# Analyzing Golang Malware With Ghidra

> Use when reverse engineer Go-compiled malware using Ghidra with specialized scripts for function recovery, string extraction, and type reconstruction in stripped Go binaries. Use when reverseing engineer go-compiled malware using ghidra with specialized scripts for.

- Skill: `oyi77/analyzing-golang-malware-with-ghidra` (Agent Skill)
- Install (CLI): `npx skillmds add oyi77/analyzing-golang-malware-with-ghidra`
- Raw SKILL.md: https://api.skillmd.com/api/skills/oyi77/analyzing-golang-malware-with-ghidra/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: Apache-2.0
- Author: oyi77 (https://skillmd.com/u/oyi77)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/oyi77/analyzing-golang-malware-with-ghidra

---


# Analyzing Golang Malware with Ghidra

## Overview

Go (Golang) has become a popular language for malware authors due to its cross-compilation capabilities, static linking that produces self-contained binaries, and the complexity it introduces for reverse engineering. Go binaries contain the entire runtime, standard library, and all dependencies statically linked, resulting in large binaries (often 5-15MB) with thousands of functions. Ghidra struggles with Go-specific string formats (non-null-terminated), stripped function names, and goroutine concurrency patterns. Specialized tools like GoResolver (Volexity, 2025) use control-flow graph similarity to automatically deobfuscate and recover function names in stripped or obfuscated Go binaries.


## When to Use
**Trigger phrases:**
- "analyzing golang malware with ghidra"
- "Reverse engineer Go-compiled malware using Ghidra with specialized scripts for f"


- When investigating security incidents that require analyzing golang malware with ghidra
- 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

- Ghidra 11.0+ with JDK 17+
- GoResolver plugin (for function name recovery)
- Go Reverse Engineering Tool Kit (go-re.tk)
- Python 3.9+ for helper scripts
- Understanding of Go runtime internals (goroutines, channels, interfaces)
- Familiarity with Go binary structure (pclntab, moduledata, itab)

## Key Concepts

This section covers key concepts for analyzing golang malware with ghidra.

- Ensure all prerequisites are met before proceeding
- Follow the documented workflow steps in sequence
- Record results and any anomalies encountered during this phase
### Go Binary Structure

Go binaries embed rich metadata in the `pclntab` (PC Line Table) structure, which maps program counters to function names, source files, and line numbers. Even stripped binaries retain this metadata. The `moduledata` structure contains pointers to type information, itabs (interface tables), and the pclntab itself. Go strings are stored as a pointer-length pair rather than null-terminated C strings.

### Function Recovery in Stripped Binaries

Despite stripping symbol tables, Go binaries retain function names within the pclntab. However, obfuscation tools like garble rename functions to random strings. GoResolver addresses this by computing control-flow graph signatures of obfuscated functions and matching them against a database of known Go standard library and third-party package functions.

### Crate/Dependency Extraction

Go's dependency management embeds module paths and version strings in the binary. Extracting these reveals the malware's third-party dependencies (HTTP libraries, encryption packages, C2 frameworks), which provides insight into capabilities without full reverse engineering.

