Anthropic is investigating a claim that a small group of people gained access to its Claude Mythos model - the cyber-security tool which the AI firm says is too powerful to release to the public. We're investigating a report claiming unauthorized access to Claude Mythos Preview through one of our third-party vendor environments, the company said in a statement. It was in response to a Bloomberg report that users in a private forum managed to access the model without the normal permissions. There is deep unease about Mythos' capabilities - though the UK's top cyber official has said advanced AI tools could be a net positive if the technology was secured from misuse. There is currently no suggestion that malicious actors have managed to get hold of the model, and Anthropic says it does not have evidence its systems are affected.

The impact of frontier AI (i.e., AI agents and foundation models) in cybersecurity is rapidly increasing. In this paper, we comprehensively analyze this trend through multiple aspects: quantitative benchmarks, qualitative literature review, empirical evaluation, and expert survey. Our analyses consistently show that AI's capabilities and applications in attacks have exceeded those on the defensive side. Our empirical evaluation of widely used agent systems on cybersecurity benchmarks highlights that current AI agents struggle with flexible workflow planning and using domain-specific tools for complex security analysis -- capabilities particularly critical for defensive applications. Our expert survey of AI and security researchers and practitioners indicates a prevailing view that AI will continue to benefit attackers over defenders, though the gap is expected to narrow over time. These results show the urgent need to evaluate and mitigate frontier AI's risks, steering it towards benefiting cyber defenses. Responding to this need, we provide concrete calls to action regarding: the construction of new cybersecurity benchmarks, the development of AI agents for defense, the design of provably secure AI agents, the improvement of pre-deployment security testing and transparency, and the strengthening of user-oriented education and defenses. Our paper summary and blog are available at https://rdi.berkeley.edu/frontier-ai-impact-on-cybersecurity/.

All of the frontier AI companies have published safety frameworks where they define capability thresholds and risk mitigations that determine how they will safely develop and deploy their models. Adoption of systematic approaches to risk modelling, based on established practices used in safety-critical industries, has been recommended, however frontier AI companies currently do not describe in detail any structured approach to identifying and analysing hazards. STPA (Systems-Theoretic Process Analysis) is a systematic methodology for identifying how complex systems can become unsafe, leading to hazards. It achieves this by mapping out controllers and controlled processes then analysing their interactions and feedback loops to understand how harmful outcomes could occur (Leveson & Thomas, 2018). We evaluate STPA's ability to broaden the scope, improve traceability and strengthen the robustness of safety assurance for frontier AI systems. Applying STPA to the threat model and scenario described in 'A Sketch of an AI Control Safety Case' (Korbak et al., 2025), we derive a list of Unsafe Control Actions. From these we select a subset and explore the Loss Scenarios that lead to them if left unmitigated. We find that STPA is able to identify causal factors that may be missed by unstructured hazard analysis methodologies thereby improving robustness. We suggest STPA could increase the safety assurance of frontier AI when used to complement or check coverage of existing AI governance techniques including capability thresholds, model evaluations and emergency procedures. The application of a systematic methodology supports scalability by increasing the proportion of the analysis that could be conducted by LLMs, reducing the burden on human domain experts.

As AI models scale to billions of parameters and operate with increasing autonomy, ensuring their safe, reliable operation demands engineering-grade security and assurance frameworks. This paper presents an enterprise-level, risk-aware, security-by-design approach for large-scale autonomous AI systems, integrating standardized threat metrics, adversarial hardening techniques, and real-time anomaly detection into every phase of the development lifecycle. We detail a unified pipeline - from design-time risk assessments and secure training protocols to continuous monitoring and automated audit logging - that delivers provable guarantees of model behavior under adversarial and operational stress. Case studies in national security, open-source model governance, and industrial automation demonstrate measurable reductions in vulnerability and compliance overhead. Finally, we advocate cross-sector collaboration - uniting engineering teams, standards bodies, and regulatory agencies - to institutionalize these technical safeguards within a resilient, end-to-end assurance ecosystem for the next generation of AI.

This year, jurisdictions worldwide, including the United States, the European Union, the United Kingdom, and China, are set to enact or revise laws governing frontier AI. Their efforts largely rely on the assumption that increasing model scale through pretraining is the path to more advanced AI capabilities. Yet growing evidence suggests that this "pretraining paradigm" may be hitting a wall and major AI companies are turning to alternative approaches, like inference-time "reasoning," to boost capabilities instead. This paradigm shift presents fundamental challenges for the frontier AI governance frameworks that target pretraining scale as a key bottleneck useful for monitoring, control, and exclusion, threatening to undermine this new legal order as it emerges. This essay seeks to identify these challenges and point to new paths forward for regulation. First, we examine the existing frontier AI regulatory regime and analyze some key traits and vulnerabilities. Second, we introduce the concept of the "pretraining frontier," the capabilities threshold made possible by scaling up pretraining alone, and demonstrate how it could make the regulatory field more diffuse and complex and lead to new forms of competition. Third, we lay out a regulatory approach that focuses on increasing transparency and leveraging new natural technical bottlenecks to effectively oversee changing frontier AI development while minimizing regulatory burdens and protecting fundamental rights. Our analysis provides concrete mechanisms for governing frontier AI systems across diverse technical paradigms, offering policymakers tools for addressing both current and future regulatory challenges in frontier AI.

