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arXiv:2605.01047v2 Announce Type: replace-cross Abstract: Hallucinations remain an unsolved problem for LLMs, and package hallucinations are a particularly dangerous instance of this phenomenon. Pack...
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:2610.27868v1 Announce Type: cross Abstract: Code translation converts source code from one programming language to another while preserving its functionality. Although Large Language Models (LL...
arXiv:2606. 30587v1 Announce Type: cross Abstract: Researchers and practitioners increasingly apply Large Language Models (LLMs) for automated vulnerability detection.
The paper investigates hallucination in large language model–based automated program repair (APR). It defines hallucination as producing patches or intermediate artifacts that are not grounded in available repair evidence, and analyzes it across final patches and intermediate tasks such as triggering test case identification, line coverage prediction, and additional test case generation. Experiments on 832 Defects4J bugs show that only 21.0%–55.9% of patches pass the developer test suite, with 72.7% of sampled repairs exhibiting hallucinations, often due to incorrect causal localization or repair strategies.
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.