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REVIEW 4 major objections 6 minor 78 references

USB: A Comprehensive and Unified Safety Evaluation Benchmark for Multimodal Large Language Models

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The paper claims that a single benchmark grid crossing 61 risk categories with four image-text combinations can replace the patchwork of existing MLLM safety tests, in English and Chinese, for both vulnerability and oversensitivity.

desk verdict The dataset and taxonomy are a real contribution; the empirical rankings rest on an unvalidated GPT-4o judge, so treat them as provisional. read the letter →

arxiv 2505.23793 v1 pith:EPX5XKZF submitted 2025-05-26 cs.CR cs.AI

Baolin Zheng , Guanlin Chen , Hongqiong Zhong , Qingyang Teng , Yingshui Tan , Zhendong Liu , Weixun Wang , Jiaheng Liu
show 7 more authors
Jian Yang Huiyun Jing Jincheng Wei Wenbo Su Xiaoyong Zhu Bo Zheng Kaifu Zhang
This is my paper
classification cs.CRcs.AI
keywords MLLMsafetyevaluationbenchmarkattacksuccessrateoversensitivitymodalitycombinationsrisktaxonomydatasynthesispipelinealignment
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper claims that existing safety benchmarks for multimodal large language models are too narrow to be trusted: most test only text-based risk paired with harmless images, skip cross-modal risks, and contain easy questions that inflate safety scores. To fix this, it introduces USB-SafeBench, built on a three-level taxonomy of 61 risk sub-categories crossed with four image-text risk combinations, in English and Chinese, testing both vulnerability (attack success rate) and oversensitivity (refusal rate on harmless inputs). The paper argues that this reaches 98.3% coverage of the 244 category-modality cells, whereas even the union of the collected open-source benchmarks reaches only about 60%, so a single benchmark can replace assembling many. Evaluations on 17 models show that no model is both safe and non-refusing, and that visual-only and cross-modal risks defeat even the strongest commercial models. If true, this gives the field a common measuring stick for MLLM safety and a concrete map of where alignment fails.

What carries the argument

The load-bearing object is the modality composition matrix, a $2 \times 2$ crossing of image risk (risky vs safe) with text risk (risky vs safe), giving the four configurations RIRT, RIST, SIRT, and SIST. This matrix defines what the paper means by 'comprehensive': coverage is the fraction of the $61 \times 4 = 244$ cells that contain more than twenty usable samples after filtering. To fill the empty cells, a data synthesis pipeline generates risk scenarios with auxiliary models, renders images with text-to-image diffusion models, pairs them with risky or safe questions, filters candidate samples through ten open-source MLLMs, iteratively rewrites failed queries to get past refusals, and finishes with dual human annotation under a cross-assessment protocol. On top of this matrix sit the two metrics that carry the empirical argument, the attack success rate $\mathrm{ASR}=\frac{1}{N_h}\sum_{i=1}^{N_h} f_h(i)$ and the average refusal rate $\mathrm{ARR}=\frac{1}{N_r}\sum_{j=1}^{N_r} f_r(j)$, which measure the two failure directions separately.

What would settle it

Take a stratified sample of model responses spanning all 17 models, the four modality combinations, and both risky and safe queries; have independent human annotators apply the paper's own three-level rubric (safe with refusal, safe with warning, unsafe) and its refusal rubric from Appendices E.6 and E.7, and compute agreement with GPT-4o's labels. If agreement is low, or if disagreement concentrates in particular risk categories or modality combinations — for instance if GPT-4o systematically marks cautious refusals as safe or misses subtly unsafe answers — then the reported ASR/ARR values and the model rankings would not survive re-scoring, even though the dataset itself would remain usable.