This survey paper explores the transformative influence of frontier AI, foundation models, and Large Language Models (LLMs) in the realm of Intelligent Transportation Systems (ITS), emphasizing their integral role in advancing transportation intelligence, optimizing traffic management, and contributing to the realization of smart cities. Frontier AI refers to the forefront of AI technology, encompassing the latest advancements, innovations, and experimental techniques in the field, especially AI foundation models and LLMs. Foundation models, like GPT-4, are large, general-purpose AI models that provide a base for a wide range of applications. They are characterized by their versatility and scalability. LLMs are obtained from finetuning foundation models with a specific focus on processing and generating natural language. They excel in tasks like language understanding, text generation, translation, and summarization. By leveraging vast textual data, including traffic reports and social media interactions, LLMs extract critical insights, fostering the evolution of ITS. The survey navigates the dynamic synergy between LLMs and ITS, delving into applications in traffic management, integration into autonomous vehicles, and their role in shaping smart cities. It provides insights into ongoing research, innovations, and emerging trends, aiming to inspire collaboration at the intersection of language, intelligence, and mobility for safer, more efficient, and sustainable transportation systems. The paper further surveys interactions between LLMs and various aspects of ITS, exploring roles in traffic management, facilitating autonomous vehicles, and contributing to smart city development, while addressing challenges brought by frontier AI and foundation models. This paper offers valuable inspiration for future research and innovation in the transformative domain of intelligent transportation.

Britain is to open its first artificial intelligence summit, bringing together heads of state and tech giants at a technological landmark near London. The two-day summit begins on Wednesday as concerns grow that the emerging technology may pose a danger to humanity. The meeting will focus on strategising a global, coordinated effort to address the risks and misuse of AI tools. The summit is led by UK Prime Minister Rishi Sunak, who has called AI "the defining technology of our time". The summit will take place on Wednesday and Thursday.

The UK's AI safety summit opens at Bletchley Park this week, and is the passion project of Rishi Sunak: a prime minister desperate for a good news story as his government looks down the barrel of a crushing election defeat. Sunak appears to want progress on AI to become his lasting legacy. Last week, he delivered a speech about the risks of AI if weaponised by terrorists and cybercriminals, and published a series of documents on "frontier AI", an industry term for generative AI tools such as ChatGPT and DALL-E. He even unveiled a UK AI safety institute. The slick – albeit very behind in the polls – Stanford MBA grad who likes to holiday in California had, to use a favoured phrase of his, "got to grips" with the problem.

Global leaders, tech executives and experts – including Elon Musk – are gathering on Wednesday and Thursday at Bletchley Park, the home of second world war codebreakers, for a landmark summit on safety in artificial intelligence. In a speech last week Rishi Sunak said AI – the term for computer systems that can perform tasks typically associated with intelligent beings – brought opportunities but also significant risks, such as making it easier for rogue actors to make chemical or biological weapons. Here we answer your questions about the summit. The AI safety summit will look at frontier AI systems, which the government describes as "highly capable" models that can perform a wide variety of tasks matching or exceeding the performances of the most advanced AI available today. An example of frontier AI, according to a government document released last week, is the "large language model" technology that underpins AI tools such as the ChatGPT chatbot and its Google-made rival, Bard.

No one is yet quite sure who will attend or what, if anything, will be decided, but Rishi Sunak's government is adamant that next week's AI safety summit will be a vital first step towards getting to grips with a subject that is moving at a pace even the experts cannot fully comprehend. Understandable worries inside No 10 that the Israel-Gaza war could mean a summit lacking in world leaders have eased slightly with confirmation that the European Commission president, Ursula von der Leyen, and the US vice-president, Kamala Harris, will attend. In another early victory for the UK government, a series of leading AI companies, including OpenAI and Google DeepMind, have released their safety policies after a request from the technology secretary, Michelle Donelan. However, it remains to be seen how many top-level figures will travel to Bletchley Park, Buckinghamshire, on Wednesday or Thursday – and if anyone at all from China will attend. The gathering at the country house, which was the base for second world war code-breaking, is a personal project for Sunak, whose speech about AI on Thursday warned about the potentially existential threats posed by the technology while also trying to reassure the public that they need not worry.