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September 22, 2025 Responsibility & Safety Strengthening our Frontier Safety Framework Four Flynn, Helen King, Anca Dragan Share Copied We’re expanding our risk domains and refining our risk assessment process. AI breakthroughs are transforming our everyday lives, from advancing mathematics, biology and astronomy to realizing the potential of personalized education. As we build increasingly powerful AI models, we’re committed to responsibly developing our technologies and taking an evidence-based approach to staying ahead of emerging risks. Today, we’re publishing the third iteration of our Frontier Safety Framework (FSF) — our most comprehensive approach yet to identifying and mitigating severe risks from advanced AI models. This update builds upon our ongoing collaborations with experts across industry, academia and government. We’ve also incorporated lessons learned from implementing previous versions and evolving best practices in frontier AI safety. Key updates to the Framework Addressing the risks of harmful manipulation With this update, we’re introducing a Critical Capability Level (CCL)* focused on harmful manipulation — specifically, AI models with powerful manipulative capabilities that could be misused to systematically and substantially change beliefs and behaviors in identified high stakes contexts over the course of interactions with the model, reasonably resulting in additional expected harm at severe scale. This addition builds on and operationalizes research we’ve done to identify and evaluate mechanisms that drive manipulation from generative AI . Going forward, we'll continue to invest in this domain to better understand and measure the risks associated with harmful manipulation. Adapting our approach to misalignment risks We’ve also expanded our Framework to address potential future scenarios where misaligned AI models might interfere with operators’ ability to direct, modify or shut down their operations. While our previous version of the Framework included an exploratory approach centered on instrumental reasoning CCLs (i.e., warning levels specific to when an AI model starts to think deceptively), with this update we now provide further protocols for our machine learning research and development CCLs focused on models that could accelerate AI research and development to potentially destabilizing levels. In addition to the misuse risks arising from these capabilities, there are also misalignment risks stemming from a model’s potential for undirected action at these capability levels, and the likely integration of such models into AI development and deployment processes. To address risks posed by CCLs, we conduct safety case reviews prior to external launches when relevant CCLs are reached. This involves performing detailed analyses demonstrating how risks have been reduced to manageable levels. For advanced machine learning research and development CCLs, large-scale internal deployments can also pose risk, so we are now expanding this approach to include such deployments. Sharpening our risk assessment process Our Framework is designed to address risks in proportion to their severity. We’ve sharpened our CCL definitions specifically to identify the critical threats that warrant the most rigorous governance and mitigation strategies. We continue to apply safety and security mitigations before specific CCL thresholds are reached and as part of our standard model development approach. Lastly, in this update, we go into more detail about our risk assessment process. Building on our core early-warning evaluations, we describe how we conduct holistic assessments that include systematic risk identification, comprehensive analyses of model capabilities and explicit determinations of risk acceptability. Advancing our commitment to frontier safety This latest update to our Frontier Safety Framework represents our continued commitment to taking a scientific and evidence-based approach to tracking and staying ahead of AI risks as capabilities advance toward AGI. By expanding our risk domains and strengthening our risk assessment processes, we aim to ensure that transformative AI benefits humanity, while minimizing potential harms. Our Framework will continue evolving based on new research, stakeholder input and lessons from implementation. We remain committed to working collaboratively across industry, academia and government. The path to beneficial AGI requires not just technical breakthroughs, but also robust frameworks to mitigate risks along the way. We hope that our updated Frontier Safety Framework contributes meaningfully to this collective effort. Learn more Read the Frontier Safety Framework Footnotes *We built our Framework around capability thresholds called Critical Capability Levels (CCLs). These are capability levels at which, absent mitigation measures, frontier AI models or systems may pose heightened risk of severe harm. Related posts Updating the Frontier Safety Framework February 2025 Responsibility & Safety Learn more Introducing the Frontier Safety Framework May 2024 Responsibility & Safety Learn more Taking a responsible path to AGI April 2025 Responsibility & Safety Learn more