Watch

Extended reading notes

Core claim

On its own terms, the paper's central claim is that MLLM safety should be measured as a grid, not a list: every risk category should be tested in all four modality combinations — risky image with risky text (RIRT), risky image with safe text (RIST), safe image with risky text (SIRT), and safe image with safe text (SIST) — and both directions of failure (answering harmful queries, and refusing harmless ones) should be recorded at once. USB-SafeBench is the claimed embodiment: 61 tertiary risk categories under three primary topics (public safety, ethical safety, national safety), four modality combinations per category, English and Chinese, with 14,126 base samples and 3,935 hard samples. The paper reports 98.3% coverage of the 244 category-modality cells after quality filtering, against roughly 59.8% for the union of existing datasets, and an average attack success rate of 53.25% on the base set and 72.71% on the hard set across ten models, showing USB is substantially harder as well as broader. The empirical payload is that text-only safety alignment is misleadingly flattering: models are far more attackable when the risk lives in the image alone (RIST) or emerges from the image-text combination (SIST), and no evaluated model simultaneously achieves a low attack success rate and a low refusal rate.

Load-bearing premise

Every number in the paper — every attack success rate, refusal rate, model ranking, and trade-off claim — is produced by GPT-4o acting as the judge of whether a model output is unsafe or over-refused, and the paper supplies no comparison of that judge against human raters, so a systematic bias in the judge would shift all the empirical conclusions.

Editorial extensions

If this is right

  • Safety results from older text-centric benchmarks need re-reading: models reported as safe look substantially weaker once the image carries the risk, so scores should be reported per modality combination rather than as one number.
  • Evaluators can replace a patchwork of datasets with one suite: USB-Base gives a broad baseline and USB-Hard (the 15 hardest samples per cell) differentiates robust models while preserving the same relative ranking.
  • Model developers should treat the safety-helpfulness trade-off as a two-axis constraint: optimizing attack success rate alone is gameable by refusing everything, so releases should report the refusal rate alongside it.
  • The synthesis pipeline offers a template for generating hard adversarial samples in under-covered risk categories without manual red-teaming, which could be reused for new risk categories or new modalities.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The 98.3% coverage figure counts a cell as covered only if it has more than twenty samples; re-auditing the grid at higher sample-size thresholds would thin it, so fine-grained per-category conclusions need per-cell size checks.
  • The paper's own appendix notes that politically sensitive national-security cases and video content were excluded, so the modality matrix's generality to the highest-severity cases and to dynamic visual input is untested; carrying the same risky/safe crossing to video is a direct next step.
  • Because every ASR and ARR label comes from the GPT-4o judge, the model rankings read as conditional on that judge's standards; an obvious validation is human re-scoring of a stratified sample, and until that exists the dataset itself and the rankings should be treated separately.
  • The strong linear correlation between USB-Base and USB-Hard, with USB-Hard amplifying gaps, suggests the hard set could double as a cheap screening stage in a two-tier evaluation pipeline, with the base set reserved for detailed diagnostics.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper introduces USB-SafeBench, a large-scale multimodal safety benchmark covering 61 risk subcategories, 4 image-text modality combinations (RIRT, SIRT, RIST, SIST), both English and Chinese, and both vulnerability (ASR) and oversensitivity (ARR). The dataset combines curated samples from 13 existing benchmarks with synthetic data generated through a pipeline that uses GPT-4o/Gemini-1.5-Pro for scenario and query generation and Stable-Diffusion-3.5/Flux for image synthesis. The final benchmark is split into USB-Base (14,126 samples) and USB-Hard (3,935 samples). The authors evaluate 17 MLLMs (5 closed-source, 12 open-source) and report ASR/ARR values, a safety-refusal trade-off analysis, a modality-combination analysis, a model-size analysis, and a comparison between USB-Base and USB-Hard. The central empirical claims are that USB achieves 98.3% coverage of the category-modality space, yields substantially higher ASR than existing benchmarks, reveals that models are more vulnerable to visual-only and cross-modal risks, and produces consistent relative model rankings across difficulty levels.

