analyzing-supply-chain-malware-artifacts

analyzing-supply-chain-malware-artifacts is a skill for Claude Code, Codex from pinkpixel-dev/skills-collection-1. It costs 42 tokens per session (1,339 once invoked), scanned A, a copy of analyzing-supply-chain-malware-artifacts, Apache-2.0.

A procedure for investigating malware introduced through trusted software suppliers, updates, build systems, or dependencies.

In plain words
What is it for?
Use it to compare suspicious binaries with trusted versions, inspect build artifacts and code-signing details, and trace the path from the initial compromise to delivered malware.
Why use it?
It helps determine whether legitimate-looking software was altered and how far a supply-chain compromise spread.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to compare suspicious binaries with trusted versions, inspect build artifacts and code-signing details, and trace the path from the initial compromise to delivered malware.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/pinkpixel-dev/skills-collection-1/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 pinkpixel-dev/skills-collection-1 --skill analyzing-supply-chain-malware-artifacts
Clone the repo
git clone --depth 1 https://github.com/pinkpixel-dev/skills-collection-1

Made for: Claude Code, Codex.

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/pinkpixel-dev/skills-collection-1/analyzing-supply-chain-malware-artifacts/github.svg)](https://agentmods.dev/skills/pinkpixel-dev/skills-collection-1/analyzing-supply-chain-malware-artifacts)
Your own site
<a href="https://agentmods.dev/skills/pinkpixel-dev/skills-collection-1/analyzing-supply-chain-malware-artifacts"><img src="https://agentmods.dev/badge/skills/pinkpixel-dev/skills-collection-1/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/pinkpixel-dev/skills-collection-1/analyzing-supply-chain-malware-artifacts"><img src="https://agentmods.dev/badge/skills/pinkpixel-dev/skills-collection-1/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 1,339 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.
Origin 91% copy Near-identical to another mod 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 $0.01339
Opus 5 $0.00021 $0.00669
Sonnet 5 $0.00008 $0.00268
Haiku 4.5 $0.00004 $0.00134

Measured 7d ago against content hash ad35db035b0d, 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 7d 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

This is a copy

91% identical to analyzing-supply-chain-malware-artifacts — 39 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

SKILLS/analyzing-supply-chain-malware-artifacts/SKILL.md · 142 lines

How it starts

The opening of the file, as written. The whole thing — 142 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 · 142 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. 7d ago First seen · 142 lines · 42 tokens per session scan A ad35db035b0d

Subscribe to this mod's changes

analyzing-supply-chain-malware-artifacts is a skill published in the GitHub repository pinkpixel-dev/skills-collection-1 (7 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 42 tokens to every session and 1,339 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to analyzing-supply-chain-malware-artifacts, differing in 39 lines, and is treated as a copy.

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