Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI

• URL: http://arxiv.org/abs/2409.15398v1
• Date: Mon, 23 Sep 2024 10:18:10 GMT
• Title: Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI
• Authors: Ambrish Rawat, Stefan Schoepf, Giulio Zizzo, Giandomenico Cornacchia, Muhammad Zaid Hameed, Kieran Fraser, Erik Miehling, Beat Buesser, Elizabeth M. Daly, Mark Purcell, Prasanna Sattigeri, Pin-Yu Chen, Kush R. Varshney,
• Abstract summary: 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.
• Score: 52.138044013005
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As 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. Despite growing academic interest in adversarial risks for generative AI, there is limited guidance tailored for practitioners to assess and mitigate these challenges in real-world environments. To address this, our contributions include: (1) a practical examination of red- and blue-teaming strategies for securing generative AI, (2) identification of key challenges and open questions in defense development and evaluation, and (3) the Attack Atlas, an intuitive framework that brings a practical approach to analyzing single-turn input attacks, placing it at the forefront for practitioners. This work aims to bridge the gap between academic insights and practical security measures for the protection of generative AI systems.

Related papers

• Towards Robust and Secure Embodied AI: A Survey on Vulnerabilities and Attacks [22.154001025679896]
  Embodied AI systems, including robots and autonomous vehicles, are increasingly integrated into real-world applications. These vulnerabilities manifest through sensor spoofing, adversarial attacks, and failures in task and motion planning.
  arXiv  Detail & Related papers  (2025-02-18T03:38:07Z)
• Considerations Influencing Offense-Defense Dynamics From Artificial Intelligence [0.0]
  AI can enhance defensive capabilities but also presents avenues for malicious exploitation and large-scale societal harm. This paper proposes a taxonomy to map and examine the key factors that influence whether AI systems predominantly pose threats or offer protective benefits to society.
  arXiv  Detail & Related papers  (2024-12-05T10:05:53Z)
• Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics [70.93622520400385]
  This paper systematically quantifies the robustness of VLA-based robotic systems. We introduce an untargeted position-aware attack objective that leverages spatial foundations to destabilize robotic actions. We also design an adversarial patch generation approach that places a small, colorful patch within the camera's view, effectively executing the attack in both digital and physical environments.
  arXiv  Detail & Related papers  (2024-11-18T01:52:20Z)
• Adversarial Attacks of Vision Tasks in the Past 10 Years: A Survey [21.4046846701173]
  Adversarial attacks pose significant security threats during machine learning inference. Existing reviews often focus on attack classifications and lack comprehensive, in-depth analysis. This article addresses these gaps by offering a thorough summary of traditional and LVLM adversarial attacks.
  arXiv  Detail & Related papers  (2024-10-31T07:22:51Z)
• 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. 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 EARBench, a novel framework for automated physical risk assessment in EAI scenarios.
  arXiv  Detail & Related papers  (2024-08-08T13:19:37Z)
• Red-Teaming for Generative AI: Silver Bullet or Security Theater? [42.35800543892003]
  We argue that while red-teaming may be a valuable big-tent idea for characterizing GenAI harm mitigations, industry may effectively apply red-teaming and other strategies behind closed doors to safeguard AI. To move toward a more robust toolbox of evaluations for generative AI, we synthesize our recommendations into a question bank meant to guide and scaffold future AI red-teaming practices.
  arXiv  Detail & Related papers  (2024-01-29T05:46:14Z)
• A Red Teaming Framework for Securing AI in Maritime Autonomous Systems [0.0]
  We propose one of the first red team frameworks for evaluating the AI security of maritime autonomous systems. This framework is a multi-part checklist, which can be tailored to different systems and requirements. We demonstrate this framework to be highly effective for a red team to use to uncover numerous vulnerabilities within a real-world maritime autonomous systems AI.
  arXiv  Detail & Related papers  (2023-12-08T14:59:07Z)
• Attention-Based Real-Time Defenses for Physical Adversarial Attacks in Vision Applications [58.06882713631082]
  Deep neural networks exhibit excellent performance in computer vision tasks, but their vulnerability to real-world adversarial attacks raises serious security concerns. This paper proposes an efficient attention-based defense mechanism that exploits adversarial channel-attention to quickly identify and track malicious objects in shallow network layers. It also introduces an efficient multi-frame defense framework, validating its efficacy through extensive experiments aimed at evaluating both defense performance and computational cost.
  arXiv  Detail & Related papers  (2023-11-19T00:47:17Z)
• Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey [114.17568992164303]
  Adrial attacks and defenses in machine learning and deep neural network have been gaining significant attention. This survey provides a comprehensive overview of the recent advancements in the field of adversarial attack and defense techniques. New avenues of attack are also explored, including search-based, decision-based, drop-based, and physical-world attacks.
  arXiv  Detail & Related papers  (2023-03-11T04:19:31Z)
• Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain [58.30296637276011]
  This paper summarizes the latest research on adversarial attacks against security solutions based on machine learning techniques. It is the first to discuss the unique challenges of implementing end-to-end adversarial attacks in the cyber security domain.
  arXiv  Detail & Related papers  (2020-07-05T18:22:40Z)

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.