analyzing-supply-chain-malware-artifacts

name	analyzing-supply-chain-malware-artifacts
description	Investigate supply chain attack artifacts including trojanized software updates, compromised build pipelines, and sideloaded dependencies to identify intrusion vectors and scope of compromise.
domain	cybersecurity
subdomain	malware-analysis
tags	["supply-chain","malware-analysis","trojanized-software","solarwinds","3cx","dependency-confusion","software-integrity"]
version	1.0
author	mahipal
license	Apache-2.0

Analyzing Supply Chain Malware Artifacts

Overview

Supply chain attacks compromise legitimate software distribution channels to deliver malware through trusted update mechanisms. Notable examples include SolarWinds SUNBURST (2020, affecting 18,000+ customers), 3CX SmoothOperator (2023, a cascading supply chain attack originating from Trading Technologies), and numerous npm/PyPI package poisoning campaigns. Analysis involves comparing trojanized binaries against legitimate versions, identifying injected code in build artifacts, examining code signing anomalies, and tracing the infection chain from initial compromise through payload delivery. As of 2025, supply chain attacks account for 30% of all breaches, a 100% increase from prior years.

Prerequisites

• Python 3.9+ with pefile, ssdeep, hashlib
• Binary diff tools (BinDiff, Diaphora)
• Code signing verification tools (sigcheck, codesign)
• Software composition analysis (SCA) tools
• Access to legitimate software versions for comparison
• Package repository monitoring (npm, PyPI, NuGet)

Practical Steps

Step 1: Binary Comparison Analysis

#!/usr/bin/env python3
"""Compare trojanized binary against legitimate version."""
import hashlib
import pefile
import sys
import json


def compare_pe_files(legitimate_path, suspect_path):
    """Compare PE file structures between legitimate and suspect versions."""
    legit_pe = pefile.PE(legitimate_path)
    suspect_pe = pefile.PE(suspect_path)

    report = {"differences": [], "suspicious_sections": [], "import_changes": []}

    # Compare sections
    legit_sections = {s.Name.rstrip(b'\x00').decode(): {
        "size": s.SizeOfRawData,
        "entropy": s.get_entropy(),
        "characteristics": s.Characteristics,
    } for s in legit_pe.sections}

    suspect_sections = {s.Name.rstrip(b'\x00').decode(): {
        "size": s.SizeOfRawData,
        "entropy": s.get_entropy(),
        "characteristics": s.Characteristics,
    } for s in suspect_pe.sections}

    # Find new or modified sections
    for name, props in suspect_sections.items():
        if name not in legit_sections:
            report["suspicious_sections"].append({
                "name": name, "reason": "New section not in legitimate version",
                "size": props["size"], "entropy": round(props["entropy"], ),
            })
         (props[] - legit_sections[name][]) > :
            report[].append({
                : name, : ,
                : legit_sections[name][],
                : props[],
            })

    
    legit_imports = ()
     (legit_pe, ):
         entry  legit_pe.DIRECTORY_ENTRY_IMPORT:
             imp  entry.imports:
                 imp.name:
                    legit_imports.add()

    suspect_imports = ()
     (suspect_pe, ):
         entry  suspect_pe.DIRECTORY_ENTRY_IMPORT:
             imp  entry.imports:
                 imp.name:
                    suspect_imports.add()

    new_imports = suspect_imports - legit_imports
     new_imports:
        report[] = (new_imports)

    
    report[] = (legit_pe.OPTIONAL_HEADER.DATA_DIRECTORY[].Size)
    report[] = (suspect_pe.OPTIONAL_HEADER.DATA_DIRECTORY[].Size)

     report


 ():
    
    hashes = {}
     (filepath, )  f:
        data = f.read()
     algo  [, , ]:
        h = hashlib.new(algo)
        h.update(data)
        hashes[algo] = h.hexdigest()
     hashes


 __name__ == :
     (sys.argv) < :
        ()
        sys.exit()
    report = compare_pe_files(sys.argv[], sys.argv[])
    (json.dumps(report, indent=))

Validation Criteria

• Trojanized components identified through binary diffing
• Injected code isolated and analyzed separately
• Code signing anomalies documented
• Infection timeline reconstructed from build artifacts
• Downstream impact scope assessed across affected systems
• IOCs extracted for detection and blocking

References

• ReversingLabs - 3CX Supply Chain Analysis
• Fortinet - SolarWinds Supply Chain Attack
• Picus - 3CX SmoothOperator Analysis
• MITRE ATT&CK T1195 - Supply Chain Compromise