Package Hallucination: Impacts and Mitigation

As AI tools become integral to our development workflows, package hallucination represents more than just a productivity hiccup— a growing security concern that demands our immediate attention. Understanding this phenomenon allows us to debug faster while building secure, resilient development practices in an AI-augmented world.

Understanding package hallucination

What are package hallucinations?

Package hallucination represents a specific type of AI error in which code generation models suggest plausible-sounding but completely non-existent software packages or dependencies. Unlike traditional AI hallucinations, which produce factually incorrect information, package hallucination creates fabricated software components that developers might unknowingly attempt to install.

This phenomenon emerged prominently around 2021-2022 with the widespread adoption of AI coding assistants like GitHub Copilot and ChatGPT for software development. AI models learn statistical patterns from their training data, including naming conventions for popular packages, then extrapolate to create convincing but fictional alternatives.

Consider this example where an AI suggests a non-existent package:

# AI-suggested code with hallucinated package
import crypto-secure-hash

def hash_password(password):
    return crypto-secure-hash.sha256_secure(password)

The package crypto-secure-hash sounds legitimate and follows typical naming patterns, but doesn't exist in any package repository.

Why this happens: AI models recognize that packages often combine descriptive terms like "crypto," "secure," or "hash," leading them to generate plausible combinations. The training data contains thousands of real package names, and models learn these linguistic patterns without understanding actual package availability.

This creates unique risks in AI-assisted development, as developers might waste time searching for non-existent packages or, worse, unknowingly create security vulnerabilities when attempting to implement suggested but fictional dependencies.

How AI creates these phantom packages

AI models generate phantom packages through sophisticated pattern recognition and statistical extrapolation mechanisms learned during training. These models analyze vast codebases, identifying recurring naming conventions and structural patterns across different programming ecosystems.

Technical mechanisms behind AI package generation

The core process involves several interconnected mechanisms:

• Training data influence: Models absorb millions of legitimate package names from repositories, documentation, and code samples, building statistical representations of naming conventions.

• Pattern recognition: AI identifies common prefixes (react-, vue-, @types/), suffixes (-utils, -core, -plugin), and structural patterns (kebab-case, camelCase, snake_case).

• Probabilistic generation: When generating code, models use learned probability distributions to construct plausible-sounding package names by combining recognized elements.

• Cross-language confusion: Models often mix naming conventions from different ecosystems, creating Python packages with npm-style scoping or JavaScript modules with Rust crate patterns.

The fundamental limitation lies in AI's lack of real-time validation. During code generation, models cannot verify package existence against live registries like npm, PyPI, or crates.io. They rely purely on statistical likelihood rather than actual availability.

We see this manifest when models confidently suggest packages like @utils/string-helper or pandas-advanced-analytics — names that follow learned patterns perfectly but don't exist. The AI's training creates false confidence in these statistically probable but non-existent dependencies, leading developers down frustrating installation rabbit holes.

Categories of hallucinated dependencies

1. Completely fabricated packages: AI models generate convincing package names that don't exist, such as crypto-validator or auth-helper-pro. These appear legitimate but create opportunities for typosquatting attacks where malicious actors register these names with harmful code.

2. Misspelled or typosquatted versions: Common misspellings include reqeusts instead of requests or numppy instead of numpy. These typos mirror existing typosquatting campaigns and can lead developers to install malicious packages with nearly identical names.

3. Version hallucinations AI suggests non-existent versions like pandas==2.5.0 or tensorflow==3.2.1. Developers attempting to install these versions may face dependency resolution failures or unknowingly install compromised packages if attackers publish fake versions.

4. Functionality hallucinations: Models recommend real packages for purposes they don't support, such as suggesting matplotlib for audio processing or requests for database operations. This misdirection wastes development time and may introduce unnecessary dependencies.

5. Cross-language package confusion: AI mixes ecosystems by suggesting Python packages for JavaScript projects or npm modules for Python development. For example, recommending lodash in Python code or pandas in Node.js applications creates confusion and potential security gaps.

Each category requires different mitigation strategies and poses unique risks to our development pipelines and security posture.

Package hallucination security implications and prevention

Splotsquatting

Slopsquatting represents one of the most insidious supply chain attack vectors we face in the AI era. This attack exploits AI's tendency to hallucinate non-existent packages, creating unprecedented vulnerabilities in our development workflows. This attack vector leverages package confusion techniques, where legitimate-sounding names mask dangerous code.

