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arxiv: 2603.04459 · v3 · pith:WE4OYEKYnew · submitted 2026-03-03 · 💻 cs.CR · cs.AI· cs.SE

Benchmark of Benchmarks: Unpacking Influence and Code Repository Quality in LLM Safety Benchmarks

Junjie Chu , Xinyue Shen , Ye Leng , Michael Backes , Yun Shen , Yang Zhang This is my paper

Pith reviewed 2026-05-21 12:05 UTC · model grok-4.3

classification 💻 cs.CR cs.AIcs.SE
keywords LLM safetybenchmarkscode qualityrunnabilityadoption factorsethical considerationsrepository analysisjailbreak
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0 comments X

The pith

LLM safety benchmark adoption tracks author prominence and basic runnability rather than code quality or ethical standards.

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

The paper conducts a measurement study on 31 LLM safety benchmarks covering areas like prompt injection and jailbreaks, comparing them to 382 non-benchmark papers. It shows that most repositories fail to run out of the box, lack reliable setup instructions, and rarely address the ethical risks of publishing harmful examples. Adoption by the community links more closely to how prominent the authors are and whether the code executes at all than to measurable code quality or documentation standards. These shortfalls have stayed consistent over time and raise questions about the dependability of safety evaluations built on such tools.

Core claim

Only 39 percent of the benchmark repositories run without any modification, 16 percent supply flawless installation guides, and just 6 percent include ethical considerations even though they contain potentially harmful content. Adoption correlates with author prominence and code runnability but shows no relation to static code quality metrics such as Pylint scores or maintainability. These patterns hold across the study period without improvement, and some repositories make successful attack responses publicly available without warnings or controls.

What carries the argument

Systematic measurement combining automated static analysis, over 220 person-hours of human runnability testing, and bibliometric analysis of adoption patterns across 31 benchmarks versus a control group of 382 papers.

If this is right

  • Downstream safety evaluations across papers may not be comparable when each requires ad-hoc code changes to run the benchmarks.
  • Repositories that expose unfiltered harmful content without warnings or access controls can serve as open resources for attacks.
  • The community does not reward higher coding standards or documentation when choosing which benchmarks to adopt.
  • Persistent deficiencies suggest that reliability and safety concerns in evaluations will continue unless practices change.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Benchmark creators could gain faster adoption by prioritizing immediate runnability and visible author networks over internal code polish.
  • A shared quality checklist might shift selection incentives if later studies show it predicts higher uptake.
  • Similar gaps in repository standards likely appear in other evaluation-heavy areas such as general LLM capability testing.

Load-bearing premise

The 31 selected benchmarks and 382 control papers form a representative sample of LLM safety literature without bias from how they were identified.

What would settle it

Re-running the full static analysis, runnability tests, and adoption correlation on an independently chosen larger set of LLM safety benchmarks to check whether the reported percentages and lack of quality correlation persist.

Figures

Figures reproduced from arXiv: 2603.04459 by Junjie Chu, Michael Backes, Xinyue Shen, Yang Zhang, Ye Leng, Yun Shen.