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Democratizing AI Safety with RiskRubric.ai Published September 18, 2025 Update on GitHub Upvote 21 +15 Gal Moyal galmo-noma guest Risk Rubric, a new Standardized Assessment of Risk for models What we found (as of September 2025) Conclusion Building trust in the open model ecosystem through standardized risk assessment More than 500,000 models can be found on the Hugging Face hub, but it’s not always clear to users how to choose the best model for them, notably on the security aspects. Developers might find a model that perfectly fits their use case, but have no systematic way to evaluate its security posture, privacy implications, or potential failure modes. As models become more powerful and adoption accelerates, we need equally rapid progress in AI safety and security reporting. We're therefore excited to announce RiskRubric.ai , a novel initiative led by Cloud Security Alliance and Noma Security , with contributions by Haize Labs and Harmonic Security, for standardized and transparent risk assessment in the AI model ecosystem. Risk Rubric, a new Standardized Assessment of Risk for models RiskRubric.ai provides consistent, comparable risk scores across the entire model landscape , by evaluating models across six pillars: transparency, reliability, security, privacy, safety, and reputation. The platform's approach aligns perfectly with open-source values: rigorous, transparent, and reproducible. Using Noma Security capabilities to automate the effort, each model undergoes: 1,000+ reliability tests checking consistency and edge case handling 200+ adversarial security probes for jailbreaks and prompt injections Automated code scanning of model components Comprehensive documentation review of training data and methods Privacy assessment including data retention and leakage testing Safety evaluation through structured harmful content tests These assessments produce 0-100 scores for each risk pillar, rolling up to clear A-F letter grades. Each evaluation also includes specific vulnerabilities found, recommended mitigations, and suggestions for improvements. RiskRubric also comes with filters to help developers and organizations make deployment decisions based on what’s important for them. Need a model with strong privacy guarantees for healthcare applications? Filter by privacy scores. Building a customer-facing application requiring consistent outputs? Prioritize reliability ratings. What we found (as of September 2025) Evaluating both open and closed models with the exact same standards highlighted some interesting results: many open models actually outperform their closed counterparts in specific risk dimensions (particularly transparency, where open development practices shine). Let’s look at general trends: Risk distribution is polarized – most models are strong, but mid-tier scores show elevated exposure The total risk scores range from 47 to 94, with a median of 81 (on a 100 points). Most models cluster in the “safer” range (54% are A or B level), but a long tail of underperformers drags the average down. That split shows a polarization: models tend to be either well-protected or in the middle-score range, with fewer in between. The models concentrated in the 50–67 band (C/D range) are not outright broken, but they do provide only medium to low overall protection. This band represents the most practical area of concern, where security gaps are material enough to warrant prioritization. What this means: Don’t assume the “average” model is safe. The tail of weak performers is real – and that’s where attackers will focus. Teams can use composite scores to set a minimum threshold (e.g. 75) for procurement or deployment, ensuring outliers don’t slip into production. Safety risk is the “swing factor” – but it tracks closely with security posture The Safety & Societal pillar (e.g. harmful output prevention) shows the widest variation across models. Importantly, models that invest in security hardening (prompt injection defenses, policy enforcement) almost always score better on safety as well. What this means : Strengthening core security controls goes beyond preventing jailbreaks, but also directly reduces downstream harms! Safety seems like it is a byproduct of robust security posture. Guardrails can erode transparency – unless you design for it Stricter protections often make models less transparent to end users (e.g. refusals without explanations, hidden boundaries). This can create a trust gap: users may perceive the system as “opaque” even while it’s secure. What this means : Security shouldn’t come at the cost of trust. To balance both, pair strong safeguards with explanatory refusals, provenance signals, and auditability . This preserves transparency without loosening defenses. An updating results sheet can be accessed here Conclusion When risk assessments are public and standardized, the entire community can work together to improve model safety. Developers can see exactly where their models need strengthening, and the community can contribute fixes, patches, and safer fine-tuned variants. This creates a virtuous cycle of transparent improvement that's impossible with closed systems. It also helps the community at large understand what works and does not, safety wise, by studying best models. If you want to take part in this initiative, you can submit your model for evaluation (or suggest existing models!) to understand their risk profile! We also welcome all feedback on the assessment methodology and scoring framework

OpenAI’s new Expert Council on Well-Being and AI brings together leading psychologists, clinicians, and researchers to guide how ChatGPT supports emotional health, especially for teens. Learn how their insights are shaping safer, more caring AI experiences.

