detecting-rootkit-activity

name	detecting-rootkit-activity
description	Detects rootkit presence on compromised systems by identifying hidden processes, hooked system calls, modified kernel structures, hidden files, and covert network connections using memory forensics, cross-view detection, and integrity checking techniques. Activates for requests involving rootkit detection, hidden process discovery, kernel integrity checking, or system call hook analysis.
domain	cybersecurity
subdomain	malware-analysis
tags	["malware","rootkit","detection","kernel-analysis","memory-forensics"]
version	1.0.0
author	mahipal
license	Apache-2.0
nist_csf	["DE.AE-02","RS.AN-03","ID.RA-01","DE.CM-01"]
mitre_attack	["T1014","T1547.006","T1564.001","T1574.006"]
source	https://github.com/mukul975/Anthropic-Cybersecurity-Skills
source_commit	04450304b12645cb2b974ab96d28c0664758a88d
note	Vendored verbatim from an external Apache-2.0 security-skill library, pinned by commit. Exceeds the internal 300-line skill guideline (agent-code-constraints.md) -- kept as-is because this is vendored reference material (forensics/threat-intel procedure), not Yana AI-authored content, and trimming would damage technical accuracy.

Detecting Rootkit Activity

When to Use

• System shows signs of compromise but standard tools (Task Manager, netstat) show nothing abnormal
• Antivirus/EDR detects rootkit signatures but cannot identify the specific hiding mechanism
• Memory forensics reveals discrepancies between kernel data structures and user-mode tool output
• Investigating a persistent threat that survives remediation attempts and system reboots
• Validating system integrity after a suspected kernel-level compromise

Do not use as a first-line detection method; start with standard malware triage and escalate to rootkit analysis when hiding behavior is suspected.

Prerequisites

• Volatility 3 for memory forensics and kernel structure analysis
• GMER or Rootkit Revealer (Windows) for live system scanning
• rkhunter and chkrootkit (Linux) for filesystem and process integrity checks
• Sysinternals tools (Process Explorer, Autoruns, RootkitRevealer) for Windows analysis
• Memory dump from the suspected system (WinPmem, LiME)
• Clean baseline of the OS for comparison (known-good kernel module hashes)

Workflow

Step 1: Cross-View Detection for Hidden Processes

Compare process lists from different data sources to find discrepancies:

# Volatility: Compare process enumeration methods
# pslist - walks ActiveProcessLinks (EPROCESS linked list - what rootkits manipulate)
vol3 -f memory.dmp windows.pslist > pslist_output.txt

# psscan - scans physical memory for EPROCESS pool tags (rootkit-resistant)
vol3 -f memory.dmp windows.psscan > psscan_output.txt

# Compare outputs to find hidden processes
python3 << 'PYEOF'
pslist_pids = set()
psscan_pids = set()

with open("pslist_output.txt") as f:
    for line in f:
        parts = line.split()
        if len(parts) > 1 and parts[1].isdigit():
            pslist_pids.add(int(parts[1]))

with open("psscan_output.txt") as f:
    for line in f:
        parts = line.split()
        if len(parts) > 1 and parts[1].isdigit():
            psscan_pids.add(int(parts[1]))

hidden = psscan_pids - pslist_pids
 hidden:
    (f)
     pid  hidden:
        (f)
:
    ()
PYEOF