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.
npx skills add Youngmaidainon/Agent-Level-Up --skill analyzing-supply-chain-malware-artifactsgit clone --depth 1 https://github.com/Youngmaidainon/Agent-Level-UpWrote 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.
[](https://agentmods.dev/skills/youngmaidainon/agent-level-up/analyzing-supply-chain-malware-artifacts)<a href="https://agentmods.dev/skills/youngmaidainon/agent-level-up/analyzing-supply-chain-malware-artifacts"><img src="https://agentmods.dev/badge/skills/youngmaidainon/agent-level-up/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.
<a href="https://agentmods.dev/skills/youngmaidainon/agent-level-up/analyzing-supply-chain-malware-artifacts"><img src="https://agentmods.dev/badge/skills/youngmaidainon/agent-level-up/analyzing-supply-chain-malware-artifacts.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00042 | $0.01488 |
| Opus 5 | $0.00021 | $0.00744 |
| Sonnet 5 | $0.00008 | $0.00298 |
| Haiku 4.5 | $0.00004 | $0.00149 |
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.
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.
This is a copy
95% identical to analyzing-supply-chain-malware-artifacts — 1 line 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.
How it starts
The opening of the file, as written. The whole thing — 174 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))
What ships with it
5 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.
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.
- 6d ago First seen · 174 lines · 42 tokens per session scan A 056f5fbc5489
analyzing-supply-chain-malware-artifacts is a skill published in the GitHub repository Youngmaidainon/Agent-Level-Up (3 stars, last pushed 16d ago), licensed MIT. It adds 42 tokens to every session and 1,488 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to analyzing-supply-chain-malware-artifacts, differing in 1 line, and is treated as a copy.
Other skills, from other repositories
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.
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.
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.
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.
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.
performing-supply-chain-attack-simulation
Simulate and detect software supply chain attacks including typosquatting detection via Levenshtein distance, dependency confusion testing against private registries, package hash verification with pip, and known vulnerability scanning with pip-audit.