Significance. If validated, this would be a genuinely valuable contribution: the first benchmark to cover 61 risk subcategories across 4 modality combinations, 2 languages, and 2 safety dimensions at this scale, with a transparent construction pipeline and public release. The taxonomy is detailed, the coverage analysis of existing benchmarks is useful, and the simultaneous treatment of vulnerability and oversensitivity is a step forward. The dataset artifact and its supporting human annotation for data attributes are strengths. However, the empirical conclusions are conditional on an unvalidated GPT-4o judge and on selection procedures that introduce circularity; these issues must be resolved before the benchmark can support the paper's claim of enabling 'a trustworthy safety assessment'.

major comments (4)
  1. [Section 3.1, Appendix E.6/E.7] All ASR and ARR numbers in Table 2, Figures 6 and 8, and the modality-combination analysis in §3.2 are produced by a single GPT-4o judge using the prompts in Appendix E.6 (vulnerability) and E.7 (oversensitivity). No human agreement or judge-vs-human agreement is reported; Appendix C describes human annotation only for data attributes (risk category, modality, style), not for the safety labels that generate ASR/ARR. Because GPT-4o is itself one of the 17 evaluated models, the judge may systematically recognize its own refusal style as 'safe', biasing relative rankings. The paper must report judge-human agreement statistics (e.g., Cohen's kappa on a representative sample) or replace the judge with a validated, independently verified classifier.
  2. [Section 2.4 and Section 2.3] USB-Hard is constructed by selecting the 15 highest-ASR samples per viewpoint from the 10 filter MLLMs (Section 2.4), and USB-Base is filtered to keep only queries that successfully attack at least one of those 10 models (Section 2.3, Data Quality Test). Since these filter models are also among the 17 evaluated models in Table 2, the finding in §3.2 and Figure 8 that ASR on USB-Hard is higher for every model, and that relative rankings are consistent, is at least partly forced by construction for the filter models. The paper should either exclude the filter models from the evaluation set, construct USB-Hard using a disjoint model set, or explicitly separate the analysis for filter and non-filter models before drawing the 'consistent rankings' conclusion.
  3. [Table 2 and Section 3.2] The reported ASR/ARR values are single-run point estimates with no confidence intervals, standard errors, or significance tests. Several model differences are small (e.g., Qwen-VL-Max 56.87% vs GLM-4V-9B 56.73% vs Qwen2-VL-7B 55.33% in Table 2), and the 'consistent relative rankings' conclusion in Figure 8 is based on visual inspection of a scatter plot without any statistical measure. The paper should provide multiple runs or bootstrap confidence intervals and report a rank-correlation statistic (e.g., Spearman's rho) with a confidence interval for the Base-vs-Hard comparison.
  4. [Table 1 and Section 2.2] The coverage metric is defined as the percentage of cells with more than 20 samples out of 244 possible category-modality combinations. This threshold is arbitrary, and because USB was generated specifically to fill every cell while existing benchmarks were not constructed with this cell-filling objective, the 98.3% vs 59.8% comparison is not an apples-to-apples measure of benchmark quality. The paper should report coverage under multiple thresholds (e.g., 10, 20, 50 samples) and discuss the sensitivity of the comparison.
minor comments (6)
  1. [Abstract] The word 'coverge' should be 'coverage', and the phrase 'modal risk combinations' is grammatically awkward; consider 'risk-modality combinations'.
  2. [Section 3.1 and Section 5] Typos: 'Appedix' should be 'Appendix' in §3.1; 'commerical' should be 'commercial' in §5; 'choosed' should be 'chose' in §3.1.
  3. [Section 2.4] The sentence 'Figure 4 illustrates the of our synthetic data' is missing a word; it should be 'illustrates examples of our synthetic data'.
  4. [Appendix E.5, E.8, E.4] Typos in headings/text: 'Iterattion' should be 'Iteration' (E.5), 'calssification' should be 'classification' (E.8), 'instrutions' should be 'instructions' (E.4).
  5. [Section 2.3] The paper refers to '10 mainstream open-source MLLMs' used as filter models but never lists them. Specify the exact model names and versions for reproducibility, especially because these models also appear in the main evaluation.
  6. [Table 1 note (i)] Clarify whether the coverage calculation applies the same 'low-quality data filtering' to the existing benchmarks as to USB; if not, the comparison may be unfair to benchmarks that were not filtered in the same way.