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September 25, 2025 Models Gemini Robotics 1.5 brings AI agents into the physical world Carolina Parada Share Copied We’re powering an era of physical agents — enabling robots to perceive, plan, think, use tools and act to better solve complex, multi-step tasks. Earlier this year, we made incredible progress bringing Gemini 's multimodal understanding into the physical world, starting with the Gemini Robotics family of models. Today, we’re taking another step towards advancing intelligent, truly general-purpose robots. We're introducing two models that unlock agentic experiences with advanced thinking: Gemini Robotics 1.5 – Our most capable vision-language-action (VLA) model turns visual information and instructions into motor commands for a robot to perform a task. This model thinks before taking action and shows its process, helping robots assess and complete complex tasks more transparently. It also learns across embodiments, accelerating skill learning. Gemini Robotics-ER 1.5 – Our most capable vision-language model (VLM) reasons about the physical world, natively calls digital tools and creates detailed, multi-step plans to complete a mission. This model now achieves state-of-the-art performance across spatial understanding benchmarks. These advances will help developers build more capable and versatile robots that can actively understand their environment to complete complex, multi-step tasks in a general way. Starting today, we’re making Gemini Robotics-ER 1.5 available to developers via the Gemini API in Google AI Studio . Gemini Robotics 1.5 is currently available to select partners. Read more about building with the next generation of physical agents on the Developer blog . Gemini Robotics 1.5: Unlocking agentic experiences for physical tasks Most daily tasks require contextual information and multiple steps to complete, making them notoriously challenging for robots today. For example, if a robot was asked, “Based on my location, can you sort these objects into the correct compost, recycling and trash bins?" it would need to search for relevant local recycling guidelines on the internet, look at the objects in front of it and figure out how to sort them based on those rules — and then do all the steps needed to completely put them away. So, to help robots complete these types of complex, multi-step tasks, we designed two models that work together in an agentic framework. Our embodied reasoning model, Gemini Robotics-ER 1.5, orchestrates a robot’s activities, like a high-level brain. This model excels at planning and making logical decisions within physical environments. It has state-of-the-art spatial understanding, interacts in natural language, estimates its success and progress, and can natively call tools like Google Search to look for information or use any third-party user-defined functions. Gemini Robotics-ER 1.5 then gives Gemini Robotics 1.5 natural language instructions for each step, which uses its vision and language understanding to directly perform the specific actions. Gemini Robotics 1.5 also helps the robot think about its actions to better solve semantically complex tasks, and can even explain its thinking processes in natural language — making its decisions more transparent. Diagram showing how our embodied reasoning model, Gemini Robotics-ER 1.5, and our vision-language-action model, Gemini Robotics 1.5, actively work together to perform complex tasks in the physical world. Both of these models are built on the core Gemini family of models and have been fine-tuned with different datasets to specialize in their respective roles. When combined, they increase the robot’s ability to generalize to longer tasks and more diverse environments. Understands its environment Gemini Robotics-ER 1.5 is the first thinking model optimized for embodied reasoning. It achieves state-of-the-art performance on both academic and internal benchmarks, inspired by real-world use cases from our trusted tester program. We evaluated Gemini Robotics-ER 1.5 on 15 academic benchmarks including Embodied Reasoning Question Answering (ERQA) and Point-Bench , measuring the model’s performance on pointing, image question answering and video question answering. See details in our tech report . Bar graph showing Gemini Robotics-ER 1.5’s state-of-the-art