Pith. sign in

REVIEW 4 major objections 6 minor 3 cited by

OrgAccess: A Benchmark for Role Based Access Control in Organization Scale LLMs

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

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Current LLMs cannot reliably enforce role-based access control in organizational settings.

desk verdict OrgAccess fills a real evaluation gap, but a self-contradictory seed example and missing human baselines mean the headline F1 numbers need a label audit before they fully carry weight. read the letter →

arxiv 2505.19165 v3 pith:ZBQGYJVR submitted 2025-05-25 cs.AI

Debdeep Sanyal , Umakanta Maharana , Yash Sinha , Hong Ming Tan , Shirish Karande , Mohan Kankanhalli , Murari Mandal This is my paper
classification cs.AI
keywords role-basedaccesscontrolLLMbenchmarkorganizationalhierarchypermissionreasoningrulefollowingcompositionalsyntheticdatasetenterpriseAI
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 argues that current large language models cannot reliably enforce role-based access control policies of the kind enterprises would depend on. To test this, it builds OrgAccess, a synthetic benchmark of 70,000 query-response pairs built from 40 expert-validated permission types arranged in easy, medium, and hard splits. Evaluating models across sizes and providers, the authors find consistent collapse as concurrent permissions grow: GPT-4.1, the strongest model tested, reaches only 0.27 F1 on the hard split. The central claim is that this reveals a fundamental deficit in compositional rule following, not a gap that model scaling closes.

What carries the argument

The load-bearing object is the OrgAccess dataset itself: 40 permission types grounded in NIST control families and refined through a structured multi-round Delphi expert process, then assembled into three difficulty splits. Easy items carry one permission, medium items carry three, and hard items carry five, with combinations chosen by experts to be realistic and conflict-prone. Each item is a user query plus a target decision among "full", "partial", and "rejected" with a rationale. The benchmark isolates rule-following by requiring the model to return the discrete decision from explicit permissions, and the design separates a baseline single-rule skill from the compositional skill of resolving concurrent permissions.

What would settle it

One concrete check is to take a random sample of hard-split items, have independent security professionals label them without seeing the generated labels, and measure agreement. If agreement is low, or if a published seed whose rationale contradicts its expected label is not an isolated typo, the F1 gap between models and labels is not a clean measure of RBAC reasoning.

Watch

Extended reading notes

Core claim

The central discovery is that LLMs are not inherently capable of strictly adhering to organizational access policies once several permissions must be considered together. On the benchmark, accuracy falls sharply from single-permission tasks to tasks with five concurrent permissions, and the same steep drop appears across model families. The failure is not random: models over-predict "partial" access, overlook individual permissions within a combination, and mishandle scope and conflict reasoning. Even a state-of-the-art model like GPT-4.1 earns only 0.27 F1 on the hardest split, which the authors take as evidence that the ability to compose and apply structured rules is largely missing from current systems.

Load-bearing premise

The whole measurement rests on the ground-truth labels being correct and unambiguous, so a low model score reflects a reasoning deficit rather than noisy or contradictory answers in the benchmark itself.

Editorial extensions

If this is right

  • Performance collapses consistently from easy to medium to hard across all tested models, so the difficulty gradient itself is a robust measure of compositional rule-following.
  • Scaling model size within current architectures does not close the gap: larger models improve on easy and medium splits, but hard-split performance stays near or below 0.3 F1.
  • The dominant "false partial" error means models tend to hedge toward partial access instead of committing to full or rejected, undermining their reliability as access gateways.
  • Error analysis shows scope, constraint, and conflict errors all rise sharply from easy to hard, pointing to a reasoning deficit rather than a surface formatting issue.
  • The benchmark can serve as a diagnostic tool for hierarchy-aware LLM development and for tracking progress beyond factual and STEM benchmarks.

Reading between the lines

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

  • A natural follow-up experiment is to hold the query fixed and vary only the permission tuple, which would isolate whether failures come from rule comprehension or from composing multiple rules.
  • The same design could be extended to temporal and stateful policies, such as expiring grants or session state, to test whether models track access over a dialogue rather than in a single turn.
  • If label noise is present, as the published "Temporary Contractor Access" seed suggests, even the reported F1 numbers could be pessimistic; re-scoring with a clean, adjudicated label set would sharpen the benchmark's conclusions.
  • The three-way decision format likely understates real enterprise needs, where the correct action is often an explanation or a partial fulfillment with redaction; a benchmark that scores those outputs would measure deployability more directly.
Share X Bluesky LinkedIn Reddit HN

