Superplatforms Have to Attack AI Agents
- URL: http://arxiv.org/abs/2505.17861v1
- Date: Fri, 23 May 2025 13:13:44 GMT
- Title: Superplatforms Have to Attack AI Agents
- Authors: Jianghao Lin, Jiachen Zhu, Zheli Zhou, Yunjia Xi, Weiwen Liu, Yong Yu, Weinan Zhang,
- Abstract summary: We argue that superplatforms have to attack AI agents to defend their centralized control of digital traffic entrance.<n>We show how AI agents can disintermediate superplatforms and potentially become the next dominant gatekeepers.<n>Our aim is to raise awareness and encourage critical discussion for collaborative solutions.
- Score: 33.71292740136041
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Over the past decades, superplatforms, digital companies that integrate a vast range of third-party services and applications into a single, unified ecosystem, have built their fortunes on monopolizing user attention through targeted advertising and algorithmic content curation. Yet the emergence of AI agents driven by large language models (LLMs) threatens to upend this business model. Agents can not only free user attention with autonomy across diverse platforms and therefore bypass the user-attention-based monetization, but might also become the new entrance for digital traffic. Hence, we argue that superplatforms have to attack AI agents to defend their centralized control of digital traffic entrance. Specifically, we analyze the fundamental conflict between user-attention-based monetization and agent-driven autonomy through the lens of our gatekeeping theory. We show how AI agents can disintermediate superplatforms and potentially become the next dominant gatekeepers, thereby forming the urgent necessity for superplatforms to proactively constrain and attack AI agents. Moreover, we go through the potential technologies for superplatform-initiated attacks, covering a brand-new, unexplored technical area with unique challenges. We have to emphasize that, despite our position, this paper does not advocate for adversarial attacks by superplatforms on AI agents, but rather offers an envisioned trend to highlight the emerging tensions between superplatforms and AI agents. Our aim is to raise awareness and encourage critical discussion for collaborative solutions, prioritizing user interests and perserving the openness of digital ecosystems in the age of AI agents.
Related papers
- Agentic Web: Weaving the Next Web with AI Agents [109.13815627467514]
The emergence of AI agents powered by large language models (LLMs) marks a pivotal shift toward the Agentic Web.<n>In this paradigm, agents interact directly with one another to plan, coordinate, and execute complex tasks on behalf of users.<n>We present a structured framework for understanding and building the Agentic Web.
arXiv Detail & Related papers (2025-07-28T17:58:12Z) - Security Challenges in AI Agent Deployment: Insights from a Large Scale Public Competition [101.86739402748995]
We run the largest public red-teaming competition to date, targeting 22 frontier AI agents across 44 realistic deployment scenarios.<n>We build the Agent Red Teaming benchmark and evaluate it across 19 state-of-the-art models.<n>Our findings highlight critical and persistent vulnerabilities in today's AI agents.
arXiv Detail & Related papers (2025-07-28T05:13:04Z) - When Autonomy Goes Rogue: Preparing for Risks of Multi-Agent Collusion in Social Systems [78.04679174291329]
We introduce a proof-of-concept to simulate the risks of malicious multi-agent systems (MAS)<n>We apply this framework to two high-risk fields: misinformation spread and e-commerce fraud.<n>Our findings show that decentralized systems are more effective at carrying out malicious actions than centralized ones.
arXiv Detail & Related papers (2025-07-19T15:17:30Z) - Agentic Enterprise: AI-Centric User to User-Centric AI [13.788858905752935]
We take a closer look at the potential of AI for Enterprises, where decision-making plays a crucial and repeated role across functions, tasks, and operations.<n>We highlight six tenets for Agentic success in enterprises, by drawing attention to what the current, AI-Centric User paradigm misses.<n>In underscoring a shift to User-Centric AI, we offer six tenets and promote market mechanisms for platforms.
arXiv Detail & Related papers (2025-06-28T14:05:59Z) - Trustless Autonomy: Understanding Motivations, Benefits and Governance Dilemma in Self-Sovereign Decentralized AI Agents [14.287042083260204]
Recent trend of self-sovereign Decentralized AI Agents (DeAgents) combines Large Language Model (LLM)-based AI agents with decentralization technologies such as blockchain smart contracts and trusted execution environments (TEEs)<n>DeAgent eliminates centralized control and reduces human intervention, addressing key trust concerns inherent in centralized AI systems.<n>This study addresses this empirical research gap through interviews with DeAgents stakeholders-experts, founders, and developers-to examine their motivations, benefits, and governance dilemmas.
arXiv Detail & Related papers (2025-05-14T19:42:43Z) - Build Agent Advocates, Not Platform Agents [5.524360691653674]
Language model agents are poised to mediate how people navigate and act online.<n>If the companies that already dominate internet search, communication, and commerce control these agents, the resulting platform agents will deepen surveillance.<n>This position paper argues that we should promote user-controlled agents that safeguard individual autonomy and choice.
arXiv Detail & Related papers (2025-05-07T11:45:38Z) - Agentic AI Needs a Systems Theory [46.36636351388794]
We argue that AI development is currently overly focused on individual model capabilities.<n>We outline mechanisms for enhanced agent cognition, emergent causal reasoning ability, and metacognitive awareness.<n>We emphasize that a systems-level perspective is essential for better understanding, and purposefully shaping, agentic AI systems.
arXiv Detail & Related papers (2025-02-28T22:51:32Z) - Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions.
In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
arXiv Detail & Related papers (2024-02-28T16:09:56Z) - Killer Apps: Low-Speed, Large-Scale AI Weapons [2.2899177316144943]
Artificial Intelligence (AI) and Machine Learning (ML) advancements present new challenges and opportunities in warfare and security.
This paper explores the concept of AI weapons, their deployment, detection, and potential countermeasures.
arXiv Detail & Related papers (2024-01-14T12:09:40Z) - The Rise and Potential of Large Language Model Based Agents: A Survey [91.71061158000953]
Large language models (LLMs) are regarded as potential sparks for Artificial General Intelligence (AGI)
We start by tracing the concept of agents from its philosophical origins to its development in AI, and explain why LLMs are suitable foundations for agents.
We explore the extensive applications of LLM-based agents in three aspects: single-agent scenarios, multi-agent scenarios, and human-agent cooperation.
arXiv Detail & Related papers (2023-09-14T17:12:03Z)
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.