Library Hallucinations in LLM-Generated Code: A Risk Analysis Grounded in Developer Queries

Helen Yannakoudakis; Jie M. Zhang; Lukas Twist; Mark Harman

arxiv: 2509.22202 · v3 · pith:JL6HB3QNnew · submitted 2025-09-26 · 💻 cs.SE · cs.CL

Library Hallucinations in LLM-Generated Code: A Risk Analysis Grounded in Developer Queries

Lukas Twist , Jie M. Zhang , Mark Harman , Helen Yannakoudakis This is my paper

Pith reviewed 2026-05-21 22:27 UTC · model grok-4.3

classification 💻 cs.SE cs.CL

keywords library hallucinationsLLM-generated codeprompt variationscode generation riskssoftware supply chainAI hallucinationsbenchmark creation

0 comments

The pith

Small prompt changes like one-character misspellings cause LLMs to invent non-existent libraries in up to 26% of code tasks.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper examines how realistic variations in user prompts affect the tendency of large language models to generate code that references non-existent libraries. It shows that even minor errors, such as single-character misspellings, can lead to invalid imports in a significant portion of generated code, while completely made-up library names are often accepted without question. These issues are studied across multiple models and prompt types, including those involving time references. The authors create a benchmark called LibHalluBench to test and measure these hallucinations systematically. Understanding these patterns is important for developers who rely on AI for coding, as it highlights potential points of failure in builds and security.

Core claim

The central discovery is that library hallucinations in LLM-generated code are highly sensitive to user prompt variations. Specifically, one-character misspellings trigger hallucinations in up to 26% of tasks, fabricated library names are accepted in up to 99% of cases, and time-based prompts induce hallucinations in up to 85%. The study analyzes both library name hallucinations involving invalid imports and library member hallucinations involving invalid calls from valid libraries across seven diverse LLMs. These findings are used to ground the introduction of LibHalluBench, a benchmark for reproducible evaluation of such hallucinations.

What carries the argument

Controlled variations in developer prompts, including misspellings, fabricated library and member names, to measure rates of invalid imports and invalid function calls in generated code.

Load-bearing premise

The specific prompt variations and the seven LLMs tested are representative of how developers actually query code generation tools and make mistakes.

What would settle it

Running the same set of prompt variations on additional LLMs not included in the study or on real-world logs of developer interactions with code assistants, and observing whether the hallucination rates remain consistent.

read the original abstract

Large language models (LLMs) now play a central role in code generation, yet they continue to hallucinate, frequently inventing non-existent libraries. Such library hallucinations are not just benign errors: they can mislead developers, break builds, and expose systems to supply chain threats such as slopsquatting. Despite growing awareness of these risks, there is limited understanding of how library hallucinations manifest under realistic usage conditions. To fill this gap, we present the first systematic study of how user-level prompt variations influence library hallucinations in LLM-generated code. Across seven diverse LLMs, we analyse library name hallucinations (invalid imports) and library member hallucinations (invalid calls from valid libraries), examining the effects of realistic developer language and controlled user mistakes, including misspellings and fabricated libraries or members. Our findings expose systemic vulnerabilities: one-character misspellings trigger hallucinations in up to 26% of tasks; fabricated library names are accepted in up to 99%; and time-based prompts induce hallucinations in up to 85%. Grounded in the highest-risk prompts identified in our study, we introduce LibHalluBench, a benchmark that enables a systematic and reproducible evaluation of these library hallucinations. Our findings underscore the fragility of LLMs to natural prompt variation and highlight the urgent need for safeguards against library-related hallucinations and their downstream risks.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper measures high library hallucination rates under realistic prompt variations like misspellings and introduces LibHalluBench, but the causal attribution to those variations rests on shaky ground without clear baselines.

read the letter

The main thing to know is that this paper runs an empirical study across seven LLMs showing that small prompt changes, such as one-character misspellings or time references, produce library hallucinations at rates up to 26 percent, 85 percent, and 99 percent in some cases, then uses the worst cases to seed a new benchmark called LibHalluBench for future testing of invalid imports and invalid member calls. The focus on everyday developer-style mistakes rather than pure adversarial prompts is a useful angle, and splitting the analysis into library-name versus library-member hallucinations gives a clearer picture of where the problems occur. The benchmark itself is a concrete output that others could adopt for reproducible checks on code-generation tools. That part of the work is straightforward and addresses a real supply-chain risk from things like slopsquatting. The soft spot is the missing comparison to matched correct prompts. The claims use language about variations triggering the hallucinations, yet the description does not show that the same tasks were run without the misspellings or fabricated names to establish a baseline rate. Without that, the percentages could simply reflect how often these models hallucinate on the chosen tasks in general. Sample sizes, exact prompting templates, verification method for hallucinations, and any statistical checks are also not visible in the abstract, which leaves the quantitative findings hard to assess fully. If the full paper supplies those controls and details, the central numbers become more convincing; otherwise the attribution stays tentative. This is the sort of paper that would interest people working on LLM code assistants, software security, or empirical studies of AI tools. Readers who want numbers on a practical failure mode and a starting benchmark would get something out of it. I would send it for peer review. The topic is timely, the benchmark is new, and the empirical framing is reasonable, even though the methods section will need expansion and the baseline comparison will need to be added or clarified before it can stand on its own.

