to determine the scope of a Linux-based breach
  • For detecting rootkits, backdoors, and unauthorized modifications

Prerequisites

  • Forensic image or live access to the Linux system (read-only)
  • Understanding of Linux file system hierarchy (FHS)
  • Knowledge of common Linux logging locations (/var/log/)
  • Tools: chkrootkit, rkhunter, AIDE, auditd logs
  • Familiarity with systemd, cron, and PAM configurations
  • Root access for complete artifact collection

Workflow

Step 1: Mount and Collect System Artifacts

bash
# Mount forensic image read-only
mount -o ro,loop,offset=$((2048*512)) /cases/case-2024-001/images/linux_evidence.dd /mnt/evidence

# Create collection directories
mkdir -p /cases/case-2024-001/linux/{logs,config,users,persistence,network}

# Collect authentication logs
cp /mnt/evidence/var/log/auth.log* /cases/case-2024-001/linux/logs/
cp /mnt/evidence/var/log/secure* /cases/case-2024-001/linux/logs/
cp /mnt/evidence/var/log/syslog* /cases/case-2024-001/linux/logs/
cp /mnt/evidence/var/log/kern.log* /cases/case-2024-001/linux/logs/
cp /mnt/evidence/var/log/audit/audit.log* /cases/case-2024-001/linux/logs/
cp /mnt/evidence/var/log/wtmp /cases/case-2024-001/linux/logs/
cp /mnt/evidence/var/log/btmp /cases/case-2024-001/linux/logs/
cp /mnt/evidence/var/log/lastlog /cases/case-2024-001/linux/logs/
cp /mnt/evidence/var/log/faillog /cases/case-2024-001/linux/logs/

# Collect user artifacts
for user_dir in /mnt/evidence/home/*/; do
    username=$(basename "$user_dir")
    mkdir -p /cases/case-2024-001/linux/users/$username
    cp "$user_dir"/.bash_history /cases/case-2024-001/linux/users/$username/ 2>/dev/null
    cp "$user_dir"/.zsh_history /cases/case-2024-001/linux/users/$username/ 2>/dev/null
    cp -r "$user_dir"/.ssh/ /cases/case-2024-001/linux/users/$username/ 2>/dev/null
    cp "$user_dir"/.bashrc /cases/case-2024-001/linux/users/$username/ 2>/dev/null
    cp "$user_dir"/.profile /cases/case-2024-001/linux/users/$username/ 2>/dev/null
    cp "$user_dir"/.viminfo /cases/case-2024-001/linux/users/$username/ 2>/dev/null
    cp "$user_dir"/.wget-hsts /cases/case-2024-001/linux/users/$username/ 2>/dev/null
    cp "$user_dir"/.python_history /cases/case-2024-001/linux/users/$username/ 2>/dev/null
done

# Collect root user artifacts
cp /mnt/evidence/root/.bash_history /cases/case-2024-001/linux/users/root/ 2>/dev/null
cp -r /mnt/evidence/root/.ssh/ /cases/case-2024-001/linux/users/root/ 2>/dev/null

# Collect system configuration
cp /mnt/evidence/etc/passwd /cases/case-2024-001/linux/config/
cp /mnt/evidence/etc/shadow /cases/case-2024-001/linux/config/
cp /mnt/evidence/etc/group /cases/case-2024-001/linux/config/
cp /mnt/evidence/etc/sudoers /cases/case-2024-001/linux/config/
cp -r /mnt/evidence/etc/sudoers.d/ /cases/case-2024-001/linux/config/
cp /mnt/evidence/etc/hosts /cases/case-2024-001/linux/config/
cp /mnt/evidence/etc/resolv.conf /cases/case-2024-001/linux/config/
cp -r /mnt/evidence/etc/ssh/ /cases/case-2024-001/linux/config/

Step 2: Analyze User Accounts and Authentication

bash
# Analyze user accounts for anomalies
python3 << 'PYEOF'
print("=== USER ACCOUNT ANALYSIS ===\n")

# Parse /etc/passwd
with open('/cases/case-2024-001/linux/config/passwd') as f:
    for line in f:
        parts = line.strip().split(':')
        if len(parts) >= 7:
            username, _, uid, gid, comment, home, shell = parts[0], parts[1], int(parts[2]), int(parts[3]), parts[4], parts[5], parts[6]