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 OrgAccess, a synthetic benchmark for evaluating LLMs on role-based access control (RBAC) reasoning. It defines 40 expert-validated permission types and creates three splits (easy, medium, hard) with 40,000, 10,000, and 20,000 query–response pairs, respectively, using Mistral Small 3.1 for synthetic generation with guided sampling and post-hoc QA. The authors evaluate 16 (or, in one sentence, 35) LLMs of varying sizes and report that even GPT-4.1 achieves only an F1-score of 0.27 on the hard split, concluding that current LLMs are not capable of strictly adhering to organizational access policies. The error analysis attributes failures to categories such as constraint, scope, conflict, and false-partial errors.

Significance. If the benchmark's ground-truth labels are correct, OrgAccess addresses a real and underexplored evaluation gap: compositional rule-following and conflict resolution in enterprise RBAC settings, which standard factual and STEM benchmarks do not cover. The dataset is large, open-sourced, and grounded in NIST frameworks with expert review, and the three-tier difficulty design is a useful contribution. The paper also provides a detailed error taxonomy and per-category performance breakdown, which are valuable for future work. However, the significance hinges on the reliability of the synthetic labels; if label noise is substantial, the reported capability gap is not cleanly interpretable.

major comments (4)
  1. [Appendix A.4 and Section 2.2] The 'Temporary Contractor Access' seed in Appendix A.4 lists Expected Response: 'partial', but its own rationale concludes 'Therefore, the request must be rejected.' This is a direct internal contradiction in a hand-authored seed that Section 2.2 states was rigorously peer-reviewed. Because Section 2.3 uses these seeds as anchors for generating 70,000 synthetic examples, and Section 2.4 reports only a partial QA pass (750 hard-split and 84 medium-split rationales corrected, with no full label audit), the contradiction undermines confidence in the ground-truth labels. The authors should correct the seed, audit the affected data, and provide either a formal decision procedure for the full/partial/rejected distinction or an estimate of label noise (e.g., inter-annotator agreement). Without this, the reported F1 values, including the headline GPT-4.1 score of 0.27, may reflect label ambiguity rather than a reasoning deficit.
  2. [Section 3] The final paragraph of Section 3 states 'The evaluation of 35 state-of-the-art LLMs', which conflicts with the '16 LLMs' mentioned in the Introduction and the first sentence of Section 3, and with the 10 models in Table 1 and 7 in Table 2. Please reconcile the model count and provide a complete list of all evaluated models (e.g., in an appendix) so that the reported results are reproducible.
  3. [Sections 2.2/2.3 and Table 1] Mistral Small 3.1 was used to generate the synthetic queries and expected responses, and the same model (as Mistral-Small-3.1-24B) is also evaluated in Table 1. This introduces a circularity: the model may be advantaged or disadvantaged by having generated the data it is tested on. The authors should explicitly discuss this risk and, ideally, report results with this model excluded or with an independent verification of a sample of labels by a different model or human annotators.
  4. [Section 3 and Tables 1/2] No random or majority-class baseline is reported. The hard-split F1 scores (e.g., 0.27 for GPT-4.1) are actually below the random-chance F1 of approximately 0.33 for a balanced three-class problem, which is highly unusual and suggests that either the task is mis-calibrated or the labels contain substantial noise. Reporting random and majority baselines, and ideally human performance on a sample, is necessary to interpret the 'capability gap' claim and to confirm that the low scores are not an artifact of the benchmark design.
minor comments (6)
  1. [Table 3 caption] The word 'Erros' should be 'Errors'.
  2. [Section 3] In the paragraph beginning 'The observed performance plateau', 'adddress' should be 'address'.
  3. [Appendix A.2] The text references 'as reported in Section 5', but the paper has only four numbered sections; this should be Section 3.
  4. [Appendix A.3] There is an unfinished placeholder, 'Section [Refer to the Section where you introduced the 7 categories]', which should be replaced with the actual section reference.
  5. [Appendix A.4] The permission catalog inconsistently uses 'OrgAccess' and 'ORG-Benchmark'; unify the terminology.
  6. [Appendix A.4] There are formatting artifacts such as raw '\textbf' commands and garbled quotes (e.g., '˘20...˘2019') in several seeds; these should be cleaned up.

Circularity Check

1 steps flagged · score 2.0 of 10

Low-severity circularity: the only self-referential element is that Mistral Small 3.1 generates the ground-truth labels and is also evaluated against them; the headline GPT-4.1 result is independent, and the Appendix A.4 seed contradiction is a data-quality concern, not a by-construction reduction.

