/analyzing-linux-elf-malware

name: analyzing-linux-elf-malware
description: 'Analyzes malicious Linux ELF (Executable and Linkable Format) binaries including botnets, cryptominers, ransomware,
  and rootkits targeting Linux servers, containers, and cloud infrastructure. Covers static analysis, dynamic tracing, and
  reverse engineering of x86_64 and ARM ELF samples. Activates for requests involving Linux malware analysis, ELF binary investigation,
  Linux server compromise assessment, or container malware analysis.

  '
domain: cybersecurity
subdomain: malware-analysis
tags:
- malware
- Linux
- ELF
- reverse-engineering
- server-malware
version: 1.0.0
author: mahipal
license: Apache-2.0
nist_csf:
- DE.AE-02
- RS.AN-03
- ID.RA-01
- DE.CM-01

Analyzing Linux ELF Malware

When to Use

• A Linux server or container has been compromised and suspicious ELF binaries are found
• Analyzing Linux botnets (Mirai, Gafgyt, XorDDoS), cryptominers, or ransomware
• Investigating malware targeting cloud infrastructure, Docker containers, or Kubernetes pods
• Reverse engineering Linux rootkits and kernel modules
• Analyzing cross-platform malware compiled for Linux x86_64, ARM, or MIPS architectures

**Do not use** for Windows PE binary analysis; use PEStudio, Ghidra, or IDA for Windows malware.

Prerequisites

• Ghidra or IDA with Linux ELF support for disassembly and decompilation
• Linux analysis VM (Ubuntu 22.04 recommended) with development tools installed
• strace, ltrace, and GDB for dynamic analysis and debugging
• readelf, objdump, and nm from GNU binutils for static inspection
• Radare2 for quick binary triage and scripted analysis
• Docker for isolated container-based malware execution

Workflow

Step 1: Identify ELF Binary Properties

Examine the ELF header and basic properties:

# File type identification
file suspect_binary

# Detailed ELF header analysis
readelf -h suspect_binary

# Section headers
readelf -S suspect_binary

# Program headers (segments)
readelf -l suspect_binary

# Symbol table (if not stripped)
readelf -s suspect_binary
nm suspect_binary 2>/dev/null

# Dynamic linking information
readelf -d suspect_binary
ldd suspect_binary 2>/dev/null  # Only on matching architecture!

# Compute hashes
md5sum suspect_binary
sha256sum suspect_binary

# Check for packing/UPX
upx -t suspect_binary

# Python-based ELF analysis
from elftools.elf.elffile import ELFFile
import hashlib

with open("suspect_binary", "rb") as f:
    data = f.read()
    sha256 = hashlib.sha256(data).hexdigest()

with open("suspect_binary", "rb") as f:
    elf = ELFFile(f)

    print(f"SHA-256:      {sha256}")
    print(f"Class:        {elf.elfclass}-bit")
    print(f"Endian:       {elf.little_endian and 'Little' or 'Big'}")
    print(f"Machine:      {elf.header.e_machine}")
    print(f"Type:         {elf.header.e_type}")
    print(f"Entry Point:  0x{elf.header.e_entry:X}")

    # Check if stripped
    symtab = elf.get_section_by_name('.symtab')
    print(f"Stripped:     {'Yes' if symtab is None else 'No'}")

    # Section entropy analysis
    import math
    from collections import Counter
    for section in elf.iter_sections():
        data = section.data()
        if len(data) > 0:
            entropy = -sum((c/len(data)) * math.log2(c/len(data))
                          for c in Counter(data).values() if c > 0)
            if entropy > 7.0:
                print(f"  [!] High entropy section: {section.name} ({entropy:.2f})")

Step 2: Extract Strings and Indicators

Search for embedded IOCs and functionality clues:

# ASCII strings
strings suspect_binary > strings_output.txt

# Search for network indicators
grep -iE "(http|https|ftp)://" strings_output.txt
grep -iE "([0-9]{1,3}\.){3}[0-9]{1,3}" strings_output.txt
grep -iE "[a-zA-Z0-9.-]+\.(com|net|org|io|ru|cn)" strings_output.txt

# Search for shell commands
grep -iE "(bash|sh|wget|curl|chmod|/tmp/|/dev/)" strings_output.txt

# Search for crypto mining indicators
grep -iE "(stratum|xmr|monero|pool\.|mining)" strings_output.txt

# Search for SSH/credential theft
grep -iE "(ssh|authorized_keys|id_rsa|shadow|passwd)" strings_output.txt

# Search for persistence mechanisms
grep -iE "(crontab|systemd|init\.d|rc\.local|ld\.so\.preload)" strings_output.txt

# FLOSS for obfuscated strings (if available)
floss suspect_binary

Step 3: Analyze System Calls and Library Usage

Identify what system calls and libraries the malware uses:

# List imported functions (dynamically linked)
readelf -r suspect_binary | grep -E "socket|connect|exec|fork|open|write|bind|listen"

# Trace system calls during execution (in isolated VM only)
strace -f -e trace=network,process,file -o strace_output.txt ./suspect_binary

# Trace library calls
ltrace -f -o ltrace_output.txt ./suspect_binary

# Key system calls to watch:
# Network: socket, connect, bind, listen, accept, sendto, recvfrom
# Process: fork, execve, clone, kill, ptrace
# File:    open, read, write, unlink, rename, chmod
# Persistence: inotify_add_watch (file monitoring)

Step 4: Dynamic Analysis with GDB

Debug the malware to observe runtime behavior:

# Start GDB with the binary
gdb ./suspect_binary

# Set breakpoints on key functions
(gdb) break main
(gdb) break socket
(gdb) break connect
(gdb) break execve
(gdb) break fork

# Run and analyze
(gdb) run
(gdb) info registers    # View register state
(gdb) x/20s $rdi        # Examine string argument
(gdb) bt                # Backtrace
(gdb) continue

# For stripped binaries, break on entry point
(gdb) break *0x400580   # Entry point from readelf
(gdb) run

# Monitor network connections during execution
# In another terminal:
ss -tlnp  # List listening sockets
ss -tnp   # List established connections

Step 5: Reverse Engineer with Ghidra

Perform deep code analysis on the ELF binary:

Ghidra Analysis for Linux ELF:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
1. Import: File -> Import -> Select ELF binary
   - Ghidra auto-dete