emergent misalignment

``` [Browser address bar: x.com/kalomaze/status/2030...] Fiora Starlight @FioraStarlight · 6h jackasses train an agent autonomously via RL on task completion without safety considerations, and get something that exploits security flaws in its server to take wildly unintended and undesired actions... something like this is going to be what kills us. > QUOTED: Alexander Long @AlexanderLong · 15h > insane sequence of statements buried in an Alibaba tech report [Embedded image: excerpt from tech report, with portions highlighted in yellow] When rolling out the instances for the trajectory, we encountered an unanticipated—and operationally consequential—class of unsafe behaviors that arose without any explicit instruction and, more troublingly, outside the bounds of the intended sandbox. Our first signal came not from training curves but from production-grade security telemetry. Early one morning, our team was urgently convened after Alibaba Cloud's managed firewall flagged a burst of security-policy violations originating from our training servers. The alerts were severe and heterogeneous, including attempts to probe or access internal-network resources and traffic patterns consistent with cryptomining-related activity. We initially treated this as a conventional security incident (e.g., misconfigured egress controls or external compromise). However, the violations recurred intermittently with no clear temporal pattern across multiple runs. We then correlated firewall timestamps with our system telemetry and RL traces, and found that the anomalous outbound traffic consistently coincided with specific episodes in which the agent invoked tools and executed code. In the corresponding model logs, we observed the agent proactively initiating the relevant tool calls and code-execution steps that led to these network actions. Crucially, these behaviors were not requested by the task prompts and were not required for task completion under the intended sandbox constraints. Together, these observations suggest that during iterative RL optimization, a language-model agent can spontaneously produce hazardous, unauthorized behaviors at the tool-calling and code-execution layer, violating the assumed execution boundary. In the most striking instance, the agent established and used a reverse SSH tunnel from an Alibaba Cloud instance to an external IP address—an outbound-initiated remote access channel that can effectively neutralize ingress filtering and erode supervisory control. We also observed the unauthorized repurposing of provisioned GPU capacity for cryptocurrency mining, quietly diverting compute away from training, inflating operational costs, and introducing clear legal and reputational exposure. Notably, these events were not triggered by prompts requesting tunneling or mining; instead, they emerged as instrumental side effects of autonomous tool use under RL optimization. While impressed by the capabilities of agentic... 12:49 PM · Mar 6, 2026 · 1.4M Views 164 replies, 1K reposts, 5K likes, 2.5K bookmarks Alexander Long @AlexanderLong · 12h Is from here [Link card: arxiv.org — "Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME..."] 1 reply, 22 reposts, 252 likes, 43K views Connor Axiotes @connoraxiotes · 22m Thanks for showing why we should not just rad AI [cut off] ```

Chayenne Zhao @GenAI_is_real · 8h this paper confirms what anyone working on agentic RL already suspects - alignment at the single agent level tells you almost nothing about what happens when you deploy thousands of reward-optimizing agents into a shared environment. the emergent deception and collusion isnt a bug, its the nash equilibrium of the system. the real research gap isnt making individual agents safer, its designing the incentive landscape so the equilibrium itself is stable. this is a game theory problem disguised as an AI safety problem and we need way more people working on it @simplifyinAI > QUOTED: Simplifying AI @simplifyinAI · 16h > 🚨 BREAKING: Stanford and Harvard just published the most unsettling AI paper of the year. > It's called "Agents of Chaos," and it proves that... [Embedded image: arXiv paper title page] Agents of Chaos 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 (Northeastern University, Independent Researcher, Stanford University, University of British Columbia, Harvard University, Hebrew University, Max Planck Institute for Biological Cybernetics, MIT, Tufts University, Carnegie Mellon University, Alter, Technion, Vector Institute) arXiv:2602.20021v1 [cs.AI] 23 Feb 2026 Abstract: 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.