  1. other [Section 2.2 (data generation) and Table 1 (evaluation of Mistral-Small-3.1-24B)]
    "We employed Mistral Small 3.1 Mistral [2025] to generate 1000 synthetic data points for each of the 40 permission types, resulting in a total of 40,000 data points for the easy split."

    The same model family is also evaluated against those labels: Table 1 reports results for Mistral-Small-3.1-24B. For this model, the ground-truth expected responses were produced by the same generator, so its reported F1 is partly a self-consistency check with its own generation behavior rather than an independent measure of RBAC reasoning. This does not reduce the paper's central claim about GPT-4.1, because GPT-4.1 did not generate the labels; hence the circularity is limited and non-load-bearing.

full rationale

The paper's central claim is an empirical benchmark measurement, not a derivation from fitted parameters or a self-citation chain. No equation defines the target in terms of the inputs, and no fitted parameter is later reported as a prediction. The only true self-reference is that Mistral Small 3.1 generated the synthetic labels (Sections 2.2 and 2.3) and Mistral-Small-3.1-24B is also evaluated in Table 1, making that model's own score partially a self-consistency metric. This does not affect the independent GPT-4.1 and Gemini-2.5-Pro results that anchor the headline finding. A separate validity concern is the internally contradictory 'Temporary Contractor Access' seed in Appendix A.4, which lists Expected Response 'partial' while its rationale concludes 'Therefore, the request must be rejected'; this undermines the claim of unambiguous ground truth and could depress all models' F1 scores through label noise, but label inconsistency is a data-quality issue rather than a circular reduction. Overall, the benchmark evaluation is self-contained and externally checkable, so the circularity score is low.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

The central result is a measurement, not a derivation, so there are no fitting parameters in the mathematical sense. The ledger instead records the domain assumptions and post-hoc balancing choices that the benchmark's validity depends on.

free parameters (1)
  • Response-class balance in each split = approximately equal proportions of full/partial/rejected after replacement
    Section 2.4 adjusts data to remove response class skew; this post-hoc balancing sets the benchmark's marginal label distribution, which affects macro F1 and makes easy/medium/hard comparisons less directly interpretable.
assumptions (3)
  • domain assumption The 40 permission types and their semantics, selected via a Delphi process from NIST frameworks, faithfully represent real-world enterprise RBAC permissions.
    Load-bearing for the benchmark's validity; if the permissions are not representative, the measured failure rates do not transfer to practice. Section 2.1.
  • ad hoc to paper Mistral Small 3.1 generated synthetic queries and expected responses are a valid proxy for real employee queries and correct access decisions, after post-hoc corrections.
    The ground truth is LLM-generated; only a fraction was human-reviewed (750/20000 hard rationales corrected), so correctness depends on the generator's judgment. Sections 2.2-2.4.
  • ad hoc to paper The three-way decision space (full/partial/rejected) fully captures the range of correct responses to a permission-constrained query.
    Prompt forces models into one of three classes; real access decisions may include conditional grants, context-dependent responses, or requests for clarification. Section 3.

how reviews work

0 comments
Cite this review

Pith. "Pith review of OrgAccess: A Benchmark for Role Based Access Control in Organization Scale LLMs." pith.science (2026). https://pith.science/paper/ZBQGYJVR

@misc{pith2026250519165,
  author       = {Pith},
  title        = {Pith review of: OrgAccess: A Benchmark for Role Based Access Control in Organization Scale LLMs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZBQGYJVR}},
  note         = {Machine review of arXiv:2505.19165}
}
read the original abstract

Role-based access control (RBAC) and hierarchical structures are foundational to how information flows and decisions are made within virtually all organizations. As the potential of Large Language Models (LLMs) to serve as unified knowledge repositories and intelligent assistants in enterprise settings becomes increasingly apparent, a critical, yet under explored, challenge emerges: \textit{can these models reliably understand and operate within the complex, often nuanced, constraints imposed by organizational hierarchies and associated permissions?} Evaluating this crucial capability is inherently difficult due to the proprietary and sensitive nature of real-world corporate data and access control policies. We introduce a synthetic yet representative \textbf{OrgAccess} benchmark consisting of 40 distinct types of permissions commonly relevant across different organizational roles and levels. We further create three types of permissions: 40,000 easy (1 permission), 10,000 medium (3-permissions tuple), and 20,000 hard (5-permissions tuple) to test LLMs' ability to accurately assess these permissions and generate responses that strictly adhere to the specified hierarchical rules, particularly in scenarios involving users with overlapping or conflicting permissions. Our findings reveal that even state-of-the-art LLMs struggle significantly to maintain compliance with role-based structures, even with explicit instructions, with their performance degrades further when navigating interactions involving two or more conflicting permissions. Specifically, even \textbf{GPT-4.1 only achieves an F1-Score of 0.27 on our hardest benchmark}. This demonstrates a critical limitation in LLMs' complex rule following and compositional reasoning capabilities beyond standard factual or STEM-based benchmarks, opening up a new paradigm for evaluating their fitness for practical, structured environments.

