hallucinated-packages-anti-pattern

name	hallucinated-packages-anti-pattern
description	Security anti-pattern for hallucinated (non-existent) packages (CWE-1357). Use when generating or reviewing AI-assisted code that imports packages, dependencies, or libraries. CRITICAL AI-specific vulnerability with 5-21% hallucination rate. Detects dependency confusion and slopsquatting risks.

Hallucinated Packages Anti-Pattern

Severity: Critical

Summary

AI models hallucinate non-existent software packages at rates of 5-21%. Attackers exploit this through slopsquatting: registering hallucinated package names with malicious code. Developers installing AI-suggested packages without verification execute attacker code, leading to malware execution, credential theft, and system compromise. This AI-specific supply chain attack exploits the trust gap between AI suggestions and package verification.

The Anti-Pattern

Never install AI-suggested packages without verifying existence, legitimacy, and reputation in official registries.

BAD Code Example

# An AI model generates the following code snippet and instruction:
# "To handle advanced image processing, you should use the `numpy-magic` library.
# First, install it using pip:"
#
# $ pip install numpy-magic

import numpy_magic as npmagic

def process_image(image_path):
    # The developer assumes `numpy-magic` is a real, safe library.
    # However, it doesn't exist, and an attacker has registered it on PyPI.
    # The moment it was installed, the attacker's code ran.
    # The import itself could also trigger malicious code.
    processed = npmagic.enhance(image_path)
    return processed

In this scenario, the developer follows the AI's instructions without question. The numpy-magic package is not a real library. An attacker, anticipating this hallucination, has published a malicious package with that exact name. The developer's pip install command downloads and executes the attacker's code, compromising their machine and potentially the entire project.

GOOD Code Example

# SECURE: Verify the package before installing.

# Before installing `numpy-magic`, the developer performs a few checks.

# 1. Search for the package on the official repository (e.g., PyPI, npm).
#    A search for "numpy-magic" on PyPI yields no results or shows a package
#    with very low downloads and a recent creation date. This is a major red flag.

# 2. Look for signs of legitimacy.
#    - Does the package have a link to a GitHub repository?
#    - Is the repository active?
#    - How many weekly downloads does it have? (Is it in the single digits or thousands?)
#    - Who are the maintainers?
#    - Are there any open issues or security advisories?

# 3. Search for the *functionality* instead of the package name.
#    A search for "advanced numpy image processing" leads to well-known libraries
#    like `scikit-image`, `OpenCV (cv2)`, or `Pillow (PIL)`, which are reputable.

# The developer chooses a legitimate, well-known library instead.
from skimage import io, filters

def process_image(image_path):
    image = io.imread(image_path)
    # Use a function from a verified, reputable library.
    processed = filters.gaussian(image, sigma=1)
    return processed

Language-Specific Examples

JavaScript/Node.js:

// VULNERABLE: AI suggests non-existent package
// AI: "Install express-jwt-secure for enhanced JWT security"
// $ npm install express-jwt-secure

const jwtSecure = require('express-jwt-secure'); // Malicious package!

app.use(jwtSecure.protect());

// SECURE: Verify before installing
// 1. Check npm: $ npm view express-jwt-secure
//    Result: "404 Not Found" - hallucination detected!
// 2. Search for real alternatives: "express jwt authentication"
// 3. Use verified packages with high download counts

const jwt = require('jsonwebtoken'); // 20M+ weekly downloads
const expressJWT = require('express-jwt'); // 1M+ weekly downloads

app.use(expressJWT({
  secret: process.env.JWT_SECRET,
  algorithms: ['HS256']
}));

Java/Maven:

<!-- VULNERABLE: AI suggests non-existent dependency -->
<!-- AI: "Add apache-commons-cryptography for encryption" -->
<dependency>
    <groupId>org.apache.commons</groupId>
    <artifactId>commons-cryptography</artifactId>
    <version>1.0.0</version>
</dependency>

<!-- SECURE: Verify on Maven Central first -->
<!-- Search: https://search.maven.org/search?q=commons-cryptography -->
<!-- No results - hallucination! -->
<!-- Real alternative: Apache Commons Crypto -->
<dependency>
    <groupId>org.apache.commons</groupId>
    <artifactId>commons-crypto</artifactId>
    <version>1.2.0</version>
</dependency>

Detection

• Verify Package Existence: Search official registries before installing:
  • Python: pip index versions <package-name> or visit pypi.org
  • Node.js: npm view <package-name> or visit npmjs.com
  • Reject packages created within 48 hours or with < 100 weekly downloads
• Check for Typosquatting: Compare against popular packages using pip search or fuzzy matching tools
• Review Package Statistics: Check downloads, release history, maintainers, GitHub stars:
  • npm view <package> time created dist-tags downloads
  • Inspect package.json repository field for active GitHub repos
• Use Auditing Tools: Integrate into CI/CD:
  • npm audit / pip-audit for known vulnerabilities
  • socket.dev for AI hallucination detection
  • osv-scanner for supply chain risks

Prevention

• Always verify a package's existence and reputation on its official registry before installing it.
• Never blindly trust a package name suggested by an AI. Treat it as a hint, not a command.
• Check package download counts, creation dates, and maintainer reputation.
• Use lockfiles (package-lock.json, Pipfile.lock, yarn.lock) to ensure that you are always installing the same version of a dependency.
• Configure a private registry or an approved list of packages for your organization to prevent developers from installing untrusted dependencies.
• Integrate dependency scanning and auditing tools into your CI/CD pipeline.

Related Security Patterns & Anti-Patterns

• Missing Input Validation Anti-Pattern: The core issue is a failure to validate the "input" from the AI model.

References

• OWASP Top 10 A03:2025 - Software Supply Chain Failures
• OWASP GenAI LLM03:2025 - Supply Chain
• OWASP API Security API10:2023 - Unsafe Consumption of APIs
• CWE-1357: Reliance on Unverified Package
• CAPEC-538: Open-Source Library Manipulation
• USENIX Study on Package Hallucination
• Socket.dev: AI Package Hallucinations
• Source: sec-context