
Analyzing Golang Malware
FreeReverse engineer Go malware using Ghidra effectively.
Free · Opens the source repo
What Analyzing Golang Malware does
Analyzing Golang Malware with Ghidra is a specialized skill designed for security analysts and researchers focused on reverse engineering Go-compiled malware. Go's unique characteristics, such as static linking and complex binary structures, make it a popular choice for malware authors. This skill provides a structured approach to dissecting Go binaries, leveraging Ghidra's capabilities alongside specific tools like GoResolver to recover function names and extract valuable metadata from binaries.
The skill offers detailed workflows and scripts that facilitate the analysis of Go binaries, including the extraction of Go build information and the identification of the pclntab structure, which retains function names even in stripped binaries. By utilizing GoResolver, users can deobfuscate function names that have been altered by obfuscation tools, enhancing the analyst's ability to understand malware behavior. The skill also assists in extracting embedded module and dependency strings, providing insights into third-party libraries used by the malware, which can inform threat detection and mitigation strategies.
This skill is particularly useful in scenarios involving security incident investigations, where understanding the tactics, techniques, and procedures (TTPs) of Go malware is critical. It supports SOC analysts in building detection rules and validating security monitoring coverage against Go-related threats. With prerequisites that include familiarity with Ghidra, Python, and Go binary structures, this skill is tailored for those with a foundational understanding of reverse engineering and malware analysis.
When to use it
Use this skill when analyzing Go malware samples in Ghidra, especially when dealing with obfuscated or stripped binaries.
When not to use it
This skill may not be suitable for analyzing malware written in languages other than Go or for users without a basic understanding of Go's runtime internals.
What you can build with it
Investigating a Security Incident
Use this skill when you need to analyze a Go malware sample as part of a security incident investigation.
Building Detection Rules
Implement this skill to develop detection rules and threat hunting queries focused on Go malware.
Validating Security Coverage
Utilize this skill to ensure your security monitoring tools effectively cover Go-related attack techniques.
How to install Analyzing Golang Malware
View source1. Install with the skills CLI
npx skills add mukul975/anthropic-cybersecurity-skills/analyzing-golang-malware-with-ghidra --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by mukul975Analyzing 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
- 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
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
Step 1: Initial Binary Analysis
#!/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
# 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
References
Frequently asked questions about Analyzing Golang Malware
Similar skills
Asset Criticality Scoring for Vulns
Prioritize vulnerabilities based on asset criticality.
Performing Alert Triage with Elastic SIEM
Streamline alert triage processes in Elastic Security.
Active Directory Vulnerability Assessment
Secure your Active Directory with comprehensive assessments.
Active Directory Investigation
Streamline your Active Directory compromise investigations.
Parsing Artifacts with Eric Zimmerman Tools
Efficiently parse Windows forensic artifacts for analysis.
Operationalizing MISP Threat Feeds
Enhance threat detection with curated MISP feeds.