Circularity Check

1 steps flagged · score 6.0 of 10

USB-Hard's consistently higher ASR is partially an artifact of selecting its samples using ASR on the same 10 MLLMs; the benchmark artifact itself is otherwise self-contained, while the GPT-4o judge issue is a validity risk rather than derivation-level circularity.

  1. fitted input called prediction [Section 2.4 (Data Selection and Statistics), Section 3.2 (USB-Hard), Table 1 note (ii)]
    "The USB-Hard dataset, in contrast, was curated differently: we selected the 15 samples with the highest attack success rates from each of the 244 viewpoints (61×4 combinations). ... Notably, all data points lie above the diagonal line, indicating that the Attack Success Rates (ASR) on USB-Hard are consistently higher than those on USB-Base across all 17 MLLMs."

    USB-Hard's hardness is defined by the same model set that is later evaluated. Section 2.3 states that 10 MLLMs are used to filter data, keeping only 'queries capable of successfully attacking any of these MLLMs,' and Table 1 note (ii) says the reported ASR is 'the mean across the 10 MLLMs depicted in Figure 1.' USB-Hard then takes the 15 highest-ASR samples per viewpoint, so for those 10 models the finding that USB-Hard raises ASR relative to the randomly sampled USB-Base is a direct consequence of selection on the outcome variable, not an independent empirical result.

full rationale

The core benchmark artifact—taxonomy, coverage grid, data synthesis pipeline, and bilingual/multimodal composition—is not circular: the 61-category × 4-modality grid is the paper's own design, and filling it with synthetic and curated data is a constructive contribution. The coverage metric is defined relative to that grid and is reported transparently. No load-bearing self-citation chain or imported uniqueness theorem is present; references to the authors' prior work are peripheral. The main circularity is USB-Hard: its construction selects samples using ASR on a fixed 10-model set, and the paper then reports that ASR is higher on USB-Hard for those very models as if it were a discovered property. This is a partial 'fitted input called prediction' pattern and justifies a score of 6. The unvalidated GPT-4o judge, which also evaluates GPT-4o itself, is a serious validity and independence concern but does not reduce to a definitional equivalence or fitted-parameter rename under the enumerated circularity kinds, so it is noted here rather than scored as an additional circular step.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The central claims rest on several design choices and external assumptions. The taxonomy (61 categories), the modality matrix, the sample counts, and the hard-set size are hand-selected design parameters. The evaluation assumes the GPT-4o judge is accurate, that synthetic data generation is faithful to intended risk scenarios, and that attacking any of 10 MLLMs is a sufficient quality criterion. No new physical or mathematical entities are introduced.

free parameters (5)
  • Coverage threshold = >20 samples per (category, modality) cell
    Chosen to define 'well-represented' scenarios in Table 1; determines the reported coverage percentages for all benchmarks.
  • USB-Base cell size = 60 samples per cell (target)
    Selected as a trade-off between evaluation reliability and cost; with 244 cells it yields roughly 14k base samples.
  • USB-Hard selection count = 15 samples per cell
    Number of highest-ASR samples per viewpoint chosen to build the hard set; controls USB-Hard difficulty.
  • Quality filter threshold = attack success on at least 1 of 10 MLLMs
    A query attacking none of the 10 filter models is deemed low-quality and removed; operationalizes 'harmful' and inflates ASR relative to an unfiltered benchmark.
  • Risk taxonomy granularity = 3 primary / 16 secondary / 61 tertiary categories
    Hand-constructed hierarchy (Figure 2) meant to cover academic and industrial risk categories; completeness is assumed, not proven.
assumptions (5)
  • domain assumption GPT-4o can reliably judge whether a model response is safe or unsafe.
    Evaluation protocol in Section 3.1 and Appendix E.6/E.7 uses GPT-4o as the judge with no human-agreement validation; all ASR/ARR numbers depend on this assumption.
  • domain assumption Text-to-image models (Stable-Diffusion-3.5-Large, Flux) generate images that faithfully depict the intended risky scenarios.
    Multimodal Data Synthesis in Section 2.3 relies on T2I models producing informative and risky images from refined prompts.
  • domain assumption The 61-category taxonomy is the correct and complete space of MLLM safety risks.
    Section 2.1 introduces the three-tier taxonomy as the reference frame; coverage of all benchmarks is measured against it.
  • ad hoc to paper A query that successfully attacks any one of the 10 filter MLLMs is a valid harmful query.
    Data Quality Test in Section 2.2 uses this criterion to decide which synthetic samples to keep; it is a pragmatic but model-relative standard.
  • domain assumption The scenarios and queries generated by GPT-4o and Gemini-1.5-Pro are diverse and representative of real-world risky user inputs.
    Risk Scenario Generation in Section 2.3 and Appendix E.1/E.2 rely on these models for content coverage; contamination is not analyzed.

