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 26zl/cybersec-toolkit --skill analyzing-supply-chain-malware-artifactsgit clone --depth 1 https://github.com/26zl/cybersec-toolkitWrote 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/26zl/cybersec-toolkit/analyzing-supply-chain-malware-artifacts)<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.
<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>- NVIDIA SkillSpector pass
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 | $0.01494 |
| Opus 5 | $0.00021 | $0.00747 |
| Sonnet 5 | $0.00008 | $0.00299 |
| 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.
Copies of this mod
3 near-identical copies found in the catalogue:
- analyzing-supply-chain-malware-artifacts — 95% identical, 1 lines differ
- analyzing-supply-chain-malware-artifacts — 91% identical, 39 lines differ
- analyzing-supply-chain-malware-artifacts — 91% identical, 39 lines differ
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))
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.
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 · 175 lines · 42 tokens per session scan A 4a899059f442
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.
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.
implementing-gcp-binary-authorization
Implement GCP Binary Authorization to enforce deploy-time security controls that ensure only trusted, attested container images are deployed to Google Kubernetes Engine and Cloud Run.