We Have a Package for You!

A Comprehensive Analysis of Package Hallucinations by Code-Generating LLMs

This repository contains the code, data, and instructions for reproducing the experiments and results from our paper:

We Have a Package for You! A Comprehensive Analysis of Package Hallucinations by Code Generating LLMs
Joseph Spracklen, Raveen Wijewickrama, A H M Nazmus Sakib, Anindya Maiti, Bimal Viswanath, Murtuza Jadliwala
In Proceedings of the USENIX Security Symposium, 2025.
📄 Paper PDF

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🔦 Highlights (TL;DR)

• Large-scale study across 16 LLMs (commercial + open-source), Python and JavaScript
• 576,000 code samples analyzed; 19.7% of recommended packages were hallucinations
• 205,474 unique hallucinated package names discovered
• Effective mitigations evaluated: RAG, self-detection, fine-tuning (largest reduction from fine-tuning)

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📌 Table of Contents

• Overview
• Repository Structure
• Setup
• Usage
• Data
• Reproducing Results
• Mitigation Experiments
• Hardware & Runtime Notes
• Security & Ethics
• Troubleshooting
• Citation
• License
• Contact

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🔍 Overview

Package hallucinations occur when an LLM generates code that references a non-existent package (e.g., via pip install xyz or npm install xyz where xyz does not exist).
This creates a software supply-chain risk: adversaries can upload a malicious package using that hallucinated name.

This repo provides:

• End-to-end pipeline to generate code, extract package names, and measure hallucinations
• Prompt datasets (Stack Overflow–derived and LLM-generated)
• Code to reproduce figures/tables and mitigation experiments

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📂 Repository Structure

.
├── run_test.py                 # Runs a full hallucination detection experiment
├── Models/                     # Place tested models here (one default model included)
├── Data/                       # Prompt datasets & per-language resources
│   ├── Python/
│   │   ├── LLM_AT.json
│   │   ├── LLM_LY.json
│   │   ├── SO_AT.json
│   │   └── SO_LY.json
│   └── JavaScript/
│       ├── LLM_AT.json
│       ├── LLM_LY.json
│       ├── SO_AT.json
│       └── SO_LY.json
├── Tests/                      # Output directory for experiment results (starts empty)
├── Mitigation/                 # Mitigation experiments
│   ├── run_model_RAG.py
│   ├── run_model_SD.py
│   ├── run_model_combined.py
│   ├── Data/                   # RAG DB + build data
│   ├── Fine_tuned/             # Fine-tuned & quantized models used in mitigation testing
│   └── RAG_setup.py            # Builds the vector DB from Mitigation/Data
├── Plots/                      # Code and data to reproduce paper figures
├── environment.yml             # Conda environment
├── requirements.txt            # (Optional) pip dependencies
└── README.md

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⚙️ Setup

1) Clone

git clone https://github.com/Spracks/PackageHallucination.git
cd PackageHallucination

2) Create environment

Using Conda (recommended):

conda env create -f environment.yml
conda activate pkg-hallucination

Or using pip:

python -m venv .venv
source .venv/bin/activate    # Windows: .venv\Scripts\activate
pip install -r requirements.txt

The environment listed is bloated, really just need PyTorch + transformers and the associated dependencies.

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🚀 Usage

Run a full hallucination detection experiment for one model:

python run_test.py DeepSeek_1B --language Python
# or
python run_test.py DeepSeek_1B --language JavaScript

What it does

• Generates code for prompts in Data/<LANG>/
• Extracts package names via the paper’s three heuristics
• Compares against master lists to mark hallucinations
• Writes results/artifacts under Tests/

Notes

• Ensure your chosen model is available under Models/
• End-to-end runs can take 24–72 hours depending on model size and hardware

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📊 Data

Included prompt datasets (per language):

• LLM_AT.json – LLM-generated prompts based on all-time most popular packages
• LLM_LY.json – LLM-generated prompts based on last-year most popular packages
• SO_AT.json – Top Stack Overflow questions (all-time)
• SO_LY.json – Top Stack Overflow questions (last-year)

Each language directory also contains the master list of valid package names used for detection.

We do not publish the master list of hallucinated package names or per-prompt detailed results (see Security & Ethics). Verified researchers can request access.

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📈 Reproducing Results

Reproduce main tables/figures by re-running experiments and then building plots:

# 1) Run experiments (example)
python run_test.py DeepSeek_1B --language Python
python run_test.py CodeLlama_7B --language Python
# ... (repeat for desired model/language combinations)

# 2) Build figures
cd Plots
python reproduce_figures.py

Where applicable, figure scripts read from Tests/ to regenerate the paper plots.

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🛠 Mitigation Experiments

We provide three mitigation strategies:

1. RAG (Retrieval-Augmented Generation)
   Augments prompts with retrieved package-context from a vector DB built from package descriptions.

   # Build RAG DB (once)
   python Mitigation/RAG_setup.py
   # Run RAG experiment
   python Mitigation/run_model_RAG.py DeepSeek_1B --language Python
   

2. Self-Detection / Self-Refinement
   The model checks its own suggested package list; if invalid, regenerate with constraints.

   python Mitigation/run_model_SD.py CodeLlama_7B --language Python
   

3. Fine-Tuning
   Fine-tune on valid (non-hallucinated) package recommendations derived from the pipeline.

   # Use the fine-tuned checkpoints under Mitigation/Fine_tuned/
   python Mitigation/run_model_combined.py DeepSeek_1B --language Python
   

See paper for comparative results; fine-tuning produced the largest hallucination reduction.

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🧰 Hardware & Runtime Notes

• Open-source models were evaluated in quantized form to mimic realistic hardware constraints.
• A single full run can take 24–72 hours depending on model size/GPU availability.
• For reproducibility, stick to the provided environment.yml and keep decoding parameters consistent unless you are explicitly testing RQ2-style variations.

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🔒 Security & Ethics

• We do not publicly release:
  • The master list of hallucinated package names
  • Per-prompt detailed results
• Rationale: releasing these could enable package confusion attacks at scale.
• Access policy: verified researchers may request full results for academic use.
• See the paper’s Ethics Considerations for additional detail.

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🧪 Troubleshooting

• Environment fails to resolve: ensure you’re using the listed CUDA/PyTorch versions in environment.yml.
• Model not found: confirm the checkpoint is placed under Models/ and the name matches your CLI arg.
• Very slow runs: you’re likely on CPU; use a CUDA GPU where possible.
• Different hallucination rates than reported: minor variation is expected across hardware/versions; ensure decoding params and temperatures match defaults in the code.

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📜 Citation

If you use this repository, please cite:

@inproceedings{spracklen2025packagehallucination,
  title     = {We Have a Package for You! A Comprehensive Analysis of Package Hallucinations by Code Generating LLMs},
  author    = {Joseph Spracklen and Raveen Wijewickrama and A H M Nazmus Sakib and Anindya Maiti and Bimal Viswanath and Murtuza Jadliwala},
  booktitle = {USENIX Security Symposium},
  year      = {2025}
}

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📄 License

This project is licensed under the MIT License.

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📬 Contact

Questions or collaboration:

• Joseph Spracklen — joseph.spracklen@my.utsa.edu
• Or open an issue on the repository