Cover Image

We Have a Package for You! A Comprehensive Analysis of Package Hallucinations by Code Generating LLMs

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Spracklen, Joseph, Wijewickrama, Raveen, Sakib, A H M Nazmus, Maiti, Anindya, Viswanath, Bimal, Jadliwala, Murtuza
Format:	Preprint
Published:	2024
Subjects:	Software Engineering Artificial Intelligence Cryptography and Security Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Prompt and Circumstances: Evaluating the Efficacy of Human Prompt Inference in AI-Generated Art
by: Trinh, Khoi, et al.
Published: (2026)

NinjaDoH: A Censorship-Resistant Moving Target DoH Server Using Hyperscalers and IPNS
by: Seidenberger, Scott, et al.
Published: (2024)

The WASM Cloak: Evaluating Browser Fingerprinting Defenses Under WebAssembly based Obfuscation
by: Sakib, A H M Nazmus, et al.
Published: (2025)

Promptly Yours? A Human Subject Study on Prompt Inference in AI-Generated Art
by: Trinh, Khoi, et al.
Published: (2024)

Unintentional Security Flaws in Code: Automated Defense via Root Cause Analysis
by: Islam, Nafis Tanveer, et al.
Published: (2024)

PackMonitor: Enabling Zero Package Hallucinations Through Decoding-Time Monitoring
by: Liu, Xiting, et al.
Published: (2026)

A Static Analysis of Popular C Packages in Linux
by: Ruohonen, Jukka, et al.
Published: (2024)

An Analysis of Malicious Packages in Open-Source Software in the Wild
by: Zhou, Xiaoyan, et al.
Published: (2024)

How Far Have We Gone in Binary Code Understanding Using Large Language Models
by: Shang, Xiuwei, et al.
Published: (2024)

A Large-scale Fine-grained Analysis of Packages in Open-Source Software Ecosystems
by: Zhou, Xiaoyan, et al.
Published: (2024)

Securing the Software Package Supply Chain for Critical Systems
by: Murali, Ritwik, et al.
Published: (2025)

An Empirical Study of Vulnerable Package Dependencies in LLM Repositories
by: Liu, Shuhan, et al.
Published: (2025)

LLM Ghostbusters: Surgical Hallucination Suppression via Adaptive Unlearning
by: Spracklen, Joseph, et al.
Published: (2026)

Mind the Gap: Evaluating LLMs for High-Level Malicious Package Detection vs. Fine-Grained Indicator Identification
by: Ryan, Ahmed, et al.
Published: (2026)

CHASE: LLM Agents for Dissecting Malicious PyPI Packages
by: Toda, Takaaki, et al.
Published: (2026)

Maven-Hijack: Software Supply Chain Attack Exploiting Packaging Order
by: Reyes, Frank, et al.
Published: (2024)

SoK: Towards Reproducibility for Software Packages in Scripting Language Ecosystems
by: Pohl, Timo, et al.
Published: (2025)

PoCGen: Generating Proof-of-Concept Exploits for Vulnerabilities in Npm Packages
by: Simsek, Deniz, et al.
Published: (2025)

DySec: A Machine Learning-based Dynamic Analysis for Detecting Malicious Packages in PyPI Ecosystem
by: Mehedi, Sk Tanzir, et al.
Published: (2025)

Microservice Vulnerability Analysis: A Literature Review with Empirical Insights
by: Jayalath, Raveen Kanishka, et al.
Published: (2024)

Signing in Four Public Software Package Registries: Quantity, Quality, and Influencing Factors
by: Schorlemmer, Taylor R, et al.
Published: (2024)

The Popularity Hypothesis in Software Security: A Large-Scale Replication with PHP Packages
by: Ruohonen, Jukka, et al.
Published: (2025)

Bridging Expert Reasoning and LLM Detection: A Knowledge-Driven Framework for Malicious Packages
by: Guo, Wenbo, et al.
Published: (2026)

What's in a Package? Getting Visibility Into Dependencies Using Security-Sensitive API Calls
by: Rahman, Imranur, et al.
Published: (2024)

Insecure Ingredients? Exploring Dependency Update Patterns of Bundled JavaScript Packages on the Web
by: Swierzy, Ben, et al.
Published: (2025)

ConfuGuard: Using Metadata to Detect Active and Stealthy Package Confusion Attacks Accurately and at Scale
by: Jiang, Wenxin, et al.
Published: (2025)

ThermalTap: Passive Application Fingerprinting in VR Headsets via Thermal Side Channels
by: Akram, Mahsin Bin, et al.
Published: (2026)

Cutting the Gordian Knot: Detecting Malicious PyPI Packages via a Knowledge-Mining Framework
by: Guo, Wenbo, et al.
Published: (2026)

Does Teaming-Up LLMs Improve Secure Code Generation? A Comprehensive Evaluation with Multi-LLMSecCodeEval
by: Sabir, Bushra, et al.
Published: (2026)

An Empirical Study of Vulnerabilities in Python Packages and Their Detection
by: Quan, Haowei, et al.
Published: (2025)

VocalBridge: Latent Diffusion-Bridge Purification for Defeating Perturbation-Based Voiceprint Defenses
by: Abbasihafshejani, Maryam, et al.
Published: (2026)

Learning to Triage Taint Flows Reported by Dynamic Program Analysis in Node.js Packages
by: Ni, Ronghao, et al.
Published: (2025)

AgentGuard: A Multi-Agent Framework for Robust Package Confusion Detection via Hybrid Search and Metadata-Content Fusion
by: Li, Yu, et al.
Published: (2026)

Developers Are Victims Too : A Comprehensive Analysis of The VS Code Extension Ecosystem
by: Edirimannage, Shehan, et al.
Published: (2024)

Using LLMs for Security Advisory Investigations: How Far Are We?
by: Abdullah, Bayu Fedra, et al.
Published: (2025)

Killing Two Birds with One Stone: Malicious Package Detection in NPM and PyPI using a Single Model of Malicious Behavior Sequence
by: Zhang, Junan, et al.
Published: (2023)

Large Language Models for Code Analysis: Do LLMs Really Do Their Job?
by: Fang, Chongzhou, et al.
Published: (2023)

Residual Risk Analysis in Benign Code: How Far Are We? A Multi-Model Semantic and Structural Similarity Approach
by: Farhad, Mohammad, et al.
Published: (2026)

Automatically Generating Rules of Malicious Software Packages via Large Language Model
by: Zhang, XiangRui, et al.
Published: (2025)

When "Correct" Is Not Safe: Can We Trust Functionally Correct Patches Generated by Code Agents?
by: Peng, Yibo, et al.
Published: (2025)