Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/adriannoes/awesome-agentic-ainpx agentmods add skills/adriannoes/awesome-agentic-ai/detecting-malicious-npm-packagesWrote 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/adriannoes/awesome-agentic-ai/detecting-malicious-npm-packages)<a href="https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/detecting-malicious-npm-packages"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/detecting-malicious-npm-packages/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/adriannoes/awesome-agentic-ai/detecting-malicious-npm-packages"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/detecting-malicious-npm-packages.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 6 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 31 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 75 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- medium Excessive Agency · line 29 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium MCP Rug Pull · line 50 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
- medium MCP Rug Pull · line 51 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
- low Tool Misuse · line 51 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
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 | $0.00027 | $0.02398 |
| Opus 5 | $0.00014 | $0.01199 |
| Sonnet 5 | $0.00005 | $0.00480 |
| Haiku 4.5 | $0.00003 | $0.00240 |
Grade A, and why
detecting-malicious-npm-packages scanned grade A with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -sL "$(npm view [email protected] dist.tarball)" -o some-pkg.tgz Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
grep -rEn "child_process|exec\(|spawn|eval\(|Buffer\.from\(.*base64|process\.env|https?://" package/ \ How it starts
The opening of the file, as written. The whole thing — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Detecting Malicious npm Packages
Legal Notice: Analyze packages in an isolated, disposable environment. Some malicious packages execute on install (
npm installruns lifecycle scripts automatically) or on import. Never analyze a suspect package on a workstation with credentials, SSH keys, cloud tokens, or network access to production. This skill is for defensive analysis and authorized incident response only.
Overview
The npm registry is the largest software package ecosystem in the world and the most heavily targeted by supply-chain attackers. Malicious packages reach victims through typosquatting (expresss, crossenv), dependency confusion, account/maintainer takeover (the 2025 Shai-Hulud worm and the event-stream compromise are canonical examples), and starjacking. The defining danger of npm is that npm install automatically runs preinstall, install, and postinstall lifecycle scripts with the developer's full privileges before any application code is invoked — so simply installing a package is enough to be compromised. Roughly 2% of npm packages use install scripts, which makes them both common and a powerful malware delivery vehicle.
Typical malicious behaviors are: exfiltrating environment variables, ~/.npmrc tokens, SSH keys, and cloud credentials to an attacker-controlled URL; opening reverse shells; dropping cryptominers; reading and posting process.env; obfuscating payloads with base64/eval; and self-propagating (worming) by stealing the maintainer's npm token and republishing trojanized versions of other packages they own.
This skill provides a repeatable triage workflow centered on GuardDog (Datadog's open-source heuristic scanner built on Semgrep + metadata rules), supplemented by manual tarball inspection, lockfile-based compromise checks against known-bad version lists, and dynamic detonation with network and filesystem monitoring. The goal is to decide, quickly and safely, whether a given package or a project's dependency tree contains malicious code.
What ships with it
4 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.
- 9d ago First seen · 200 lines · 27 tokens per session scan A a1537f89b6a9
detecting-malicious-npm-packages is a skill published in the GitHub repository adriannoes/awesome-agentic-ai (57 stars, last pushed 14d ago), licensed MIT. It adds 27 tokens to every session and 2,398 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 2 findings (makes network calls, runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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