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Prompts Linux Persistence Mechanisms Analyzer

security analyst security skill risk: medium

Linux Persistence Mechanisms Analyzer

The prompt outlines steps to scan crontab entries, audit systemd units, detect LD_PRELOAD hijacking, inspect shell profiles and SSH keys, correlate auditd logs, and generate a risk…

  • Policy sensitive
  • Human review
  • External action: medium

SKILL 4 files · 2 folders

SKILL.md
---
name: analyzing-persistence-mechanisms-in-linux
description: "Detect and analyze Linux persistence mechanisms including crontab entries, systemd service units, LD_PRELOAD"
---
# Analyzing Persistence Mechanisms in Linux

## Overview

Adversaries establish persistence on Linux systems through crontab jobs, systemd service/timer units, LD_PRELOAD library injection, shell profile modifications (.bashrc, .profile), SSH authorized_keys backdoors, and init script manipulation. This skill scans for all known persistence vectors, checks file timestamps and integrity, and correlates findings with auditd logs to build a timeline of persistence installation.


## When to Use

- When investigating security incidents that require analyzing persistence mechanisms in linux
- 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

- Root or sudo access on target Linux system (or forensic image)
- auditd configured with file watch rules on persistence paths
- Python 3.8+ with standard library (os, subprocess, json)
- Optional: OSSEC/Wazuh agent for file integrity monitoring alerts

## Steps

1. **Scan Crontab Entries** — Enumerate all user crontabs, /etc/cron.d/, /etc/cron.daily/, and anacron jobs for suspicious commands
2. **Audit Systemd Units** — Check /etc/systemd/system/ and ~/.config/systemd/user/ for non-package-managed service and timer units
3. **Detect LD_PRELOAD Hijacking** — Check /etc/ld.so.preload and LD_PRELOAD environment variable for injected shared libraries
4. **Inspect Shell Profiles** — Scan .bashrc, .bash_profile, .profile, /etc/profile.d/ for injected commands or reverse shells
5. **Check SSH Authorized Keys** — Audit all authorized_keys files for unauthorized public keys with command restrictions
6. **Correlate Auditd Logs** — Search auditd logs for file modification events on persistence paths to build an installation timeline
7. **Generate Persistence Report** — Produce a risk-scored report of all discovered persistence mechanisms

## Expected Output

- JSON report of all persistence mechanisms found with risk scores
- Timeline of persistence installation from auditd correlation
- MITRE ATT&CK technique mapping (T1053, T1543, T1574, T1546)
- Remediation commands for each detected persistence mechanism

REQUIRED CONTEXT

  • root or sudo access on target Linux system or forensic image
  • auditd logs with file watch rules

OPTIONAL CONTEXT

  • OSSEC/Wazuh alerts
  • Python 3.8+ environment

EXPECTED OUTPUT

Format
json
Schema
json_report · persistence mechanisms with risk scores, installation timeline, MITRE ATT&CK technique mapping, remediation commands
Constraints
  • include risk scores
  • include installation timeline from auditd
  • include MITRE ATT&CK mappings
  • include remediation commands

SUCCESS CRITERIA

  • Scan for all known persistence vectors
  • Check file timestamps and integrity
  • Correlate findings with auditd logs
  • Produce risk-scored report

CAVEATS

Dependencies
  • Root or sudo access on target Linux system (or forensic image)
  • auditd configured with file watch rules on persistence paths
  • Python 3.8+ with standard library (os, subprocess, json)
  • Optional: OSSEC/Wazuh agent for file integrity monitoring alerts

QUALITY

OVERALL
0.78
CLARITY
0.90
SPECIFICITY
0.85
REUSABILITY
0.60
COMPLETENESS
0.80

IMPROVEMENT SUGGESTIONS

  • Add explicit placeholders (e.g., {{target_host}}, {{audit_log_path}}) to make the prompt reusable as a template.

USAGE

Copy the prompt above and paste it into your AI of choice — Claude, ChatGPT, Gemini, or anywhere else you're working. Replace any placeholder sections with your own context, then ask for the output.

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