analyzing-linux-elf-malware

# 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:

```bash
# 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
# 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:

```bash
# 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:

```bash
# 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:

```bash
# 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-detects ELF format and architecture
   - Accept default analysis options

2. Key analysis targets:
   - main() function (or entry point if stripped)
   - Socket creation and connection functions
   - Command dispatch logic (switch/case on received data)
   - Encryption/encoding routines
   - Persistence installation code
   - Self-propagation/scanning functions

3. For Mirai-like botnets, look for:
   - Credential list for brute-forcing (telnet/SSH)
   - Attack module selection (UDP flood, SYN flood, ACK flood)
   - Scanner module (port scanning for vulnerable devices)
   - Killer module (killing competing botnets)

4. For cryptominers, look for:
   - Mining pool connection (stratum protocol)
   - Wallet address strings