            # Flag accounts with UID 0 (root equivalent)
            if uid == 0 and username != 'root':
                print(f"  ALERT: UID 0 account: {username} (shell: {shell})")

            # Flag accounts with login shells that shouldn't have them
            if shell not in ('/bin/false', '/usr/sbin/nologin', '/bin/sync') and uid >= 1000:
                print(f"  User: {username} (UID:{uid}, Shell:{shell}, Home:{home})")

            # Flag system accounts with login shells
            if uid < 1000 and uid > 0 and shell in ('/bin/bash', '/bin/sh', '/bin/zsh'):
                print(f"  WARNING: System account with shell: {username} (UID:{uid}, Shell:{shell})")

# Parse /etc/shadow for account status
print("\n=== PASSWORD STATUS ===")
with open('/cases/case-2024-001/linux/config/shadow') as f:
    for line in f:
        parts = line.strip().split(':')
        if len(parts) >= 3:
            username = parts[0]
            pwd_hash = parts[1]
            last_change = parts[2]

            if pwd_hash and pwd_hash not in ('*', '!', '!!', ''):
                hash_type = 'Unknown'
                if pwd_hash.startswith('$6$'): hash_type = 'SHA-512'
                elif pwd_hash.startswith('$5$'): hash_type = 'SHA-256'
                elif pwd_hash.startswith('$y$'): hash_type = 'yescrypt'
                elif pwd_hash.startswith('$1$'): hash_type = 'MD5 (WEAK)'
                print(f"  {username}: {hash_type} hash, last changed: day {last_change}")
PYEOF

# Analyze login history
last -f /cases/case-2024-001/linux/logs/wtmp > /cases/case-2024-001/linux/analysis/login_history.txt
lastb -f /cases/case-2024-001/linux/logs/btmp > /cases/case-2024-001/linux/analysis/failed_logins.txt 2>/dev/null

Step 3: Examine Persistence Mechanisms

bash
# Check cron jobs for all users
echo "=== CRON JOBS ===" > /cases/case-2024-001/linux/persistence/cron_analysis.txt

# System cron
for cronfile in /mnt/evidence/etc/crontab /mnt/evidence/etc/cron.d/*; do
    echo "--- $cronfile ---" >> /cases/case-2024-001/linux/persistence/cron_analysis.txt
    cat "$cronfile" 2>/dev/null >> /cases/case-2024-001/linux/persistence/cron_analysis.txt
    echo "" >> /cases/case-2024-001/linux/persistence/cron_analysis.txt
done

# User cron tabs
for cronfile in /mnt/evidence/var/spool/cron/crontabs/*; do
    echo "--- User crontab: $(basename $cronfile) ---" >> /cases/case-2024-001/linux/persistence/cron_analysis.txt
    cat "$cronfile" 2>/dev/null >> /cases/case-2024-001/linux/persistence/cron_analysis.txt
    echo "" >> /cases/case-2024-001/linux/persistence/cron_analysis.txt
done

# Check systemd services for persistence
echo "=== SYSTEMD SERVICES ===" > /cases/case-2024-001/linux/persistence/systemd_analysis.txt
find /mnt/evidence/etc/systemd/system/ -name "*.service" -newer /mnt/evidence/etc/os-release \
   >> /cases/case-2024-001/linux/persistence/systemd_analysis.txt

for svc in /mnt/evidence/etc/systemd/system/*.service; do
    echo "--- $(basename $svc) ---" >> /cases/case-2024-001/linux/persistence/systemd_analysis.txt
    cat "$svc" >> /cases/case-2024-001/linux/persistence/systemd_analysis.txt
    echo "" >> /cases/case-2024-001/linux/persistence/systemd_analysis.txt
done

# Check authorized SSH keys (backdoor detection)
echo "=== SSH AUTHORIZED KEYS ===" > /cases/case-2024-001/linux/persistence/ssh_keys.txt
find /mnt/evidence/home/ /mnt/evidence/root/ -name "authorized_keys" -exec sh -c \
   'echo "--- {} ---"; cat {}; echo ""' \; >> /cases/case-2024-001/linux/persistence/ssh_keys.txt

