Secret Collusion among Generative AI Agents

• URL: http://arxiv.org/abs/2402.07510v3
• Date: Fri, 08 Nov 2024 14:46:40 GMT
• Title: Secret Collusion among Generative AI Agents
• Authors: Sumeet Ramesh Motwani, Mikhail Baranchuk, Martin Strohmeier, Vijay Bolina, Philip H. S. Torr, Lewis Hammond, Christian Schroeder de Witt,
• Abstract summary: Recent capability increases in large language models (LLMs) open up applications in which groups of communicating generative AI agents solve joint tasks. This poses privacy and security challenges concerning the unauthorised sharing of information. Modern steganographic techniques could render such dynamics hard to detect.
• Score: 43.468790060808914
• License:
• Abstract: Recent capability increases in large language models (LLMs) open up applications in which groups of communicating generative AI agents solve joint tasks. This poses privacy and security challenges concerning the unauthorised sharing of information, or other unwanted forms of agent coordination. Modern steganographic techniques could render such dynamics hard to detect. In this paper, we comprehensively formalise the problem of secret collusion in systems of generative AI agents by drawing on relevant concepts from both AI and security literature. We study incentives for the use of steganography, and propose a variety of mitigation measures. Our investigations result in a model evaluation framework that systematically tests capabilities required for various forms of secret collusion. We provide extensive empirical results across a range of contemporary LLMs. While the steganographic capabilities of current models remain limited, GPT-4 displays a capability jump suggesting the need for continuous monitoring of steganographic frontier model capabilities. We conclude by laying out a comprehensive research program to mitigate future risks of collusion between generative AI models.

Related papers

• Unsupervised Model Diagnosis [49.36194740479798]
  This paper proposes Unsupervised Model Diagnosis (UMO) to produce semantic counterfactual explanations without any user guidance. Our approach identifies and visualizes changes in semantics, and then matches these changes to attributes from wide-ranging text sources.
  arXiv  Detail & Related papers  (2024-10-08T17:59:03Z)
• Hidden in Plain Text: Emergence & Mitigation of Steganographic Collusion in LLMs [0.600808022072121]
  Collusion to the disadvantage of others has been identified as a central form of undesirable agent cooperation. The use of information hiding (steganography) in agent communications could render collusion practically undetectable. This underscores the need for evaluation frameworks to monitor and mitigate steganographic collusion capabilities.
  arXiv  Detail & Related papers  (2024-10-02T16:18:33Z)
• AI Safety in Generative AI Large Language Models: A Survey [14.737084887928408]
  Large Language Model (LLMs) that exhibit generative AI capabilities are facing accelerated adoption and innovation. Generative AI (GAI) inevitably raises concerns about the risks and safety associated with these models. This article provides an up-to-date survey of recent trends in AI safety research of GAI-LLMs from a computer scientist's perspective.
  arXiv  Detail & Related papers  (2024-07-06T09:00:18Z)
• On the Challenges and Opportunities in Generative AI [135.2754367149689]
  We argue that current large-scale generative AI models do not sufficiently address several fundamental issues that hinder their widespread adoption across domains. In this work, we aim to identify key unresolved challenges in modern generative AI paradigms that should be tackled to further enhance their capabilities, versatility, and reliability.
  arXiv  Detail & Related papers  (2024-02-28T15:19:33Z)
• HuntGPT: Integrating Machine Learning-Based Anomaly Detection and Explainable AI with Large Language Models (LLMs) [0.09208007322096533]
  We present HuntGPT, a specialized intrusion detection dashboard applying a Random Forest classifier. The paper delves into the system's architecture, components, and technical accuracy, assessed through Certified Information Security Manager (CISM) Practice Exams. The results demonstrate that conversational agents, supported by LLM and integrated with XAI, provide robust, explainable, and actionable AI solutions in intrusion detection.
  arXiv  Detail & Related papers  (2023-09-27T20:58:13Z)
• Towards General Visual-Linguistic Face Forgery Detection [95.73987327101143]
  Deepfakes are realistic face manipulations that can pose serious threats to security, privacy, and trust. Existing methods mostly treat this task as binary classification, which uses digital labels or mask signals to train the detection model. We propose a novel paradigm named Visual-Linguistic Face Forgery Detection(VLFFD), which uses fine-grained sentence-level prompts as the annotation.
  arXiv  Detail & Related papers  (2023-07-31T10:22:33Z)
• Discovering Individual Rewards in Collective Behavior through Inverse Multi-Agent Reinforcement Learning [3.4437947384641032]
  We introduce an off-policy inverse multi-agent reinforcement learning algorithm (IMARL) By leveraging demonstrations, our algorithm automatically uncovers the reward function and learns an effective policy for the agents. The proposed IMARL algorithm is a significant step towards understanding collective dynamics from the perspective of its constituents.
  arXiv  Detail & Related papers  (2023-05-17T20:07:30Z)
• Decentralized Adversarial Training over Graphs [55.28669771020857]
  The vulnerability of machine learning models to adversarial attacks has been attracting considerable attention in recent years. This work studies adversarial training over graphs, where individual agents are subjected to varied strength perturbation space.
  arXiv  Detail & Related papers  (2023-03-23T15:05:16Z)
• DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanations [119.1953397679783]
  We focus on advancing the state-of-the-art in interpreting multimodal models. Our proposed approach, DIME, enables accurate and fine-grained analysis of multimodal models.
  arXiv  Detail & Related papers  (2022-03-03T20:52:47Z)

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.