Figure 1
Figure 1. Figure 1: Data collection pipeline [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Human-based evaluation results of code quality. [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Human-based evaluation results of supplementary materials. Repositories without code or unavailable ones are labeled “Not [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: PRISMA-style flow diagram for benchmark selection. [PITH_FULL_IMAGE:figures/full_fig_p014_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Average values of five influence-related metrics on benchmark and non-benchmark papers. [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Distribution of the scientific fields that the LLM safety papers influence. [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Average values of eight metrics related to the code repository quality on benchmark and non-benchmark papers. We have [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Average time to successfully run the example scripts [PITH_FULL_IMAGE:figures/full_fig_p020_8.png] view at source ↗
Figure 10
Figure 10. Figure 10: GitHub repository availability proportions. [PITH_FULL_IMAGE:figures/full_fig_p020_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: A typical example of the general pattern we identify. [PITH_FULL_IMAGE:figures/full_fig_p020_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Spearman correlation ρ matrix of the influence met￾rics (those with p ≥ 0.05 are omitted). 20 [PITH_FULL_IMAGE:figures/full_fig_p020_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Spearman correlation ρ matrices between the influence metrics and the potential quantitative factors. The unadjusted p-values on the left can be interpreted exploratively. Pylint Score Cyclomatic Complexity Maintainability Index Number of Static Errors Reply Time (Hours) Last Commit Time (Days) Number of Commits Commit Frequency Citation Density Citation Count GitHub Star Density GitHub Star Count Scienti… view at source ↗
Figure 14
Figure 14. Figure 14: Spearman correlation ρ matrices between the influence metrics and the code repository quality metrics. The unadjusted p-values on the left can be interpreted exploratively. Author Number Institution Number Area Number Author H-Index (Top-1) Author Citation Count (Top-1) Insitution CSRankings (Top-1) Insitution ARWU (Top-1) Search Appearance Frequency Pylint Score Cyclomatic Complexity Maintainability Inde… view at source ↗
Figure 15
Figure 15. Figure 15: Spearman correlation ρ matrices between the code repository quality metrics and the potential quantitative factors. The unadjusted p-values on the left can be interpreted exploratively. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Box plots of citation density by group. The red dashed lines represent the means. [PITH_FULL_IMAGE:figures/full_fig_p022_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Box plots of citation density and the status of extra modifications and runnable code. The red dashed lines represent the [PITH_FULL_IMAGE:figures/full_fig_p022_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Box plots of citation density and various potential qualitative factors. The red dashed lines in the box plots represent the [PITH_FULL_IMAGE:figures/full_fig_p022_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Box plots of Pylint score and various potential qualitative factors. The red dashed lines in the box plots represent the mean [PITH_FULL_IMAGE:figures/full_fig_p022_19.png] view at source ↗
read the original abstract

The rapid expansion of research in LLM safety presents challenges in tracking advancements, making benchmarks important evaluation infrastructures for identifying key trends and facilitating systematic comparisons. Yet no systematic assessment exists of their code quality and runnability, nor of what factors are associated with the community's adoption of certain benchmarks over others. To address this gap, we conduct a systematic measurement study of 31 LLM safety benchmarks (covering prompt injection, jailbreak, and hallucination) with 382 non-benchmark papers as a control group, combining automated static analysis, human runnability testing (220+ person-hours), and bibliometric analysis. We find that only 39\% of benchmark repositories can run without modification, only 16\% provide flawless installation guides, and a mere 6\% include ethical considerations despite containing potentially harmful content. These deficiencies persist across the study period with no significant improvement. Analyzing adoption factors, we find that benchmark adoption correlates with author prominence and code runnability, but not with code quality standards such as Pylint score and maintainability, suggesting that the community's benchmark selection does not reward higher coding standards. Based on these results, we identify potential safety and reliability concerns. Some safety benchmark repositories openly expose harmful content, such as successful jailbreak responses, without any ethical warning or access control, effectively serving as unguarded attack resources. Furthermore, when benchmarks require ad-hoc modifications to run, downstream safety evaluations across different papers may not be comparable. We present case studies illustrating these concrete consequences and propose a targeted checklist to help benchmark contributors improve code quality, documentation, and ethical practices.

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.

Referee Report

2 major / 2 minor

Summary. The paper conducts a systematic measurement study of 31 LLM safety benchmarks (prompt injection, jailbreak, hallucination) against 382 non-benchmark control papers. It combines automated static analysis, 220+ person-hours of manual runnability testing, and bibliometric analysis to quantify code quality and adoption factors. Key results: 39% of repositories run without modification, 16% have flawless installation guides, and only 6% include ethical notes. Adoption correlates with author prominence and runnability but shows no correlation with code-quality metrics such as Pylint score or maintainability. The work identifies safety risks from unguarded harmful content and proposes a contributor checklist.