# Check rc.local and init scripts
cat /mnt/evidence/etc/rc.local 2>/dev/null > /cases/case-2024-001/linux/persistence/rc_local.txt

# Check /etc/profile.d/ for login-triggered scripts
ls -la /mnt/evidence/etc/profile.d/ > /cases/case-2024-001/linux/persistence/profile_scripts.txt

# Check for LD_PRELOAD hijacking
grep -r "LD_PRELOAD" /mnt/evidence/etc/ 2>/dev/null > /cases/case-2024-001/linux/persistence/ld_preload.txt
cat /mnt/evidence/etc/ld.so.preload 2>/dev/null >> /cases/case-2024-001/linux/persistence/ld_preload.txt

Step 4: Analyze Shell History and Command Execution

bash
# Analyze bash history for each user
python3 << 'PYEOF'
import os, glob

print("=== SHELL HISTORY ANALYSIS ===\n")

suspicious_commands = [
    'wget', 'curl', 'nc ', 'ncat', 'netcat', 'python -c', 'python3 -c',
    'perl -e', 'base64', 'chmod 777', 'chmod +s', '/dev/tcp', '/dev/udp',
    'nmap', 'masscan', 'hydra', 'john', 'hashcat', 'passwd', 'useradd',
    'iptables -F', 'ufw disable', 'history -c', 'rm -rf /', 'dd if=',
    'crontab', 'at ', 'systemctl enable', 'ssh-keygen', 'scp ', 'rsync',
    'tar czf', 'zip -r', 'openssl enc', 'gpg --encrypt', 'shred',
    'chattr', 'setfacl', 'awk', '/tmp/', '/dev/shm/'
]

for hist_file in glob.glob('/cases/case-2024-001/linux/users/*/.bash_history'):
    username = hist_file.split('/')[-2]
    print(f"User: {username}")

    with open(hist_file, 'r', errors='ignore') as f:
        lines = f.readlines()

    print(f"  Total commands: {len(lines)}")
    flagged = []
    for i, line in enumerate(lines):
        line = line.strip()
        for cmd in suspicious_commands:
            if cmd in line.lower():
                flagged.append((i+1, line))
                break

    if flagged:
        print(f"  Suspicious commands: {len(flagged)}")
        for lineno, cmd in flagged:
            print(f"    Line {lineno}: {cmd[:120]}")
    print()
PYEOF

Step 5: Check for Rootkits and Modified Binaries

bash
# Check for known rootkit indicators
# Compare system binary hashes against known-good
find /mnt/evidence/usr/bin/ /mnt/evidence/usr/sbin/ /mnt/evidence/bin/ /mnt/evidence/sbin/ \
   -type f -executable -exec sha256sum {} \; > /cases/case-2024-001/linux/analysis/binary_hashes.txt

# Check for SUID/SGID binaries (potential privilege escalation)
find /mnt/evidence/ -perm -4000 -type f 2>/dev/null > /cases/case-2024-001/linux/analysis/suid_files.txt
find /mnt/evidence/ -perm -2000 -type f 2>/dev/null > /cases/case-2024-001/linux/analysis/sgid_files.txt

# Check for suspicious files in /tmp and /dev/shm
find /mnt/evidence/tmp/ /mnt/evidence/dev/shm/ -type f 2>/dev/null \
   -exec file {} \; > /cases/case-2024-001/linux/analysis/tmp_files.txt

# Check for hidden files and directories
find /mnt/evidence/ -name ".*" -not -path "*/\." -type f 2>/dev/null | \
   head -100 > /cases/case-2024-001/linux/analysis/hidden_files.txt

# Check kernel modules
ls -la /mnt/evidence/lib/modules/$(ls /mnt/evidence/lib/modules/ | head -1)/extra/ 2>/dev/null \
   > /cases/case-2024-001/linux/analysis/extra_modules.txt

# Check for modified PAM configuration (authentication backdoors)
diff /mnt/evidence/etc/pam.d/ /cases/baseline/pam.d/ 2>/dev/null \
   > /cases/case-2024-001/linux/analysis/pam_changes.txt