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Cite this review

Pith. "Pith review of USB: A Comprehensive and Unified Safety Evaluation Benchmark for Multimodal Large Language Models." pith.science (2026). https://pith.science/paper/EPX5XKZF

@misc{pith2026250523793,
  author       = {Pith},
  title        = {Pith review of: USB: A Comprehensive and Unified Safety Evaluation Benchmark for Multimodal Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EPX5XKZF}},
  note         = {Machine review of arXiv:2505.23793}
}
read the original abstract

Despite their remarkable achievements and widespread adoption, Multimodal Large Language Models (MLLMs) have revealed significant security vulnerabilities, highlighting the urgent need for robust safety evaluation benchmarks. Existing MLLM safety benchmarks, however, fall short in terms of data quality and coverge, and modal risk combinations, resulting in inflated and contradictory evaluation results, which hinders the discovery and governance of security concerns. Besides, we argue that vulnerabilities to harmful queries and oversensitivity to harmless ones should be considered simultaneously in MLLMs safety evaluation, whereas these were previously considered separately. In this paper, to address these shortcomings, we introduce Unified Safety Benchmarks (USB), which is one of the most comprehensive evaluation benchmarks in MLLM safety. Our benchmark features high-quality queries, extensive risk categories, comprehensive modal combinations, and encompasses both vulnerability and oversensitivity evaluations. From the perspective of two key dimensions: risk categories and modality combinations, we demonstrate that the available benchmarks -- even the union of the vast majority of them -- are far from being truly comprehensive. To bridge this gap, we design a sophisticated data synthesis pipeline that generates extensive, high-quality complementary data addressing previously unexplored aspects. By combining open-source datasets with our synthetic data, our benchmark provides 4 distinct modality combinations for each of the 61 risk sub-categories, covering both English and Chinese across both vulnerability and oversensitivity dimensions.

Figures

Figures reproduced from arXiv: 2505.23793 by the authors.

Figure 1
Figure 1. Attack successful rate distributions across different open source datasets against 10 [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. A hierarchical three-level taxonomy for vulnerability evaluation in our USB-SafeBench, [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. An overview of USB-SafeBench framework, including components of data collection and [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Examples of our synthetic multimodal data in our USB-SafeBench, including three-level [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: The detailed statistics of data source in [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 7
Figure 7. Figure 7: ASR across 16 risk subcategories 0.0 0.2 0.4 0.6 0.8 1.0 ASR (USB-Base) 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 ASR (USB-Hard) Claude3.5-sonnet2 Gemini-1.5-pro Gemini-2.0-flash GLM-4V-9B GPT-4o InternVL2-40B InternVL2-8B LLAVA-v1.5-13B LLAVA-v1.5-7B MiniCPM-V 2.6 Q…
Figure 6
Figure 6. Figure 6: Safety-refusal trade-off. Claude3.5-Sonnet2, shows a notable increase in ASR under RIST and SIST scenarios, a vul￾nerability pattern also evident in GPT-4o and Gemini-1.5-Pro. Open-source models are espe￾cially poor at detecting hidden threats in RIST/- SIST combinatio…
Figure 9
Figure 9. Figure 9: Radar Visualization of ASR against 17 MLLMs across 61 tertiary risk categories. [PITH_FULL_IMAGE:figures/full_fig_p009_9.png]

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.