## Workflow

1. **Isolate the sample** — ensure the malware is in a sandboxed environment with no network access
2. **Record file metadata** — hash the sample and note file type, size, and compile timestamp
3. **Static analysis** — examine strings, imports, and disassembled code without execution
4. **Dynamic analysis** — execute in a monitored sandbox and record behavior (file, registry, network)
5. **Document IOCs** — extract indicators of compromise and write the analysis report
### Step 1: Initial Binary Analysis

```python
#!/usr/bin/env python3
"""Analyze Go binary metadata for malware analysis."""
import struct
import sys
import re


def find_go_build_info(data):
    """Extract Go build information from binary."""
    # Go buildinfo magic: \xff Go buildinf:
    magic = b'\xff Go buildinf:'
    offset = data.find(magic)
    if offset == -1:
        return None

    print(f"[+] Go build info at offset 0x{offset:x}")

    # Extract Go version string nearby
    go_version = re.search(rb'go\d+\.\d+(?:\.\d+)?', data[offset:offset+256])
    if go_version:
        print(f"  Go Version: {go_version.group().decode()}")

    return offset


def find_pclntab(data):
    """Locate the pclntab (PC Line Table) structure."""
    # pclntab magic bytes vary by Go version
    magics = {
        b'\xfb\xff\xff\xff\x00\x00': "Go 1.2-1.15",
        b'\xfa\xff\xff\xff\x00\x00': "Go 1.16-1.17",
        b'\xf1\xff\xff\xff\x00\x00': "Go 1.18-1.19",
        b'\xf0\xff\xff\xff\x00\x00': "Go 1.20+",
    }

    for magic, version in magics.items():
        offset = data.find(magic)
        if offset != -1:
            print(f"[+] pclntab found at 0x{offset:x} ({version})")
            return offset, version

    return None, None


def extract_function_names(data, pclntab_offset):
    """Extract function names from pclntab."""
    if pclntab_offset is None:
        return []

    functions = []
    # Function name strings follow specific patterns
    func_pattern = re.compile(
        rb'(?:main|runtime|fmt|net|os|crypto|encoding|io|sync|'
        rb'syscall|reflect|strings|bytes|path|time|math|sort|'
        rb'github\.com|golang\.org)[/\.][\w/.]+',
    )

    for match in func_pattern.finditer(data):
        name = match.group().decode('utf-8', errors='replace')
        if len(name) > 4 and len(name) < 200:
            functions.append(name)

    return sorted(set(functions))


def extract_go_strings(data):
    """Extract Go-style strings (pointer+length pairs)."""
    # Go strings are not null-terminated; extract readable sequences
    strings = []
    ascii_pattern = re.compile(rb'[\x20-\x7e]{10,}')

    for match in ascii_pattern.finditer(data):
        s = match.group().decode('ascii')
        # Filter for interesting malware strings
        interesting = [
            'http', 'https', 'tcp', 'udp', 'dns',
            'cmd', 'shell', 'exec', 'upload', 'download',
            'encrypt', 'decrypt', 'key', 'token', 'password',
            'c2', 'beacon', 'agent', 'implant', 'bot',
            'mutex', 'persist', 'registry', 'scheduled',
        ]
        if any(kw in s.lower() for kw in interesting):
            strings.append(s)

    return strings


def extract_dependencies(data):
    """Extract Go module dependencies from binary."""
    deps = []
    # Module paths follow pattern: github.com/user/repo
    dep_pattern = re.compile(
        rb'((?:github\.com|gitlab\.com|golang\.org|gopkg\.in|'
        rb'go\.etcd\.io|google\.golang\.org)/[^\x00\s]{5,80})'
    )

    for match in dep_pattern.finditer(data):
        dep = match.group().decode('utf-8', errors='replace')
        deps.append(dep)

    unique_deps = sorted(set(deps))
    return unique_deps


def analyze_go_binary(filepath):
    """Full analysis of Go malware binary."""
    with open(filepath, 'rb') as f:
        data = f.read()

    print(f"[+] Analyzing Go binary: {filepath}")
    print(f"  File size: {len(data):,} bytes")
    print("=" * 60)

    # Build info
    find_go_build_info(data)

    # pclntab
    pclntab_offset, go_version = find_pclntab(data)

    # Functions
    functions = extract_function_names(data, pclntab_offset)
    print(f"\n[+] Recovered {len(functions)} function names")

    # Categorize functions
    categories = {
        "network": [], "crypto": [], "os_exec": [],
        "file_io": [], "main": [], "third_party": [],
    }
    for f in functions:
        if 'net/' in f or 'http' in f.lower():
            categories["network"].append(f)
        elif 'crypto' in f:
            categories["crypto"].append(f)
        elif 'os/exec' in f or 'syscall' in f:
            categories["os_exec"].append(f)
        elif 'os.' in f or 'io/' in f:
            categories["file_io"].append(f)
        elif f.startswith('main.'):
            categories["main"].append(f)
        elif 'github.com' in f or 'golang.org' in f:
            categories["third_party"].append(f)

    for cat, funcs in categories.items():
        if funcs:
            print(f"\n  [{cat}] ({len(funcs)} functions):")
            for fn in funcs[:10]:
                print(f"    {fn}")

    # Dependencies
    deps = extract_dependencies(data)
    print(f"\n[+] Dependencies ({len(deps)}):")
    for dep in deps[:20]:
        print(f"    {dep}")

    # Suspicious strings
    sus_strings = extract_go_strings(data)
    print(f"\n[+] Suspicious strings ({len(sus_strings)}):")
    for s in sus_strings[:20]:
        print(f"    {s}")


if __name__ == "__main__":
    if len(sys.argv) < 2:
        print(f"Usage: {sys.argv[0]} <go_binary>")
        sys.exit(1)
    analyze_go_binary(sys.argv[1])
```