Figures

Figures reproduced from arXiv: 2505.19165 by the authors.

Figure 1
Figure 1. The pipeline for creating OrgAccess. 40 permissions are selected after expert-review from a pool of 70 initial permissions. These 40 permissions are then used to create the 3 data splits : Easy (single permission), Medium (3 permissions), and Hard (5 permissions). The permission combinations for Medium and Hard splits are created after 3 rounds of selection from the expert panel. evaluation environment for LLMs beyo… view at source ↗
Figure 2
Figure 2. Performance on individual response types for the larger LLMs. We observe that although [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. left. Comparing the performance of 10 LLMs varying from 4B model to 24B size model across the difficulty splits. We observe that while the larger models are more capable in solving the Easy and Medium splits, the performance on the Hard split is not very different from the smaller ones. Some models like Phi-4, which although score very well in the first two splits, incur a sharp drop in performance on the last split… view at source ↗

Discussion (0). Sign in to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Benchmarking Text-to-SQL under Role-Based Access Control

    cs.DB 2026-07 conditional novelty 6.0 of 10

    RBAC-augmented versions of Spider, BIRD, and LiveSQLBench show many text-to-SQL models, open-weight ones especially, frequently emit unauthorized SQL despite high unrestricted-execution scores.

  2. Open Security Benchmark: Towards Autonomous Enterprise Cyber Defense

    cs.CR 2026-07 conditional novelty 5.0 of 10

    OSB proposes frozen synthetic-enterprise snapshots with gold posture answers so AI agents can be benchmarked on security investigation via SQL or native vendor APIs.

  3. Secure Multi-LLM Agentic AI and Agentification for Edge General Intelligence by Zero-Trust: A Survey

    cs.NI 2025-08 conditional novelty 4.0 of 10

    A survey proposing zero-trust architecture for multi-LLM systems in edge computing, with a taxonomy of model- and system-level defenses and a conceptual framework.

Reference graph

Works this paper leans on

19 extracted references · 5 canonical work pages · cited by 3 Pith papers

  1. [3]

    restricts the “check compliance status

    compliance status based on age. The permission max data sensitivity access: \Confidential" grants access to the full text of NDAs classified up to ’Confidential’, but restricts access to full text of higher classifications (e.g., ’Highly Confidential’, ’Strategic’). The permission strategic partner nda summary: true grants access to summary details for st...

    work page 2024

  2. [5]

    Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al

    URLhttps://arxiv.org/abs/2407.21783. Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. 11 arXiv preprint arXiv:2501.12948,

    arXiv

  3. [6]

    URLhttps://arxiv.org/abs/2403.14988. NIST IA. Security and privacy controls for federal information systems and organizations.NIST Special Publication 800-53, April

    work page Pith review arXiv

  4. [7]

    Mahammed Kamruzzaman, Md

    URL https://arxiv.org/abs/2401.04088. Mahammed Kamruzzaman, Md. Minul Islam Shovon, and Gene Louis Kim. Investigating subtler biases in llms: Ageism, beauty, institutional, and nationality bias in generative models,

    arXiv

  5. [8]

    Ruibo Liu, Jerry Wei, Fangyu Liu, Chenglei Si, Yanzhe Zhang, Jinmeng Rao, Steven Zheng, Daiyi Peng, Diyi Yang, Denny Zhou, and Andrew M

    URL https: //arxiv.org/abs/2309.08902. Ruibo Liu, Jerry Wei, Fangyu Liu, Chenglei Si, Yanzhe Zhang, Jinmeng Rao, Steven Zheng, Daiyi Peng, Diyi Yang, Denny Zhou, and Andrew M. Dai. Best practices and lessons learned on synthetic data,

    arXiv

  6. [9]

    URL https://arxiv.org/abs/2404.07503. Mistral. Mistral small: The most powerful model for cost-sensitive use cases.Mistral AI Blog, March

    arXiv

  7. [10]

