analyzing-supply-chain-malware-artifacts

analyzing-supply-chain-malware-artifacts is a skill for Claude Code from 26zl/cybersec-toolkit. It costs 42 tokens per session (1,494 once invoked), scanned A, original, MIT.

A malware-forensics workflow for investigating software-supply-chain attacks, where trusted updates, build systems, or dependencies are compromised to deliver malicious code. It examines binaries, build artifacts, signatures, and the infection path.

In plain words
What is it for?
Use it to compare trojanized and legitimate binaries, find injected code, inspect signing anomalies, examine compromised build pipelines, and trace payload delivery.
Why use it?
Malicious changes can enter through software that users already trust. Comparing legitimate and altered artifacts helps determine how the compromise happened and how far it spread.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

Part of the cybersec-toolkit plugin — 197 skills, 2 hooks, 1 MCP server shipped together

Good fit Use it to compare trojanized and legitimate binaries, find injected code, inspect signing anomalies, examine compromised build pipelines, and trace payload delivery.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/26zl/cybersec-toolkit/analyzing-supply-chain-malware-artifacts
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add 26zl/cybersec-toolkit --skill analyzing-supply-chain-malware-artifacts
Clone the repo
git clone --depth 1 https://github.com/26zl/cybersec-toolkit

Made for: Claude Code.

Or install cybersec-toolkit, the plugin that ships this one along with the rest of its 197 skills, 2 hooks, 1 MCP server.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for analyzing-supply-chain-malware-artifacts

README.md
[![agentmods](https://agentmods.dev/badge/skills/26zl/cybersec-toolkit/analyzing-supply-chain-malware-artifacts/github.svg)](https://agentmods.dev/skills/26zl/cybersec-toolkit/analyzing-supply-chain-malware-artifacts)
Your own site
<a href="https://agentmods.dev/skills/26zl/cybersec-toolkit/analyzing-supply-chain-malware-artifacts"><img src="https://agentmods.dev/badge/skills/26zl/cybersec-toolkit/analyzing-supply-chain-malware-artifacts/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for analyzing-supply-chain-malware-artifacts

Your own site · 80×15
<a href="https://agentmods.dev/skills/26zl/cybersec-toolkit/analyzing-supply-chain-malware-artifacts"><img src="https://agentmods.dev/badge/skills/26zl/cybersec-toolkit/analyzing-supply-chain-malware-artifacts.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 42 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,494 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00042 $0.01494
Opus 5 $0.00021 $0.00747
Sonnet 5 $0.00008 $0.00299
Haiku 4.5 $0.00004 $0.00149

Measured 6d ago against content hash 4a899059f442, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

analyzing-supply-chain-malware-artifacts scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/agent.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

3 near-identical copies found in the catalogue:

.claude/skills/analyzing-supply-chain-malware-artifacts/SKILL.md · 175 lines

How it starts

The opening of the file, as written. The whole thing — 175 lines — stays where its author put it; the contents beside it link to each section 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["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))

Read the full file on GitHub · 175 lines

Files

What ships with it

6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 6d ago First seen · 175 lines · 42 tokens per session scan A 4a899059f442

Subscribe to this mod's changes

analyzing-supply-chain-malware-artifacts is a skill published in the GitHub repository 26zl/cybersec-toolkit (52 stars, last pushed today), licensed MIT. It adds 42 tokens to every session and 1,494 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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