How slopsquatting attacks unfold:

1. AI hallucination: When we ask AI coding assistants for help, they confidently suggest packages that don't exist.

2. Developer integration: We unknowingly include these fictional packages in our code repositories.

3. Malicious registration: Attackers monitor AI outputs and register the hallucinated package names with malicious payloads.

4. Compromise: When we later attempt to install dependencies, we unknowingly download the attacker's malicious code.

Real-world implications

Consider a scenario where an AI suggests "secure-jwt-validator" for token validation. We integrate it into our authentication system, then an attacker registers this package with backdoor functionality. Our entire security infrastructure becomes compromised through what appeared to be a helpful AI suggestion.

In a recent experiment in 2023, Bar Lanyado uploaded an empty package named ‘huggingface-cli’, simulating a hallucinated name. The package received over 30,000 downloads in three months. This was when the term "AI package hallucination" was first identified. In this phase of the research, Lanyado not only uploaded an empty software package but also used the APIs of GPT-3.5-Turbo, GPT-4, Gemini Pro (Bard), Coral, and Cohere to search for hallucinated packages. The results showed that GPT-3.5-Turbo, GPT-4, and Cohere generated hallucinated responses roughly 20% of the time, while Gemini did so at a much higher rate of 64.5%.

Unlike traditional typosquatting, slopsquatting creates entirely new attack surfaces. Attackers don't need to guess popular package names; they simply monitor AI outputs and weaponize the hallucinations. This creates a dangerous feedback loop where our reliance on AI assistance directly enables supply chain compromises.

We must implement package verification protocols and maintain vigilance when integrating AI-suggested dependencies into production systems.

Impact on development workflows

Package hallucination significantly disrupts development workflows, creating cascading issues that affect every stage of the software delivery pipeline. When AI-generated code references non-existent packages, we face immediate build failures that halt our development process.

These hallucinated packages slip through initial code reviews, only to surface during integration testing or deployment, creating frustrating bottlenecks in our CI/CD pipelines.

The impact on our workflows includes:

• Developer frustration from repeatedly encountering mysterious build errors.

• Delayed releases as teams spend time identifying and removing phantom dependencies.

• Increased security review workload when SCA tools flag non-existent packages as "unknown risks".

• Contaminated dependency trees requiring manual cleanup and validation.

• CI/CD pipeline failures that break automated deployment processes.

• Lost productivity from context switching between debugging real issues and AI-generated artifacts.

Technical solutions for package hallucinations

Core verification systems

SBOM (Software Bill of Materials) integration forms the backbone of our dependency tracking strategy. We generate comprehensive SBOMs that document all components, versions, and dependencies throughout the software lifecycle. This enables us to maintain complete visibility into our supply chain.

Snyk's vulnerability detection tools provide critical dependency validation capabilities, leveraging Snyk's extensive database to identify known vulnerabilities and license issues in real time, integrating these checks directly into our CI/CD pipelines.

AI model enhancements

• Prompt engineering: Optimizing prompts for better package recommendation precision.

• Retrieval-Augmented Generation (RAG): Combining external knowledge bases with LLM capabilities for contextual package suggestions.

• Fine-tuning approaches: Training models on curated datasets of verified packages.

Advanced accuracy methods

Curated package lists serve as our trusted baseline, containing pre-vetted, security-approved packages. We combine this with ensemble approaches, where multiple AI models vote on package recommendations, significantly reducing false positives.

Looking ahead

Regulatory bodies are increasingly scrutinizing AI code generators, with potential compliance requirements on the horizon. Industry leaders are revolutionizing AI model training methodologies, implementing more rigorous validation datasets, and developing sophisticated hallucination detection algorithms. 

Snyk continues to pioneer advanced tooling that identifies phantom packages in real-time, integrating into CI/CD pipelines to catch these vulnerabilities before deployment.

Ready to protect your codebase from package hallucination attacks? Snyk Software Composition Analysis (SCA) platform is designed to detect and flag unknown or non-existent packages. Explore Snyk's comprehensive security solutions that help identify and prevent AI-generated vulnerabilities in your software supply chain. Start building more secure, AI-assisted development workflows today.