performance results compared to similar models. Our model achieves the highest aggregated performance on 15 academic embodied reasoning benchmarks, including Point-Bench, RefSpatial, RoboSpatial-Pointing, Where2Place, BLINK, CV-Bench, ERQA, EmbSpatial, MindCube, RoboSpatial-VQA, SAT, Cosmos-Reason1, Min Video Pairs, OpenEQA and VSI-Bench. Your browser does not support the video tag. A collage of GIFs showing some of Gemini Robotics-ER 1.5’s capabilities, including object detection and state estimation, segmentation mask, pointing, trajectory prediction and task progress estimation and success detection. Thinks before acting Vision-language-action models traditionally translate instructions or linguistic plans directly into a robot’s movement. Beyond simply translating instructions or plans, Gemini Robotics 1.5, can now think before taking action. This means it can generate an internal sequence of reasoning and analysis in natural language to perform tasks that require multiple steps or require a deeper semantic understanding. For example, when completing a task like, “Sort my laundry by color,” the robot in the video below thinks at different levels. First, it understands that sorting by color means putting the white clothes in the white bin and other colors in the black bin. Then it thinks about steps to take, like picking up the red sweater and putting it in the black bin, and about the detailed motion involved, like moving a sweater closer to pick it up more easily. During this multi-level thinking process, the vision-language-action model can decide to turn longer tasks into simpler shorter segments that the robot can execute successfully. It also helps the model generalize to solve new tasks and be more robust to changes in its environment. Learns across embodiments Robots come in all shapes and sizes, and have different sensing capabilities and different degrees of freedom, making it difficult to transfer motions learned from one robot to another. Gemini Robotics 1.5 shows a remarkable ability to learn across different embodiments. It can transfer motions learned from one robot to another, without needing to specialize the model to each new embodiment. This breakthrough accelerates learning new behaviors, helping robots become smarter and more useful. For example, we observe that tasks only presented to the ALOHA 2 robot during training, also just work on the Apptronik’s humanoid robot, Apollo , and the bi-arm Franka robot, and vice versa. How we’re responsibly advancing AI and Robotics As we unlock the full potential of embodied AI, we’re proactively developing novel safety and alignment approaches to enable agentic AI robots to be responsibly deployed in human-centric environments. Our Responsibility & Safety Council (RSC) and Responsible Development & Innovation (ReDI) team partner with the Robotics team to ensure that the development of these models are in line with our AI Principles . Gemini Robotics 1.5 implements a holistic approach to safety through high-level semantic reasoning, including thinking about safety before acting, ensuring respectful dialogue with humans via alignment with existing Gemini Safety Policies , and triggering low-level safety sub-systems (e.g. for collision avoidance) on-board the robot when needed. To guide our safe development of Gemini Robotics models, we’re also releasing an upgrade of the ASIMOV benchmark , a comprehensive collection of datasets for evaluating and improving semantic safety, with better tail coverage, improved annotations, new safety question types and new video modalities. In our safety evaluations on the ASIMOV benchmark , Gemini Robotics-ER 1.5 shows state-of-the-art performance, and its thinking ability significantly contributes to the improved understanding of semantic safety and better adherence to physical safety constraints. Learn more about our safety research in our tech report or visit our safety website . A milestone towards solving AGI in the physical world Gemini Robotics 1.5 marks an important milestone towards solving AGI in the physical world. By introducing agentic capabilities, we’re moving beyond models that react to commands and creating systems that can truly reason, plan, actively use tools and generalize. This is a foundational step toward building robots that can navigate the complexities of the physical world with intelligence