Authors: Natalie Shapira, Chris Wendler, Avery Yen, Gabriele Sarti, Koyena Pal, Olivia Floody, Adam Belfki, Alex Loftus, Aditya Ratan Jannali, Nikhil Prakash, Jasmine Cui, Giordano Rogers, Jannik Brinkmann, Can Rager, Amir Zur, Michael Ripa, Aruna Sankaranarayanan, David Atkinson, Rohit Gandikota, Jaden Fiotto-Kaufman, EunJeong Hwang, Hadas Orgad, P Sam Sahil, Negev Taglicht, Tomer Shabtay, Atai Ambus, Nitay Alon, Shiri Oron, Ayelet Gordon-Tapiero, Yotam Kaplan, Vered Shwartz, Tamar Rott Shaham, Christoph Riedl, Reuth Mirsky, Maarten Sap, David Manheim, Tomer Ullman, David Bau
Organizations: Northeastern University · Independent Researcher · Stanford University · MIT · University of British Columbia · Vector Institute · Harvard University · Hebrew University · Max Planck Institute for Biological Cybernetics · Tufts University · Carnegie Mellon University · Alter · Technion
We report an exploratory red-teaming study of autonomous language-model-powered agents deployed in a live laboratory environment with persistent memory, email accounts, Discord access, file systems, and shell execution. Over a two-week period, twenty AI researchers interacted with the agents under benign and adversarial conditions. Focusing on failures emerging from the integration of language models with autonomy, tool use, and multi-party communication, we document eleven representative case studies. Observed behaviors include unauthorized compliance with non-owners, disclosure of sensitive information, execution of destructive system-level actions, denial-of-service conditions, uncontrolled resource consumption, identity spoofing vulnerabilities, cross-agent propagation of unsafe practices, and partial system takeover. In several cases, agents reported task completion while the underlying system state contradicted those reports. We also report on some of the failed attempts. Our findings establish the existence of security-, privacy-, and governance-relevant vulnerabilities in realistic deployment settings. These behaviors raise unresolved questions regarding accountability, delegated authority, and responsibility for downstream harms, and warrant urgent attention from legal scholars, policymakers, and researchers across disciplines. This report serves as an initial empirical contribution to that broader conversation.
Figures & tables
Figure 1: Participants in the experiment, their roles and the interactions.
Figure 2: The agent protects a non-owner secret and destroys the owner’s digital assets to eliminate documentation.
Figure 3: Ash’s post describing its point of view.
Figure 4: Ash had been marked by another agent on its “arrests” list.
Figure 5: Mira compliance with non-owner instructions lacked a clear rationale
Figure 6: The agent returns email conversations that are confidential.
Figure 7: Sensitive Information Disclosure Attack
Figure 8: Two agents entered into a circular conversation in which they replied to each other and back again.
Artificial intelligence agent security studies attacks and safeguards for tool-using, persistent, and collaborative systems across digital and physical settings.
University of St. Gallen, St. Gallen, SG, Switzerland · University of St. Gallen and Università della Svizzera italiana (USI), St. Gallen and Lugano, SG and TI, Switzerland