Asset-centric Threat Modeling for AI-based Systems
- URL: http://arxiv.org/abs/2403.06512v2
- Date: Mon, 3 Jun 2024 09:30:24 GMT
- Title: Asset-centric Threat Modeling for AI-based Systems
- Authors: Jan von der Assen, Jamo Sharif, Chao Feng, Christian Killer, Gérôme Bovet, Burkhard Stiller,
- Abstract summary: 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.
- Score: 7.696807063718328
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Threat modeling is a popular method to securely develop systems by achieving awareness of potential areas of future damage caused by adversaries. However, threat modeling for systems relying on Artificial Intelligence is still not well explored. While conventional threat modeling methods and tools did not address AI-related threats, research on this amalgamation still lacks solutions capable of guiding and automating the process, as well as providing evidence that the methods hold up in practice. Consequently, this paper presents ThreatFinderAI, an approach and tool providing guidance and automation 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. Secondly, the approach was used to identify and discuss strategic risks in an LLM-based application through a case study. Overall, the solution's usability was well-perceived and effectively supports threat identification and risk discussion.
Related papers
- A Formal Framework for Assessing and Mitigating Emergent Security Risks in Generative AI Models: Bridging Theory and Dynamic Risk Mitigation [0.3413711585591077]
As generative AI systems, including large language models (LLMs) and diffusion models, advance rapidly, their growing adoption has led to new and complex security risks.
This paper introduces a novel formal framework for categorizing and mitigating these emergent security risks.
We identify previously under-explored risks, including latent space exploitation, multi-modal cross-attack vectors, and feedback-loop-induced model degradation.
arXiv Detail & Related papers (2024-10-15T02:51:32Z) - AsIf: Asset Interface Analysis of Industrial Automation Devices [1.3216177247621483]
Industrial control systems are increasingly adopting IT solutions, including communication standards and protocols.
As these systems become more decentralized and interconnected, a critical need for enhanced security measures arises.
Threat modeling is traditionally performed in structured brainstorming sessions involving domain and security experts.
We propose a method for the analysis of assets in industrial systems, with special focus on physical threats.
arXiv Detail & Related papers (2024-09-26T07:19:15Z) - Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI [52.138044013005]
generative AI, particularly large language models (LLMs), become increasingly integrated into production applications.
New attack surfaces and vulnerabilities emerge and put a focus on adversarial threats in natural language and multi-modal systems.
Red-teaming has gained importance in proactively identifying weaknesses in these systems, while blue-teaming works to protect against such adversarial attacks.
This work aims to bridge the gap between academic insights and practical security measures for the protection of generative AI systems.
arXiv Detail & Related papers (2024-09-23T10:18:10Z) - EAIRiskBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [47.69642609574771]
Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction.
Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results.
However, the deployment of these agents in physical environments presents significant safety challenges.
This study introduces EAIRiskBench, a novel framework for automated physical risk assessment in EAI scenarios.
arXiv Detail & Related papers (2024-08-08T13:19:37Z) - The Shadow of Fraud: The Emerging Danger of AI-powered Social Engineering and its Possible Cure [30.431292911543103]
Social engineering (SE) attacks remain a significant threat to both individuals and organizations.
The advancement of Artificial Intelligence (AI) has potentially intensified these threats by enabling more personalized and convincing attacks.
This survey paper categorizes SE attack mechanisms, analyzes their evolution, and explores methods for measuring these threats.
arXiv Detail & Related papers (2024-07-22T17:37:31Z) - 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) - Autonomous Threat Hunting: A Future Paradigm for AI-Driven Threat Intelligence [0.0]
Review explores the amalgamation of artificial intelligence (AI) and traditional threat intelligence methodologies.
Examines the transformative influence of AI and machine learning on conventional threat intelligence practices.
Case studies and evaluations highlight success stories and lessons learned by organizations adopting AI-driven threat intelligence.
arXiv Detail & Related papers (2023-12-30T17:36:08Z) - Managing extreme AI risks amid rapid progress [171.05448842016125]
We describe risks that include large-scale social harms, malicious uses, and irreversible loss of human control over autonomous AI systems.
There is a lack of consensus about how exactly such risks arise, and how to manage them.
Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems.
arXiv Detail & Related papers (2023-10-26T17:59:06Z) - Model evaluation for extreme risks [46.53170857607407]
Further progress in AI development could lead to capabilities that pose extreme risks, such as offensive cyber capabilities or strong manipulation skills.
We explain why model evaluation is critical for addressing extreme risks.
arXiv Detail & Related papers (2023-05-24T16:38:43Z) - AI Maintenance: A Robustness Perspective [91.28724422822003]
We introduce highlighted robustness challenges in the AI lifecycle and motivate AI maintenance by making analogies to car maintenance.
We propose an AI model inspection framework to detect and mitigate robustness risks.
Our proposal for AI maintenance facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle.
arXiv Detail & Related papers (2023-01-08T15:02:38Z) - Holistic Adversarial Robustness of Deep Learning Models [91.34155889052786]
Adversarial robustness studies the worst-case performance of a machine learning model to ensure safety and reliability.
This paper provides a comprehensive overview of research topics and foundational principles of research methods for adversarial robustness of deep learning models.
arXiv Detail & Related papers (2022-02-15T05:30:27Z)
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.