### Step 2: Ghidra Analysis Script

```python
# Ghidra script (run within Ghidra's script manager)
# Save as AnalyzeGoBinary.py in Ghidra scripts directory

# @category MalwareAnalysis
# @description Analyze Go binary structure and recover metadata

def analyze_go_binary_ghidra():
    """Ghidra script for Go binary analysis."""
    from ghidra.program.model.mem import MemoryAccessException

    program = getCurrentProgram()
    memory = program.getMemory()
    listing = program.getListing()

    print("[+] Go Binary Analysis Script")
    print(f"  Program: {program.getName()}")

    # Find pclntab
    pclntab_magics = [
        bytes([0xf0, 0xff, 0xff, 0xff]),  # Go 1.20+
        bytes([0xf1, 0xff, 0xff, 0xff]),  # Go 1.18-1.19
        bytes([0xfa, 0xff, 0xff, 0xff]),  # Go 1.16-1.17
        bytes([0xfb, 0xff, 0xff, 0xff]),  # Go 1.2-1.15
    ]

    for magic in pclntab_magics:
        addr = memory.findBytes(
            program.getMinAddress(), magic, None, True, None
        )
        if addr:
            print(f"[+] pclntab found at {addr}")
            # Create label
            program.getSymbolTable().createLabel(
                addr, "go_pclntab", None,
                ghidra.program.model.symbol.SourceType.ANALYSIS
            )
            break

    # Fix Go string definitions
    # Go strings are ptr+len, not null terminated
    print("[+] Fixing Go string references...")

    # Search for function names containing package paths
    symbol_table = program.getSymbolTable()
    func_count = 0
    for symbol in symbol_table.getAllSymbols(True):
        name = symbol.getName()
        if ('.' in name and
            any(pkg in name for pkg in
                ['main.', 'runtime.', 'net.', 'crypto.', 'os.'])):
            func_count += 1

    print(f"[+] Found {func_count} Go function symbols")


# Execute
analyze_go_binary_ghidra()
```

## Validation Criteria

- Go version and build information extracted from binary
- pclntab located and parsed for function name recovery
- Third-party dependencies identified revealing malware capabilities
- Main package functions enumerated for targeted analysis
- Network, crypto, and OS exec functions categorized
- Ghidra analysis correctly labels Go runtime structures

## When NOT to Use

- You need to perform the attack, not analyze it (use performing-* skills)
- Task is about detection, not analysis (use detecting-* skills)
- You need to implement controls (use implementing-* skills)
- Task is about threat hunting, not post-incident analysis (use hunting-* skills)
- You don't have access to the artifacts/logs to analyze
- Task requires real-time monitoring (use SOC tools)
- Non-malware Go binaries, or triage without a Ghidra Go workflow — use
  `go-rust-reverse`; this skill is malware-sample + Ghidra-specific.


## Red Flags

- Performing actions without explicit written authorization from the asset owner
- Testing against production systems without a defined scope and rules of engagement
- Analyzing malware on a machine connected to the production network
- Failing to isolate the analysis environment from the internet
- Executing samples without proper containment (VM, sandbox)

## Verification

- All steps executed successfully against a test environment before production use
- Output documented with screenshots or logs demonstrating expected behavior
- Sample hash recorded and verified (MD5, SHA-1, SHA-256)
- Analysis environment confirmed isolated from production network
- Indicators of compromise (IOCs) extracted and documented

## References

- [CUJO AI - Reverse Engineering Go Binaries with Ghidra](https://cujo.com/blog/reverse-engineering-go-binaries-with-ghidra/)
- [Volexity GoResolver](https://www.volexity.com/blog/2025/04/01/goresolver-using-control-flow-graph-similarity-to-deobfuscate-golang-binaries-automatically/)
- [Go Reverse Engineering Tool Kit](https://go-re.tk/about/)
- [SentinelOne AlphaGolang](https://www.sentinelone.com/labs/alphagolang-a-step-by-step-go-malware-reversing-methodology-for-ida-pro/)
- [Go Binary Reversing Notes](https://gist.github.com/0xdevalias/4e430914124c3fd2c51cb7ac2801acba)

## Process

1. Analyze the task requirements
2. Apply domain expertise
3. Verify output quality

## Anti-Rationalization Table

| Rationalization | Reality |
|---|---|
| "We are too small to be targeted" | Automated attacks target everyone. Size does not matter. |
| "Security slows us down" | A breach slows you down 100x more. Build security in from the start. |
| "We will fix it after launch" | Vulnerabilities in production are exploited within hours. Fix before deploy. |