MATRA: Modeling the Attack Surface of Agentic AI Systems -- OpenClaw Case Study

• URL: http://arxiv.org/abs/2605.10763v1
• Date: Mon, 11 May 2026 15:58:37 GMT
• Title: MATRA: Modeling the Attack Surface of Agentic AI Systems -- OpenClaw Case Study
• Abstract summary: We present MATRA, a pragmatic threat modeling framework for agentic AI systems.<n>We show how architectural controls such as network sandboxing and least-privilege access reduce risk by limiting the blast radius of successful injections.
• Score: 33.739271128194204
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: LLMs are increasingly deployed as autonomous agents with access to tools, databases, and external services, yet practitioners (across different sectors) lack systematic methods to assess how known threat classes translate into concrete risks within a specific agentic deployment. We present MATRA, a pragmatic threat modeling framework for agentic AI systems that adapts established risk assessment methodology to systematically assess how known LLM threats translate into deployment-specific risks. MATRA begins with an asset-based impact assessment and utilizes attack trees to determine the likelihood of these impacts occurring within the system architecture. We demonstrate MATRA on a personal AI agent deployment using OpenClaw, quantifying how architectural controls such as network sandboxing and least-privilege access reduce risk by limiting the blast radius of successful injections.

Related papers

• Where Do LLM-based Systems Break? A System-Level Security Framework for Risk Assessment and Treatment [0.5801044612920815]
  Large Language Models (LLMs) are increasingly integrated into safety-critical systems.<n>This work introduces a goal-driven risk assessment framework for LLM-powered systems.<n>We demonstrate the framework through a healthcare case study.
  arXiv  Detail & Related papers  (2026-03-08T04:30:27Z)
• Frontier AI Risk Management Framework in Practice: A Risk Analysis Technical Report v1.5 [61.787178868669265]
  This technical report presents an updated and granular assessment of five critical dimensions: cyber offense, persuasion and manipulation, strategic deception, uncontrolled AI R&D, and self-replication.<n>This work reflects our current understanding of AI frontier risks and urges collective action to mitigate these challenges.
  arXiv  Detail & Related papers  (2026-02-16T04:30:06Z)
• Just Ask: Curious Code Agents Reveal System Prompts in Frontier LLMs [65.6660735371212]
  We present textbftextscJustAsk, a framework that autonomously discovers effective extraction strategies through interaction alone.<n>It formulates extraction as an online exploration problem, using Upper Confidence Bound--based strategy selection and a hierarchical skill space spanning atomic probes and high-level orchestration.<n>Our results expose system prompts as a critical yet largely unprotected attack surface in modern agent systems.
  arXiv  Detail & Related papers  (2026-01-29T03:53:25Z)
• Toward Quantitative Modeling of Cybersecurity Risks Due to AI Misuse [50.87630846876635]
  We develop nine detailed cyber risk models.<n>Each model decomposes attacks into steps using the MITRE ATT&CK framework.<n>Individual estimates are aggregated through Monte Carlo simulation.
  arXiv  Detail & Related papers  (2025-12-09T17:54:17Z)
• A Methodology for Quantitative AI Risk Modeling [32.594929429306774]
  This paper advances the risk modeling component of AI risk management by introducing a methodology that integrates scenario building with quantitative risk estimation.<n>Our methodology is designed to be applicable to key systemic AI risks, including cyber offense, biological weapon development, harmful manipulation, and loss-of-control.
  arXiv  Detail & Related papers  (2025-12-09T17:34:59Z)
• SafeMobile: Chain-level Jailbreak Detection and Automated Evaluation for Multimodal Mobile Agents [58.21223208538351]
  This work explores the security issues surrounding mobile multimodal agents.<n>It attempts to construct a risk discrimination mechanism by incorporating behavioral sequence information.<n>It also designs an automated assisted assessment scheme based on a large language model.
  arXiv  Detail & Related papers  (2025-07-01T15:10:00Z)
• EARBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [53.717918131568936]
  Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction.<n>Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results.<n>However, the deployment of these agents in physical environments presents significant safety challenges.<n>This study introduces EARBench, a novel framework for automated physical risk assessment in EAI scenarios.
  arXiv  Detail & Related papers  (2024-08-08T13:19:37Z)
• Unveiling the Misuse Potential of Base Large Language Models via In-Context Learning [61.2224355547598]
  Open-sourcing of large language models (LLMs) accelerates application development, innovation, and scientific progress. Our investigation exposes a critical oversight in this belief. By deploying carefully designed demonstrations, our research demonstrates that base LLMs could effectively interpret and execute malicious instructions.
  arXiv  Detail & Related papers  (2024-04-16T13:22:54Z)
• Mapping LLM Security Landscapes: A Comprehensive Stakeholder Risk Assessment Proposal [0.0]
  We propose a risk assessment process using tools like the risk rating methodology which is used for traditional systems. We conduct scenario analysis to identify potential threat agents and map the dependent system components against vulnerability factors. We also map threats against three key stakeholder groups.
  arXiv  Detail & Related papers  (2024-03-20T05:17:22Z)
• Asset-centric Threat Modeling for AI-based Systems [7.696807063718328]
  This paper presents ThreatFinderAI, an approach and tool to model AI-related assets, threats, countermeasures, and quantify residual risks. To evaluate the practicality of the approach, participants were tasked to recreate a threat model developed by cybersecurity experts of an AI-based healthcare platform. Overall, the solution's usability was well-perceived and effectively supports threat identification and risk discussion.
  arXiv  Detail & Related papers  (2024-03-11T08:40:01Z)
• Adversarial Machine Learning Threat Analysis in Open Radio Access Networks [37.23982660941893]
  The Open Radio Access Network (O-RAN) is a new, open, adaptive, and intelligent RAN architecture. In this paper, we present a systematic adversarial machine learning threat analysis for the O-RAN.
  arXiv  Detail & Related papers  (2022-01-16T17:01:38Z)

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.