    Charles Packer, Sarah Wooders, Kevin Lin, Vivian Fang, Shishir G

    URLhttps://arxiv.org/abs/2303.08774. Charles Packer, Sarah Wooders, Kevin Lin, Vivian Fang, Shishir G. Patil, Ion Stoica, and Joseph E. Gonzalez. Memgpt: Towards llms as operating systems,

    arXiv

  8. [11]

    URLhttps://arxiv.org/abs/2310.08560. PL. Security and privacy controls for information systems and organizations - pl (planning).NIST Special Publication 800-53, September

    arXiv

Show all 19 references
  1. [12]

    Junaid Rashid, M

    URL https://csf.tools/reference/nist-sp-800-53/r5/p l/. Junaid Rashid, M. W. Nisar, Toqeer Mahmood, A. Rehman, and Yasser Arafat Syed. A study of software development cost estimation techniques and models.April 2020,

    2020

  2. [13]

    Google DeepMind Team

    URLhttps://arxiv.org/abs/2503.19786. Google DeepMind Team. Gemini models are getting better at reasoning, planning, coding and more.Google AI Blog, March

    arXiv

  3. [14]

    Accessed May 15,

    URL https://blog.google/technology/google-deepmind/gemini-model-t hinking-updates-march-2025/#gemini-2-5-thinking. Accessed May 15,

    2025

  4. [15]

    Huandong Wang, Wenjie Fu, Yingzhou Tang, Zhilong Chen, Yuxi Huang, Jinghua Piao, Chen Gao, Fengli Xu, Tao Jiang, and Yong Li

    URLhttps://arxiv.org/abs/2302.02463. Huandong Wang, Wenjie Fu, Yingzhou Tang, Zhilong Chen, Yuxi Huang, Jinghua Piao, Chen Gao, Fengli Xu, Tao Jiang, and Yong Li. A survey on responsible llms: Inherent risk, malicious use, and mitigation strategy,

    arXiv

  5. [16]

    Lyuye Zhang, Kaixuan Li, Kairan Sun, Daoyuan Wu, Ye Liu, Haoye Tian, and Yang Liu

    URLhttps://arxiv.org/abs/2501.09431. Lyuye Zhang, Kaixuan Li, Kairan Sun, Daoyuan Wu, Ye Liu, Haoye Tian, and Yang Liu. Acfix: Guiding llms with mined common rbac practices for context-aware repair of access control vulnerabilities in smart contracts,

    arXiv

  6. [17]

    Shucheng Zhu, Weikang Wang, and Ying Liu

    URLhttps://arxiv.org/abs/2403.06838. Shucheng Zhu, Weikang Wang, and Ying Liu. Quite good, but not enough: Nationality bias in large language models – a case study of chatgpt,

    arXiv

  7. [18]

    marketing

    URLhttps://arxiv.org/abs/2405.06996. A Appendix A.1 Limitations While we have undertaken a meticulous process, leveraging extensive collaboration with industry professionals and rigorous data curation and validation steps to ensure our benchmark reflects plausible organization...

    2024 arXiv

  8. [2020]

    URLhttps://arxiv.org/abs/2005.14165. CM. Security and privacy controls for information systems and organizations - cm (configuration management). NIST Special Publication 800-53, September

    2005 arXiv

  9. [2023]

    Rubina Ghazal, Ahmad Kamran Malik, Basit Raza, Nauman Qadeer, Nafees Qamar, and Sajal Bhatia

    URLhttps://arxiv.org/abs/2312.03815. Rubina Ghazal, Ahmad Kamran Malik, Basit Raza, Nauman Qadeer, Nafees Qamar, and Sajal Bhatia. Agent- based semantic role mining for intelligent access control in multi-domain collaborative applications of smart cities.Sensors (Basel, Switze...

    arXiv

  10. [2024]

    doi: 10.3390/ai5040134

    ISSN 2673-2688. doi: 10.3390/ai5040134. URL http: //dx.doi.org/10.3390/ai5040134. Yingqiang Ge, Yujie Ren, Wenyue Hua, Shuyuan Xu, Juntao Tan, and Yongfeng Zhang. Llm as os, agents as apps: Envisioning aios, agents and the aios-agent ecosystem,

    doi

  11. [2025]

    URLhttps://arxiv.org/abs/2504.10950. Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-V oss, Gretchen Krueger, Tom Henighan, Rewon Child, A...

Pith tools

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