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Names Can Hurt: Spotting Slopsquatting Risks Caused by Package Name Hallucinations in Local Coding LLMs

The paper 'Names Can Hurt' studies slopsquatting, where local coding LLMs hallucinate Python package names that adversaries can pre-register on PyPI to achieve supply-chain compromise, and proposes a two-layer detector combining a deterministic PyPI existence check with a Random Forest classifier embedded in a LangGraph retry pipeline. Across 300 curated prompts the pipeline yields hallucination-free code on 76% of runs, and the authors find hallucination rates scale with prompt adversariality (up to 40-73% on slopsquat baits) and that same-family fallback models fail to recover ~84% of primary failures. Details →

The Range Shrinks, the Threat Remains: Re-evaluating LLM Package Hallucinations on the 2026 Frontier-Model Cohort

A replication study by Aleksandr Churilov re-evaluated package-name hallucination across five 2026 frontier code LLMs (Claude Sonnet 4.6, Claude Haiku 4.5, GPT-5.4-mini, Gemini 2.5 Pro, DeepSeek V3.2), measuring hallucination rates of 4.62%-6.10% across ~199,845 Python/JavaScript prompts. The authors identified 127 package names all five models invent identically and, after coordinated disclosure with PyPI Security and Socket, found 53 (41 PyPI, 12 npm) remain registrable by an attacker, forming a model-agnostic slopsquatting supply-chain attack surface. Details →
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