Mitigating Package Hallucinations in Large Language Models via Model Editing

• URL: http://arxiv.org/abs/2607.02052v1
• Date: Thu, 02 Jul 2026 11:27:01 GMT
• Title: Mitigating Package Hallucinations in Large Language Models via Model Editing
• Abstract summary: BOUND is a lightweight localized model editing framework for mitigating package hallucinations in large language models.<n>It first locates modules related to package hallucination through a risk-aware localization strategy, and then edits these modules with lightweight LoRA.<n> Experimental results show that BOUND effectively reduces package hallucinations while preserving valid package recommendations.
• Score: 11.455817131025675
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have demonstrated strong capabilities in software engineering tasks, such as code generation, library recommendation, and dependency configuration. However, recent studies show that LLMs may suffer from package hallucination, where they generate non-existent or invalid package names. These hallucinations can be exploited in software supply chain attacks, as attackers may register malicious packages under hallucinated names. Therefore, mitigating package hallucination is important for improving the reliability and security of LLM-assisted software development. In this paper, we introduce BOUND, a lightweight localized model editing framework for mitigating package hallucinations in LLMs. BOUND formulates package hallucination mitigation as a package-validity boundary editing problem, where the boundary refers to the model's ability to distinguish valid packages from hallucinated package names under a given task context. It first locates modules related to package hallucination through a risk-aware localization strategy, and then edits these modules with lightweight LoRA adapters using a boundary-aware objective that reinforces valid packages, suppresses hallucinated packages, and preserves locality behavior. Experimental results show that BOUND effectively reduces package hallucinations while preserving valid package recommendations. In the package recommendation task, BOUND reduces package-level hallucination rate (Package-HR) by 79.9% on edit prompts and by 65.4% on unseen prompts. The learned package-validity boundary further generalizes to other package-related tasks, reducing Package-HR by 12.8% in code generation and by 34.0% in pip install recommendation. These results show that BOUND refines the package-validity boundary of LLMs and improves the reliability of package-related outputs.

Related papers

• Harmless Yet Harmful: Neutral Prompting Attacks for Stealthy Hallucination Steering in Agent Skills [15.148439025792017]
  This paper introduces emphNeutral Prompting Attack (NPA)<n>NPA is a stealthy attack paradigm in which semantically benign instructions, such as encouraging imagination, increase package hallucination propensity without containing explicit malicious intent.<n>Our results show that NPA increases both emphHallucination ASR and emphPip Install ASR, changes the distribution of hallucinated package names, and evades existing static-analysis, LLM-based, and agent-based Skill defenses.
  arXiv  Detail & Related papers  (2026-05-28T04:48:45Z)
• The Range Shrinks, the Threat Remains: Re-evaluating LLM Package Hallucinations on the 2026 Frontier-Model Cohort [51.56484100374058]
  Spracklen et al. showed that code-generating large language models hallucinate package names that do not exist on PyPI or npm.<n>We measure hallucination rates across 199,845 paired Python and JavaScript prompts validated against PyPI and npm master lists.<n>We identify a set of 127 package names (109 on PyPI, 18 on npm) that all five evaluated models invent identically.
  arXiv  Detail & Related papers  (2026-05-16T16:08:52Z)
• PackMonitor: Enabling Zero Package Hallucinations Through Decoding-Time Monitoring [14.864903095382937]
  We argue that package hallucinations are theoretically preventable based on the key insight that package validity is decidable through finite and enumerable authoritative package lists.<n>We propose PackMonitor, the first approach capable of fundamentally eliminating package hallucinations by continuously monitoring the model's decoding process and intervening when necessary.
  arXiv  Detail & Related papers  (2026-02-24T09:26:11Z)
• A Systematic Literature Review of Code Hallucinations in LLMs: Characterization, Mitigation Methods, Challenges, and Future Directions for Reliable AI [54.34738767990601]
  As Large Language Models become increasingly integrated into software engineering tasks, understanding and mitigating hallucination in code becomes essential.<n>We provide a systematic review of hallucination phenomena in code-oriented LLMs from four key perspectives.
  arXiv  Detail & Related papers  (2025-11-02T02:58:41Z)
• HFuzzer: Testing Large Language Models for Package Hallucinations via Phrase-based Fuzzing [8.667234284704655]
  Large Language Models (LLMs) are widely used for code generation, but they face critical security risks when applied to practical production.<n>It is critical to test LLMs for package hallucinations to mitigate package hallucinations and defend against potential attacks.<n>We propose HFUZZER, a novel phrase-based fuzzing framework to test LLMs for package hallucinations.
  arXiv  Detail & Related papers  (2025-09-28T12:16:43Z)
• Mitigating Hallucinations in Multimodal LLMs via Object-aware Preference Optimization [55.543583937522804]
  Multimodal Large Language Models (MLLMs) emerge as a unified interface to address a multitude of tasks.<n>Despite showcasing state-of-the-art results in many benchmarks, a long-standing issue is the tendency of MLLMs to hallucinate.<n>In this paper, we address the problem of hallucinations as an alignment problem, seeking to steer the MLLM so that it prefers generating content without hallucinations.
  arXiv  Detail & Related papers  (2025-08-27T18:02:04Z)
• JPS: Jailbreak Multimodal Large Language Models with Collaborative Visual Perturbation and Textual Steering [73.962469626788]
  Jailbreak attacks against multimodal large language Models (MLLMs) are a significant research focus.<n>We propose JPS, underlineJailbreak MLLMs with collaborative visual underlinePerturbation and textual underlineSteering.
  arXiv  Detail & Related papers  (2025-08-07T07:14:01Z)
• Generate, but Verify: Reducing Hallucination in Vision-Language Models with Retrospective Resampling [78.78822033285938]
  Vision-Language Models (VLMs) excel at visual understanding but often suffer from visual hallucinations.<n>In this work, we introduce REVERSE, a unified framework that integrates hallucination-aware training with on-the-fly self-verification.
  arXiv  Detail & Related papers  (2025-04-17T17:59:22Z)
• Importing Phantoms: Measuring LLM Package Hallucination Vulnerabilities [11.868859925111561]
  Large Language Models (LLMs) have become an essential tool in the programmer's toolkit.<n>Their tendency to hallucinate code can be used by malicious actors to introduce vulnerabilities to broad swathes of the software supply chain.
  arXiv  Detail & Related papers  (2025-01-31T10:26:18Z)
• We Have a Package for You! A Comprehensive Analysis of Package Hallucinations by Code Generating LLMs [3.515912713354746]
  Package hallucinations arise from fact-conflicting errors when generating code using Large Language Models.<n>This paper conducts a rigorous and comprehensive evaluation of package hallucinations across different programming languages.<n>We show that the average percentage of hallucinated packages is at least 5.2% for commercial models and 21.7% for open-source models.
  arXiv  Detail & Related papers  (2024-06-12T03:29:06Z)
• DrAttack: Prompt Decomposition and Reconstruction Makes Powerful LLM Jailbreakers [74.7446827091938]
  We introduce an automatic prompt textbfDecomposition and textbfReconstruction framework for jailbreak textbfAttack (DrAttack) DrAttack includes three key components: (a) Decomposition' of the original prompt into sub-prompts, (b) Reconstruction' of these sub-prompts implicitly by in-context learning with semantically similar but harmless reassembling demo, and (c) a Synonym Search' of sub-prompts, aiming to find sub-prompts' synonyms that maintain the original intent while
  arXiv  Detail & Related papers  (2024-02-25T17:43:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.