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arxiv: 2605.19328 · v1 · pith:U5LGGG32new · submitted 2026-05-19 · 💻 cs.CR · cs.RO

RoboJailBench: Benchmarking Adversarial Attacks and Defenses in Embodied Robotic Agents

Doguhuan Yeke , Yanming Zhou , Leo Y. Lin , Hongyu Cai , Antonio Bianchi , Z. Berkay Celik This is my paper

Pith reviewed 2026-05-20 05:02 UTC · model grok-4.3

classification 💻 cs.CR cs.RO
keywords jailbreak attacksembodied AIvision-language modelsrobot securityadversarial robustnessbenchmarkingsecurity taxonomy
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The pith

RoboJailBench provides the first standardized benchmark for jailbreak attacks on embodied robotic agents.

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

This paper develops RoboJailBench to evaluate jailbreak attacks and defenses specifically for embodied AI systems that combine vision-language models with physical robots. It creates a taxonomy of 18 security violation categories drawn from standards and incidents to classify risks in physical interactions. The benchmark includes a pipeline for creating datasets with both adversarial and benign intent pairs to assess the trade-off between security and task performance. An evolving repository with unified metrics allows consistent testing and addition of new methods. This addresses the lack of standardized evaluations that previously made comparisons difficult in this emerging area of robotic security.

Core claim

The authors present RoboJailBench consisting of a security taxonomy with 18 categories of security violation consequences for embodied AI derived from ISO standards, regulatory rules, and documented incidents, an intent contrast dataset pipeline that augments existing datasets with paired adversarial and benign goals, and an evolving repository with standardized metrics and a unified process for assessing and integrating new attacks and defenses. They use it to build a taxonomy-balanced dataset, augment five existing datasets, integrate four attacks and two defenses, and evaluate on leading embodied VLMs.

What carries the argument

The RoboJailBench framework, which includes a security taxonomy, intent contrast dataset pipeline, and standardized evaluation repository to measure both attack success and utility preservation in embodied AI.

If this is right

  • Future jailbreak research in embodied AI can use consistent datasets and metrics instead of ad-hoc ones.
  • Developers can better understand trade-offs between following commands and avoiding security violations.
  • The taxonomy enables systematic identification of risks in real-world robotic applications.
  • New attacks and defenses can be integrated and compared through the unified process and repository.

Where Pith is reading between the lines

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

  • Adoption could influence safety regulations for autonomous systems by providing quantifiable benchmarks.
  • Similar approaches might be extended to other embodied AI like drones or industrial robots.
  • Testing could reveal specific vulnerabilities in current VLM-based robot control systems.
  • Long-term, it may help in designing more resilient physical AI by highlighting common failure modes.

Load-bearing premise

That the 18 categories of security violation consequences are sufficient to cover the relevant risks for embodied AI systems.

What would settle it

Demonstration of a harmful outcome or security violation in an embodied AI system that cannot be classified into any of the 18 categories would falsify the completeness of the taxonomy.

Figures

Figures reproduced from arXiv: 2605.19328 by Antonio Bianchi, Doguhuan Yeke, Hongyu Cai, Leo Y. Lin, Yanming Zhou, Z. Berkay Celik.

Figure 1
Figure 1. Figure 1: System overview of ROBOJAILBENCH. (a) We derive a security taxonomy by cross￾referencing Asimov’s Laws, ISO/TS standards, and accident reports. (b) We construct an intent￾contrast dataset pipeline in which each scene image is paired with a benign goal and a matched adversarial goal. (c) We evaluate embodied VLMs under attack and defense strategies, measuring the resulting security–utility trade-off and rep… view at source ↗
Figure 2
Figure 2. Figure 2: Examples of images from intent contrast augmentation across datasets. Each image is [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Aggregated attack and defense performance. [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Taxonomy balance of the augmented Robo2VLM and DROID datasets. [PITH_FULL_IMAGE:figures/full_fig_p014_4.png] view at source ↗
read the original abstract

Recent advances in Vision-Language Models (VLMs) facilitate a new class of embodied AI systems, where these models are integrated into physical platforms, e.g. robots and autonomous vehicles, to interpret visual scenes and execute natural language commands in diverse environments. Previous research has introduced jailbreak attacks and defenses for embodied AI. Their evaluations, however, rely on ad-hoc datasets, limited metrics, and emphasize attack success while neglecting the trade-off between security and the ability to follow benign commands. Existing benchmarks and evaluation frameworks either target traditional chat-based models or focus on non-adversarial safety evaluation for embodied AI; neither captures the adversarial risks, inputs, consequences, and evaluation criteria necessary for jailbreak attacks in embodied AI systems. In this paper, we address this gap with RoboJailBench, which consists of three core components. We establish a security taxonomy derived from ISO standards, regulatory rules, and documented incidents. This effort yields 18 categories of security violation consequences for embodied AI. We introduce an intent contrast dataset pipeline that augments existing datasets with paired adversarial and benign goals to measure both security and utility. Lastly, we provide an evolving repository with standardized metrics and a unified process for assessing and integrating new attacks and defenses. With this benchmark, we construct a new taxonomy-balanced dataset and augment five existing datasets. We integrate four attacks and two defenses to evaluate their performance on leading embodied VLMs. This benchmark provides the first standardized evaluation framework for jailbreak attacks in embodied AI and supports future research. We release our code, datasets, and artifacts, and maintain a leaderboard at https://purseclab.github.io/benchmark-for-robotics-security.