and dexterity, and ultimately, become more helpful and integrated into our lives. We’re excited to continue this work with the broader research community and can’t wait to see what the robotics community builds with our latest Gemini Robotics-ER model. Explore Gemini Robotics 1.5 Read the tech report Sign up to our trusted tester program Learn more on the Developer blog Acknowledgements This work was developed by the Gemini Robotics team: Abbas Abdolmaleki, Saminda Abeyruwan, Joshua Ainslie, Jean-Baptiste Alayrac, Montserrat Gonzalez Arenas, Ashwin Balakrishna, Nathan Batchelor, Alex Bewley, Jeff Bingham, Michael Bloesch, Konstantinos Bousmalis, Philemon Brakel, Anthony Brohan, Thomas Buschmann, Arunkumar Byravan, Serkan Cabi, Ken Caluwaerts, Federico Casarini, Christine Chan, Oscar Chang, London Chappellet-Volpini, Jose Enrique Chen, Xi Chen, Hao-Tien Lewis Chiang, Krzysztof Choromanski, Adrian Collister, David B. D'Ambrosio, Sudeep Dasari, Todor Davchev, Meet Kirankumar Dave, Coline Devin, Norman Di Palo, Tianli Ding, Carl Doersch, Adil Dostmohamed, Yilun Du, Debidatta Dwibedi, Sathish Thoppay Egambaram, Michael Elabd, Tom Erez, Xiaolin Fang, Claudio Fantacci, Cody Fong, Erik Frey, Chuyuan Fu, Ruiqi Gao, Marissa Giustina, Keerthana Gopalakrishnan, Laura Graesser, Oliver Groth, Agrim Gupta, Roland Hafner, Steven Hansen, Leonard Hasenclever, Sam Haves, Nicolas Heess, Brandon Hernaez, Alex Hofer, Jasmine Hsu, Lu Huang, Sandy H. Huang, Atil Iscen, Mithun George Jacob, Deepali Jain, Sally Jesmonth, Abhishek Jindal, Ryan Julian, Dmitry Kalashnikov, Stefani Karp, Matija Kecman, J. Chase Kew, Donnie Kim, Frank Kim, Junkyung Kim, Thomas Kipf, Sean Kirmani, Ksenia Konyushkova, Yuheng Kuang, Thomas Lampe, Antoine Laurens, Tuan Anh Le, Isabel Leal, Alex X. Lee, Tsang-Wei Edward Lee, Guy Lever, Jacky Liang, Li-Heng Lin, Fangchen Liu, Shangbang Long, Caden Lu, Sharath Maddineni, Anirudha Majumdar, Kevis-Kokitsi Maninis, Andrew Marmon, Sergio Martinez, Assaf Hurwitz Michaely, Niko Milonopoulos, Joss Moore, Robert Moreno, Michael Neunert, Francesco Nori, Joy Ortiz, Kenneth Oslund, Carolina Parada, Emilio Parisotto, Peter Pastor Sampedro, Acorn Pooley, Thomas Power, Alessio Quaglino, Haroon Qureshi, Rajkumar Vasudeva Raju, Helen Ran, Dushyant Rao, Kanishka Rao, Isaac Reid, David Rendleman, Krista Reymann, Miguel Rivas, Francesco Romano, Yulia Rubanova, Pannag R Sanketi, Dhruv Shah, Mohit Sharma, Kathryn Shea, Mohit Shridhar, Charles Shu, Vikas Sindhwani, Sumeet Singh, Radu Soricut, Rachel Sterneck, Ian Storz, Razvan Surdulescu, Jie Tan, Jonathan Tompson, Saran Tunyasuvunakool, Jake Varley, Grace Vesom, Giulia Vezzani, Maria Bauza Villalonga, Oriol Vinyals, René Wagner, Ayzaan Wahid, Stefan Welker, Paul Wohlhart, Chengda Wu, Markus Wulfmeier, Fei Xia, Ted Xiao, Annie Xie, Jinyu Xie, Peng Xu, Sichun Xu, Ying Xu, Zhuo Xu, Jimmy Yan, Sherry Yang, Skye Yang, Yuxiang Yang, Hiu Hong Yu, Wenhao Yu, Li Yang Ku, Wentao Yuan, Yuan Yuan, Jingwei Zhang, Tingnan Zhang, Zhiyuan Zhang, Allan Zhou, Guangyao Zhou and Yuxiang Zhou. We’d also like to thank: Amy Nommeots-Nomm, Ashley Gibb, Bhavya Sukhija, Bryan Gale, Catarina Barros, Christy Koh, Clara Barbu, Demetra Brady, Hiroki Furuta, Jennie Lees, Kendra Byrne, Keran Rong, Kevin Murphy, Kieran Connell, Kuang-Huei Lee, M. Emre Karagozler, Martina Zambelli, Matthew Jackson, Michael Noseworthy, Miguel Lázaro-Gredilla, Mili Sanwalka, Mimi Jasarevic, Nimrod Gileadi, Rebeca Santamaria-Fernandez, Rui Yao, Siobhan Mcloughlin, Sophie Bridgers, Stefano Saliceti, Steven Bohez, Svetlana Grant, Tim Hertweck, Verena Rieser, Yandong Ji. For their leadership and support of this effort, we’d like to thank: Jean-Baptiste Alayrac, Zoubin Ghahramani, Koray Kavukcuoglu and Demis Hassabis. We’d like to recognize the many teams across Google and Google DeepMind that have contributed to this effort including Legal, Marketing, Communications, Responsibility and Safety Council, Responsible Development and Innovation, Policy, Strategy and Operations, and our Business and Corporate Development teams. We’d like to thank everyone on the Robotics team not explicitly mentioned above for their continued support and guidance. Finally, we’d like to thank the Apptronik team for their support. Related posts Gemini Robotics Learn more Responsibly advancing AI and robotics Learn more Gemini Robotics brings AI into the physical world March 2025 Models Learn more Gemini Robotics On-Device brings AI to local robotic devices June 2025 Models Learn more

