OS-SPEAR: A Toolkit for the Safety, Performance,Efficiency, and Robustness Analysis of OS Agents

• URL: http://arxiv.org/abs/2604.24348v1
• Date: Mon, 27 Apr 2026 11:44:26 GMT
• Title: OS-SPEAR: A Toolkit for the Safety, Performance,Efficiency, and Robustness Analysis of OS Agents
• Abstract summary: OS-SPEAR is a comprehensive toolkit for the systematic analysis of OS agents across four dimensions: Safety, Performance, Efficiency, and Robustness.<n>We conduct an extensive evaluation of 22 popular OS agents using OS-SPEAR.
• Score: 30.910389076133693
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The evolution of Multimodal Large Language Models (MLLMs) has shifted the focus from text generation to active behavioral execution, particularly via OS agents navigating complex GUIs. However, the transition of these agents into trustworthy daily partners is hindered by a lack of rigorous evaluation regarding safety, efficiency, and multi-modal robustness. Current benchmarks suffer from narrow safety scenarios, noisy trajectory labeling, and limited robustness metrics. To bridge this gap, we propose OS-SPEAR, a comprehensive toolkit for the systematic analysis of OS agents across four dimensions: Safety, Performance, Efficiency, and Robustness. OS-SPEAR introduces four specialized subsets: (1) a S(afety)-subset encompassing diverse environment- and human-induced hazards; (2) a P(erformance)-subset curated via trajectory value estimation and stratified sampling; (3) an E(fficiency)-subset quantifying performance through the dual lenses of temporal latency and token consumption; and (4) a R(obustness)-subset that applies cross-modal disturbances to both visual and textual inputs. Additionally, we provide an automated analysis tool to generate human-readable diagnostic reports. We conduct an extensive evaluation of 22 popular OS agents using OS-SPEAR. Our empirical results reveal critical insights into the current landscape: notably, a prevalent trade-off between efficiency and safety or robustness, the performance superiority of specialized agents over general-purpose models, and varying robustness vulnerabilities across different modalities. By providing a multidimensional ranking and a standardized evaluation framework, OS-SPEAR offers a foundational resource for developing the next generation of reliable and efficient OS agents. The dataset and codes are available at https://github.com/Wuzheng02/OS-SPEAR.