Key Concepts

ConceptDescription
/var/log/auth.logPrimary authentication log on Debian/Ubuntu systems
/var/log/securePrimary authentication log on RHEL/CentOS systems
wtmp/btmpBinary logs recording successful and failed login sessions
.bash_historyUser command history file (can be cleared by attackers)
crontabScheduled task system commonly used for persistence
authorized_keysSSH public keys granting passwordless access to an account
SUID bitFile permission allowing execution as the file owner (privilege escalation vector)
LD_PRELOADEnvironment variable that loads a shared library before all others (hooking technique)

Tools & Systems

ToolPurpose
chkrootkitRootkit detection scanner for Linux systems
rkhunterRootkit Hunter - checks for rootkits, backdoors, and local exploits
AIDEAdvanced Intrusion Detection Environment - file integrity monitor
auditdLinux audit framework for system call and file access monitoring
last/lastbParse wtmp/btmp for login and failed login history
Plaso/log2timelineSuper-timeline creation including Linux artifacts
osquerySQL-based system querying for live forensic investigation
VelociraptorEndpoint agent with Linux artifact collection capabilities

Common Scenarios

Scenario 1: SSH Brute Force Followed by Compromise Analyze auth.log for failed SSH attempts followed by success, identify the attacking IP, check .bash_history for post-compromise commands, examine authorized_keys for added backdoor keys, check crontab for persistence, review network connections.

Scenario 2: Web Server Compromise via Application Vulnerability Examine web server access and error logs for exploitation attempts, check /tmp and /dev/shm for webshells, analyze the web server user's activity (www-data), check for privilege escalation via SUID binaries or kernel exploits, review outbound connections.

Scenario 3: Insider Threat on Database Server Analyze the suspect user's bash_history for database dump commands, check for large tar/zip files in home directory or /tmp, examine scp/rsync commands for data transfer, review cron jobs for automated exfiltration, check USB device logs.

Scenario 4: Crypto-Miner on Cloud Instance Check for high-CPU processes in /proc (live) or systemd service files, examine crontab entries for miner restart scripts, check /tmp for mining binaries, analyze network connections for mining pool communications, review authorized_keys for attacker access.

Output Format

Linux Forensics Summary:
  System: webserver01 (Ubuntu 22.04 LTS)
  Hostname: webserver01.corp.local
  Kernel: 5.15.0-91-generic

  User Accounts:
    Total: 25 (3 with UID 0 - 1 ANOMALOUS)
    Interactive shells: 8 users
    Recently created: admin2 (created 2024-01-15)

  Authentication Events:
    Successful SSH logins: 456
    Failed SSH attempts: 12,345 (from 23 unique IPs)
    Sudo executions: 89

  Persistence Mechanisms Found:
    Cron jobs: 3 suspicious (reverse shell, miner restart)
    Systemd services: 1 unknown (update-checker.service)
    SSH keys: 2 unauthorized keys in root authorized_keys
    rc.local: Modified with download cradle

  Suspicious Activity:
    - bash_history contains wget to pastebin URL
    - SUID binary /tmp/.hidden/escalate found
    - /dev/shm/ contains compiled ELF binary
    - LD_PRELOAD in /etc/ld.so.preload pointing to /lib/.hidden.so

  Report: /cases/case-2024-001/linux/analysis/

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Analyzing Linux System Artifacts AI skill do?

Examine Linux system artifacts (auth logs, cron/systemd persistence, shell history, SSH keys, and system configuration) to uncover evidence of compromise, detect rootkits or backdoors, and reconstruct user/attacker activity. Use when investigating a compromised Linux server or workstation, hunting for persistence mechanisms, or scoping a Linux-based breach during incident response.

Why use Analyzing Linux System Artifacts on TypingMind?

Because you install it once and use it with any model. Analyzing Linux System Artifacts is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Analyzing Linux System Artifacts in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-linux-system-artifacts. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Analyzing Linux System Artifacts?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Analyzing Linux System Artifacts?

As many as you like. As long as a model supports skills, you can use Analyzing Linux System Artifacts with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Analyzing Linux System Artifacts AI skill free?

Yes. It is published on GitHub by mukul975 under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