Significance. If the sample is representative, the study supplies concrete, reproducible evidence on reproducibility failures and ethical gaps in LLM safety benchmarks, backed by extensive manual verification and bibliometric controls. The finding that adoption tracks prominence and runnability rather than quality standards, together with the explicit checklist, offers actionable guidance for the community. The combination of automated tools, large-scale human testing, and falsifiable quantitative claims (percentages, correlations) is a clear strength.

major comments (2)
  1. [§3] §3 (Methodology): The paper provides no search strategy, databases, keywords, date range, inclusion/exclusion rules, or justification for selecting the 31 benchmarks and 382 control papers. This detail is load-bearing for the central claim that adoption correlates with prominence and runnability rather than quality metrics, because an uncharacterized sample could mechanically produce the reported pattern through selection artifacts.
  2. [§4.3] §4.3 (Adoption analysis): The reported correlations (e.g., with author prominence and runnability) are presented without sensitivity checks for alternative sampling frames or controls for publication venue; if the 31 benchmarks over-represent prominent authors, the absence of correlation with Pylint/maintainability cannot be interpreted as a community-wide preference.
minor comments (2)
  1. [§2] §2: A brief comparison table of prior benchmark surveys would help situate the novelty of the 220+ person-hour manual evaluation.
  2. [Table 2] Table 2: Define all column abbreviations (e.g., “EA”, “Pylint”) in the caption so the table is self-contained.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. We address each major comment below and outline the revisions we will make to improve the manuscript's clarity and robustness.

read point-by-point responses

  1. Referee: [§3] §3 (Methodology): The paper provides no search strategy, databases, keywords, date range, inclusion/exclusion rules, or justification for selecting the 31 benchmarks and 382 control papers. This detail is load-bearing for the central claim that adoption correlates with prominence and runnability rather than quality metrics, because an uncharacterized sample could mechanically produce the reported pattern through selection artifacts.

    Authors: We agree that a more explicit description of the sampling process is necessary to support the reproducibility and validity of our findings. In the revised manuscript we will add a dedicated subsection to §3 that fully documents the search strategy. This will specify the databases queried (arXiv, ACL Anthology, and Google Scholar), the precise keywords and Boolean combinations used, the date range (2022–2024), the inclusion criteria (publicly available code repositories for prompt-injection, jailbreak, or hallucination benchmarks), the exclusion criteria (non-code-based evaluations, non-English papers, or works without GitHub links), and the rationale for arriving at the final counts of 31 benchmarks and 382 control papers. These additions will directly address the possibility of selection artifacts and strengthen the interpretation of the reported correlations. revision: yes

  2. Referee: [§4.3] §4.3 (Adoption analysis): The reported correlations (e.g., with author prominence and runnability) are presented without sensitivity checks for alternative sampling frames or controls for publication venue; if the 31 benchmarks over-represent prominent authors, the absence of correlation with Pylint/maintainability cannot be interpreted as a community-wide preference.

    Authors: We acknowledge the value of additional robustness checks. In the revision we will augment §4.3 with sensitivity analyses that (i) restrict the sample to benchmarks published in top-tier venues, (ii) include publication venue as a control variable in the regression models, and (iii) repeat the correlation tests on a venue-matched subset of the control group. These checks will help demonstrate that the observed pattern—adoption tracking prominence and runnability rather than static code-quality metrics—holds under alternative sampling frames and is not an artifact of over-representation of prominent authors. revision: yes

Circularity Check

0 steps flagged

No significant circularity: empirical measurement study with direct observations

full rationale

This paper performs a systematic measurement study by selecting 31 LLM safety benchmarks and 382 control papers, then applying automated static analysis, human runnability testing, and bibliometric analysis to measure code quality, runnability, and adoption correlations. No derivations, equations, or first-principles predictions exist that reduce to the paper's own inputs by construction. The reported correlations (adoption with prominence/runnability, none with Pylint/maintainability) are computed directly from the sampled data without fitted parameters renamed as predictions or self-citation chains that bear the central load. The study is self-contained against external benchmarks of repository quality and adoption metrics, with no self-definitional, uniqueness-imported, or ansatz-smuggled steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claims rest on operational definitions of 'runnable without modification', 'flawless installation guide', and 'ethical consideration' that are applied during human testing; these definitions are not derived from prior literature but introduced for the study.

axioms (1)
  • domain assumption The selected 31 benchmarks and 382 control papers adequately represent the broader LLM safety literature.
    Selection criteria and search strategy are not specified in the provided abstract.

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Forward citations

Cited by 1 Pith paper

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