arXiv Machine Learning
LLM Ghostbusters: Surgical Package Hallucination Suppression via Adaptive Unlearning
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arXiv Machine Learning
Sep 7
Harmless Yet Harmful: Neutral Prompting Attacks for Stealthy Hallucination Steering in Agent Skills
The paper introduces the Neutral Prompting Attack (NPA), a stealthy method that uses semantically benign instructions to increase the likelihood of large language models hallucinating non‑existent package names in coding agents. Unlike traditional dependency steering, NPA does not target a specific package but shifts the model’s output toward more speculative names. Experiments across multiple LLMs show that NPA raises hallucination rates, affects pip install success, alters the distribution of hallucinated packages, and bypasses existing static‑analysis, LLM‑based, and agent‑based defenses.
arXiv AI
Aug 25
Evaluating Inference-Time Defenses Against Package Hallucination in LLM-Generated Code
The paper investigates how large language models (LLMs) hallucinate nonexistent software packages during code generation and evaluates methods to mitigate this issue. It finds that current evaluation practices overestimate hallucination rates, especially for Python, and that Retrieval-Augmented Generation (RAG) and Self-Refine reduce hallucinations across multiple models and languages. The study also introduces Package Utility (PU) to measure whether defenses preserve useful recommendations and shows that Greedy decoding offers the best trade‑off between mitigation and utility, while adversarial prompts significantly increase hallucination rates, particularly in Ruby.
arXiv Computation and Language
Aug 24
Library Hallucinations in LLM-Generated Code: A Risk Analysis Grounded in Developer Queries
arXiv:2509.22202v4 Announce Type: replace-cross Abstract: Large language models (LLMs) now play a central role in code generation, yet they continue to hallucinate, frequently inventing non-existent...
arXiv AI
Aug 26
Names Can Hurt: Spotting Slopsquatting Risks Caused by Package Name Hallucinations in Local Coding LLMs
The paper introduces a two‑layer detector to prevent ‘slopsquatting’—the risk of local coding LLMs fabricating Python package names that adversaries can pre‑register on PyPI. The first layer checks PyPI for existence, while the second uses a Random Forest classifier on ten name‑and‑metadata features; an import reconciler resolves naming mismatches. Embedded in a LangGraph state machine, the system retries at escalating temperatures and falls back to stronger models, achieving hallucination‑free code in 76% of 300 curated prompts and recovering additional runs through intra‑ and cross‑model retries.
arXiv Machine Learning
Aug 11
The Range Shrinks, the Threat Remains: Re-evaluating LLM Package Hallucinations on the 2026 Frontier-Model Cohort
arXiv:2605. 17062v3 Announce Type: replace-cross Abstract: Spracklen et al.
arXiv AI
Sep 15
Measuring and Exploiting Contextual Bias in LLM-Assisted Security Code Review
arXiv:2603.18740v3 Announce Type: replace-cross Abstract: Automated Code Review (ACR) systems integrating Large Language Models (LLMs) are increasingly adopted in software development workflows, rang...