analyzing-golang-malware-with-ghidra

name	analyzing-golang-malware-with-ghidra
description	Reverse engineer Go-compiled malware using Ghidra with specialized scripts for function recovery, string extraction, and type reconstruction in stripped Go binaries.
domain	cybersecurity
subdomain	malware-analysis
tags	["golang","ghidra","reverse-engineering","malware-analysis","binary-analysis","go-malware","disassembly"]
version	1.0
author	mahipal
license	Apache-2.0
nist_csf	["DE.AE-02","RS.AN-03","ID.RA-01","DE.CM-01"]
mitre_attack	["T1027","T1620","T1140","T1059"]
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.

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

• 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

 struct
 sys
 re


 ():
    
    
    magic = 
    offset = data.find(magic)
     offset == -:
         

    ()

    
    go_version = re.search(, data[offset:offset+])
     go_version:
        ()

     offset


 ():
    
    
    magics = {
        : ,
        : ,
        : ,
        : ,
    }

     magic, version  magics.items():
        offset = data.find(magic)
         offset != -:
            ()
             offset, version

     , 


 ():
    
     pclntab_offset  :
         []

    functions = []
    
    func_pattern = re.(
        
        
        ,
    )

       func_pattern.finditer(data):
        name = .group().decode(, errors=)
         (name) >   (name) < :
            functions.append(name)

     ((functions))


 ():
    
    
    strings = []
    ascii_pattern = re.()

       ascii_pattern.finditer(data):
        s = .group().decode()
        
        interesting = [
            , , , , ,
            , , , , ,
            , , , , ,
            , , , , ,
            , , , ,
        ]
         (kw  s.lower()  kw  interesting):
            strings.append(s)

     strings


 ():
    
    deps = []
    
    dep_pattern = re.(
        
        
    )

       dep_pattern.finditer(data):
        dep = .group().decode(, errors=)
        deps.append(dep)

    unique_deps = ((deps))
     unique_deps


 ():
    
     (filepath, )  f:
        data = f.read()

    ()
    ()
    ( * )

    
    find_go_build_info(data)

    
    pclntab_offset, go_version = find_pclntab(data)

    
    functions = extract_function_names(data, pclntab_offset)
    ()

    
    categories = {
        : [], : [], : [],
        : [], : [], : [],
    }
     f  functions:
           f    f.lower():
            categories[].append(f)
           f:
            categories[].append(f)
           f    f:
            categories[].append(f)
           f    f:
            categories[].append(f)
         f.startswith():
            categories[].append(f)
           f    f:
            categories[].append(f)

     cat, funcs  categories.items():
         funcs:
            ()
             fn  funcs[:]:
                ()

    
    deps = extract_dependencies(data)
    ()
     dep  deps[:]:
        ()

    
    sus_strings = extract_go_strings(data)
    ()
     s  sus_strings[:]:
        ()


 __name__ == :
     (sys.argv) < :
        ()
        sys.exit()
    analyze_go_binary(sys.argv[])