Referee Report

1 major / 2 minor

Summary. The manuscript presents the first systematic empirical study of library hallucinations (invalid imports and invalid member calls) in code generated by seven LLMs. It examines the influence of realistic developer prompt variations, including one-character misspellings, fabricated library names, and time-based prompts, reports quantitative hallucination rates (up to 26%, 99%, and 85% respectively), and introduces LibHalluBench as a benchmark derived from the highest-risk prompts identified.

Significance. If the central measurements are shown to be robust, the work is significant because it quantifies how common, low-effort prompt variations can produce high rates of library hallucinations with downstream security implications (e.g., slopsquatting). The introduction of LibHalluBench is a constructive contribution that could support reproducible follow-on evaluation in the LLM-for-code literature.

major comments (1)

[Section 3 and Section 4] Section 3 (Methodology) and Section 4 (Results): the central claims attribute specific hallucination rates to the tested prompt variations (e.g., 'one-character misspellings trigger hallucinations in up to 26% of tasks'). However, the reported experiments do not include or reference paired baseline measurements on matched prompts that use correct spellings and valid library names. Without these controls it is not possible to determine whether the observed rates exceed the models' baseline hallucination propensity on the chosen tasks, weakening the causal language of 'trigger' and 'influence'.

minor comments (2)

[Abstract] Abstract: the phrase 'up to 26%' (and similar maxima) should be accompanied by the specific model, task count, and prompt template that produced the maximum so readers can assess the scope of the reported effect.
[Section 3] The manuscript would benefit from an explicit statement of the total number of prompts, tasks, and generations per condition, together with any statistical tests or confidence intervals used to support the reported percentages.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their positive assessment of the work's significance and for this constructive comment on experimental controls. We address the point below and have revised the manuscript to strengthen the presentation of results.

read point-by-point responses

Referee: [Section 3 and Section 4] Section 3 (Methodology) and Section 4 (Results): the central claims attribute specific hallucination rates to the tested prompt variations (e.g., 'one-character misspellings trigger hallucinations in up to 26% of tasks'). However, the reported experiments do not include or reference paired baseline measurements on matched prompts that use correct spellings and valid library names. Without these controls it is not possible to determine whether the observed rates exceed the models' baseline hallucination propensity on the chosen tasks, weakening the causal language of 'trigger' and 'influence'.

Authors: We agree that explicit paired baselines on matched prompts with correct spellings and valid library names would allow clearer isolation of the incremental effect of the variations. Our original design emphasized realistic developer prompt conditions rather than exhaustive controls, but we acknowledge this limits strong causal attribution. In the revised manuscript we have added these baseline conditions to Section 3 and report comparative hallucination rates in Section 4, showing that rates under the varied prompts are substantially higher than the matched correct-prompt baselines. We have updated the abstract, results, and discussion to frame the findings in terms of relative increases rather than absolute triggering, while retaining the quantitative rates observed under each condition. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical measurement study with direct experimental outcomes

full rationale

This paper conducts an empirical study testing library hallucinations across seven LLMs under controlled prompt variations including misspellings, fabricated names, and time-based prompts. The central claims report observed hallucination rates (e.g., up to 26% for one-character misspellings) as direct results from the experiments rather than any mathematical derivation, fitted parameters renamed as predictions, or load-bearing self-citations. No equations or first-principles chains are present that reduce outputs to inputs by construction; LibHalluBench is introduced as a benchmark grounded in the highest-risk prompts identified experimentally. The analysis is self-contained against external benchmarks with no reduction to prior author work or ansatz smuggling.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is an empirical study of LLM behavior under varied prompts. No mathematical derivations, fitted parameters, or new postulated entities are introduced beyond the creation of the evaluation benchmark.

pith-pipeline@v0.9.0 · 5773 in / 1154 out tokens · 65270 ms · 2026-05-21T22:27:21.690568+00:00 · methodology

discussion (0)

Forward citations

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