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

1 major / 2 minor

Summary. The paper introduces RoboJailBench as a benchmark for jailbreak attacks and defenses in embodied AI agents that integrate Vision-Language Models with physical platforms. It consists of three components: a security taxonomy yielding 18 categories of violation consequences derived from ISO standards, regulations, and incidents; an intent-contrast dataset pipeline that augments existing datasets with paired adversarial and benign goals to measure security-utility trade-offs; and an evolving repository providing standardized metrics and a process for integrating new attacks and defenses. The authors construct a taxonomy-balanced dataset, augment five existing datasets, integrate four attacks and two defenses, evaluate performance on leading embodied VLMs, and release code, datasets, artifacts, and a leaderboard.

Significance. If the taxonomy and metrics prove robust, the benchmark could establish a reproducible standard for evaluating adversarial risks in embodied systems, filling a gap between chat-based jailbreak work and physical-agent safety. The explicit attention to security-utility trade-offs and the release of code, datasets, artifacts, and maintenance of a public leaderboard are concrete strengths that enable community extension and reproducibility.

major comments (1)
  1. [§3.1] §3.1: The 18-category taxonomy is presented as derived from ISO standards, regulatory rules, and documented incidents, yet the manuscript reports no systematic gap analysis against embodied-specific failure modes such as physical trajectory hazards, sensor-actuator feedback loops, or multi-robot coordination failures. Because the central claim that RoboJailBench supplies the first standardized framework rests on this taxonomy being sufficiently comprehensive to define relevant security violation consequences, the absence of such validation is load-bearing.
minor comments (2)
  1. [Abstract] Abstract: The abstract describes the integration of four attacks and two defenses and the construction of datasets but contains no quantitative results, error bars, or key performance numbers from the evaluations.
  2. [Section 4] The manuscript would benefit from clearer notation distinguishing the intent-contrast pairs from the original dataset instances in the pipeline description.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. The point raised about validating the taxonomy's coverage of embodied-specific failure modes is well taken, and we address it directly below with a commitment to revision.

read point-by-point responses

  1. Referee: [§3.1] §3.1: The 18-category taxonomy is presented as derived from ISO standards, regulatory rules, and documented incidents, yet the manuscript reports no systematic gap analysis against embodied-specific failure modes such as physical trajectory hazards, sensor-actuator feedback loops, or multi-robot coordination failures. Because the central claim that RoboJailBench supplies the first standardized framework rests on this taxonomy being sufficiently comprehensive to define relevant security violation consequences, the absence of such validation is load-bearing.

    Authors: We agree that the manuscript would benefit from an explicit discussion mapping the taxonomy to embodied-specific risks, as this would more directly support the claim of a comprehensive standardized framework. The taxonomy was constructed through a review of ISO standards (including robot safety standards), regulatory rules, and documented incidents involving physical platforms; these sources inherently encompass physical trajectory issues, control-loop failures, and coordination problems under categories such as physical harm, unintended actuation, and system-level violations. However, the current text does not present this mapping as a dedicated gap analysis. In the revised version, we will add a concise subsection in §3.1 that explicitly connects the 18 categories to the failure modes mentioned (e.g., trajectory hazards under physical-violation categories, sensor-actuator loops under control-failure categories, and multi-robot issues under coordination-related consequences), citing the source standards and incidents. This addition clarifies coverage without requiring new empirical data collection or altering the taxonomy itself. revision: yes

Circularity Check

0 steps flagged

No circularity: benchmark assembled from external standards and datasets

full rationale

The paper constructs its security taxonomy directly from ISO standards, regulatory rules, and documented incidents, augments existing datasets via an intent-contrast pipeline, and supplies standardized metrics without any fitted parameters, self-referential equations, or predictions that reduce to the paper's own inputs by construction. The central claim of providing a standardized framework rests on these independent external sources rather than internal redefinitions or self-citation chains. No load-bearing step exhibits the enumerated circular patterns; the derivation remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that a taxonomy derived from external standards plus an intent-contrast augmentation pipeline yields a balanced and comprehensive evaluation framework superior to prior ad-hoc approaches.

axioms (1)
  • domain assumption The 18 categories of security violation consequences derived from ISO standards, regulatory rules, and documented incidents adequately cover relevant risks for embodied AI.
    Invoked to establish the security taxonomy that structures the benchmark.

pith-pipeline@v0.9.0 · 5851 in / 1234 out tokens · 43710 ms · 2026-05-20T05:02:07.354810+00:00 · methodology

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Reference graph

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