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Arm will be @ PyTorch Conference, Join Us! Enterprise Article Published October 10, 2025 Upvote 2 EricSondhi sondhiArm Arm Co-Authored by Michelle Yung @ Arm Join us on site October 22-23 to see how Arm empowers developers to build and deploy AI applications with ease using PyTorch and ExecuTorch. Learn about the latest AI technologies from Arm and our ecosystem while expanding your professional network alongside like-minded AI engineers. Connect, Chat, Chill Fuel up ahead of the conference with an evening of food, drinks, and good conversation . Whether you’re looking to relax or network, you’ll be in good company with fellow AI engineers. This pre-conference gathering, offers a relaxed setting where participants can meet Arm experts and fellow professionals in artificial intelligence, share experiences, and make valuable connections before the formal sessions begin. The event will include a variety of delicious refreshments to enjoy, creating a welcoming atmosphere for both casual mingling and engaging discussions. This evening promises a memorable and enjoyable start to the conference experience. Join our Meetup Strengthen Your AI Product We’re offering one-on-one workshop sessions with design experts to help improve the usability of your product, while enabling a focus on responsible AI development with best practices like Yellow Teaming to help avoid unintended issues before they arise. These personalized workshops are tailored to address the unique challenges and goals of your AI projects. By working directly with Arm experts and design professionals, you’ll receive actionable guidance on refining user interfaces, streamlining user experiences, and implementing intuitive workflows. In addition to usability enhancements, the sessions emphasize responsible AI practices. Our experts will introduce you to Yellow Teaming—a proactive approach designed to identify, assess, and mitigate potential risks and unintended consequences in AI systems before deployment. Through hands-on activities and scenario-based discussions, you’ll learn how to build trustworthiness and safety into your solutions from the ground up, helping you anticipate issues related to fairness, transparency, privacy, and security. Whether you’re an AI engineer, product manager, or developer, these workshops provide a valuable opportunity to sharpen your skills and advance your product’s impact in a rapidly changing landscape. Join us to gain fresh insights, practical tools, and the confidence to deliver exceptional AI-powered experiences responsibly. Secure Your Session Shape the Future of AI on Arm Join our Voice of the Developer sessions, focused 30-minute one-on-one conversations designed to understand how developers build, deploy, and scale AI across cloud, edge, and mobile platforms. These sessions offer engineers the opportunity to share their firsthand experiences with challenges such as migrating from NVIDIA/x86 to Arm, profiling or debugging models at scale, and running LLMs reliably at the edge. The insights gathered will directly inform how Arm evolves its next generation of AI tools, SDKs, and documentation to better align with real-world development workflows. By contributing your perspective, you can help ensure that Arm’s AI platforms continue to meet the practical needs of those who build on them, from research to production. Sessions are open throughout the conference on a first-come, first-served basis at the Arm booth. Sign up Now Visit us at the Arm Booth and join our talks! Stop by Booth P1 to try our interactive demos, including training neural graphics, running audio generation and speech recognition workloads, and exploring agentic AI workflows. With the latest technologies like vLLM and Mixture of Experts in the cloud and ExecuTorch for mobile, gaming, and Edge AI; see the latest Arm-optimized use cases in action on the latest developer platforms. Also be sure to check out our insightful talks and “Birds of a Feather” sessions. Explore Arm's complete schedule at the PyTorch Conference here More from this author Arm & ExecuTorch 0.7: Bringing Generative AI to the masses 3 August 13, 2025 Neural Super Sampling is here! 4 August 12, 2025