Related papers

• RIFT-Bench: Dynamic Red-teaming For Agentic AI Systems [4.789897887600273]
  We introduce RIFT-Bench, a graph representation-driven methodology for dynamic red-teaming.<n>RIFT-Bench operates in two automated phases: Discovery, which extracts system structure, and Scanning, which deploys adaptive adversarial attacks.<n>We demonstrate the effectiveness of the proposed evaluation pipeline across 45 agentic systems spanning a diverse range of implementations.
  arXiv  Detail & Related papers  (2026-06-22T20:46:56Z)
• HIDBench: Benchmarking Large Language Models for Host-Based Intrusion Detection [11.060899899257755]
  We present a new benchmark to assess large language models' capabilities in supporting host-based intrusion detection systems.<n>This task requires fine-grained reasoning over large-scale, noisy, and highly imbalanced system logs.<n>Our results highlight that while LLMs show strong potential for HIDS, their effectiveness is highly sensitive to data complexity.
  arXiv  Detail & Related papers  (2026-05-20T22:07:12Z)
• GAMMAF: A Common Framework for Graph-Based Anomaly Monitoring Benchmarking in LLM Multi-Agent Systems [0.42970700836450487]
  We introduce Gammaf (Graph-based Anomaly Monitoring for LLM Multi-Agent systems Framework), an open-source benchmarking platform.<n>Gammaf is not a novel defense mechanism itself, but rather a comprehensive evaluation architecture designed to generate synthetic multi-agent interaction datasets.
  arXiv  Detail & Related papers  (2026-04-27T13:45:14Z)
• Beyond Static Benchmarks: Synthesizing Harmful Content via Persona-based Simulation for Robust Evaluation [14.366084759097445]
  We propose a framework for synthesizing harmful content, leveraging persona-guided large language model (LLM) agents.<n>Our approach constructs two-dimensional user personas by integrating demographic identities and topical interests with situational harmful strategies.<n>Both human and LLM-based evaluations confirm that our framework achieves a high harmful generation success rate.
  arXiv  Detail & Related papers  (2026-04-18T14:58:02Z)
• Explainable and Fine-Grained Safeguarding of LLM Multi-Agent Systems via Bi-Level Graph Anomaly Detection [76.91230292971115]
  Large language model (LLM)-based multi-agent systems (MAS) have shown strong capabilities in solving complex tasks.<n>XG-Guard is an explainable and fine-grained safeguarding framework for detecting malicious agents in MAS.
  arXiv  Detail & Related papers  (2025-12-21T13:46:36Z)
• OS-Sentinel: Towards Safety-Enhanced Mobile GUI Agents via Hybrid Validation in Realistic Workflows [77.95511352806261]
  Computer-using agents powered by Vision-Language Models (VLMs) have demonstrated human-like capabilities in operating digital environments like mobile platforms.<n>We propose OS-Sentinel, a novel hybrid safety detection framework that combines a Formal Verifier for detecting explicit system-level violations with a Contextual Judge for assessing contextual risks and agent actions.
  arXiv  Detail & Related papers  (2025-10-28T13:22:39Z)
• MAVUL: Multi-Agent Vulnerability Detection via Contextual Reasoning and Interactive Refinement [9.377934769326416]
  MAVUL is a novel multi-agent vulnerability detection system that integrates contextual reasoning and interactive refinement.<n>We show MAVUL significantly outperforms existing multi-agent systems with over 62% higher pairwise accuracy and single-agent systems with over 600% higher average performance.
  arXiv  Detail & Related papers  (2025-09-30T22:21:43Z)
• Agent4FaceForgery: Multi-Agent LLM Framework for Realistic Face Forgery Detection [108.5042835056188]
  This work introduces Agent4FaceForgery to address two fundamental problems.<n>How to capture the diverse intents and iterative processes of human forgery creation.<n>How to model the complex, often adversarial, text-image interactions that accompany forgeries in social media.
  arXiv  Detail & Related papers  (2025-09-16T01:05:01Z)
• Deep Learning Models for Robust Facial Liveness Detection [56.08694048252482]
  This study introduces a robust solution through novel deep learning models addressing the deficiencies in contemporary anti-spoofing techniques.<n>By innovatively integrating texture analysis and reflective properties associated with genuine human traits, our models distinguish authentic presence from replicas with remarkable precision.
  arXiv  Detail & Related papers  (2025-08-12T17:19:20Z)
• AGENTSAFE: Benchmarking the Safety of Embodied Agents on Hazardous Instructions [64.85086226439954]
  We present SAFE, a benchmark for assessing the safety of embodied VLM agents on hazardous instructions.<n> SAFE comprises three components: SAFE-THOR, SAFE-VERSE, and SAFE-DIAGNOSE.<n>We uncover systematic failures in translating hazard recognition into safe planning and execution.
  arXiv  Detail & Related papers  (2025-06-17T16:37:35Z)
• Expert-in-the-Loop Systems with Cross-Domain and In-Domain Few-Shot Learning for Software Vulnerability Detection [38.083049237330826]
  This study explores the use of Large Language Models (LLMs) in software vulnerability assessment by simulating the identification of Python code with known Common Weaknessions (CWEs)<n>Our results indicate that while zero-shot prompting performs poorly, few-shot prompting significantly enhances classification performance.<n> challenges such as model reliability, interpretability, and adversarial robustness remain critical areas for future research.
  arXiv  Detail & Related papers  (2025-06-11T18:43:51Z)
• Run-time Introspection of 2D Object Detection in Automated Driving Systems Using Learning Representations [13.529124221397822]
  We introduce a novel introspection solution for 2D object detection based on Deep Neural Networks (DNNs) We implement several state-of-the-art (SOTA) introspection mechanisms for error detection in 2D object detection, using one-stage and two-stage object detectors evaluated on KITTI and BDD datasets. Our performance evaluation shows that the proposed introspection solution outperforms SOTA methods, achieving an absolute reduction in the missed error ratio of 9% to 17% in the BDD dataset.
  arXiv  Detail & Related papers  (2024-03-02T10:56:14Z)
• MMRNet: Improving Reliability for Multimodal Object Detection and Segmentation for Bin Picking via Multimodal Redundancy [68.7563053122698]
  We propose a reliable object detection and segmentation system with MultiModal Redundancy (MMRNet) This is the first system that introduces the concept of multimodal redundancy to address sensor failure issues during deployment. We present a new label-free multi-modal consistency (MC) score that utilizes the output from all modalities to measure the overall system output reliability and uncertainty.
  arXiv  Detail & Related papers  (2022-10-19T19:15:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.