Anthropic-Cybersecurity-Skills/skills/analyzing-supply-chain-malware-artifacts/SKILL.md

name, description, domain, subdomain, tags, version, author, license, atlas_techniques, nist_ai_rmf, d3fend_techniques, nist_csf

name	description	domain	subdomain	tags	version	author	license	atlas_techniques	nist_ai_rmf	d3fend_techniques	nist_csf
analyzing-supply-chain-malware-artifacts	Investigate supply chain attack artifacts including trojanized software updates, compromised build pipelines, and sideloaded dependencies to identify intrusion vectors and scope of compromise.	cybersecurity	malware-analysis	supply-chain malware-analysis trojanized-software solarwinds 3cx dependency-confusion software-integrity	1.0	mahipal	Apache-2.0	AML.T0010 AML.T0104	GOVERN-5.2 MAP-1.6 MANAGE-2.2	Platform Hardening Hardware Component Inventory Restore Object Electromagnetic Radiation Hardening RF Shielding	DE.AE-02 RS.AN-03 ID.RA-01 DE.CM-01

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.

When to Use

• When investigating security incidents that require analyzing supply chain malware artifacts
• 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

• 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)

Workflow

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"], 2),
            })
        elif abs(props["size"] - legit_sections[name]["size"]) > 1024:
            report["suspicious_sections"].append({
                "name": name, "reason": "Section size significantly changed",
                "legit_size": legit_sections[name]["size"],
                "suspect_size": props["size"],
            })

    # Compare imports
    legit_imports = set()
    if hasattr(legit_pe, 'DIRECTORY_ENTRY_IMPORT'):
        for entry in legit_pe.DIRECTORY_ENTRY_IMPORT:
            for imp in entry.imports:
                if imp.name:
                    legit_imports.add(f"{entry.dll.decode()}!{imp.name.decode()}")

    suspect_imports = set()
    if hasattr(suspect_pe, 'DIRECTORY_ENTRY_IMPORT'):
        for entry in suspect_pe.DIRECTORY_ENTRY_IMPORT:
            for imp in entry.imports:
                if imp.name:
                    suspect_imports.add(f"{entry.dll.decode()}!{imp.name.decode()}")

    new_imports = suspect_imports - legit_imports
    if new_imports:
        report["import_changes"] = list(new_imports)

    # Check code signing
    report["legit_signed"] = bool(legit_pe.OPTIONAL_HEADER.DATA_DIRECTORY[4].Size)
    report["suspect_signed"] = bool(suspect_pe.OPTIONAL_HEADER.DATA_DIRECTORY[4].Size)

    return report


def hash_file(filepath):
    """Calculate multiple hashes for a file."""
    hashes = {}
    with open(filepath, 'rb') as f:
        data = f.read()
    for algo in ['md5', 'sha1', 'sha256']:
        h = hashlib.new(algo)
        h.update(data)
        hashes[algo] = h.hexdigest()
    return hashes


if __name__ == "__main__":
    if len(sys.argv) < 3:
        print(f"Usage: {sys.argv[0]} <legitimate_binary> <suspect_binary>")
        sys.exit(1)
    report = compare_pe_files(sys.argv[1], sys.argv[2])
    print(json.dumps(report, indent=2))

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