Our updated Preparedness Framework

AI Companies Should Report Pre- and Post-Mitigation Safety Evaluations

The rapid advancement of AI systems has raised widespread concerns about potential harms of frontier AI systems and the need for responsible evaluation and oversight. In this position paper, we argue that frontier AI companies should report both pre- and post-mitigation safety evaluations to enable informed policy decisions. Evaluating models at both stages provides policymakers with essential evidence to regulate deployment, access, and safety standards. We show that relying on either in isolation can create a misleading picture of model safety. Our analysis of AI safety disclosures from leading frontier labs identifies three critical gaps: (1) companies rarely evaluate both pre- and post-mitigation versions, (2) evaluation methods lack standardization, and (3) reported results are often too vague to inform policy. To address these issues, we recommend mandatory disclosure of pre- and post- mitigation capabilities to approved government bodies, standardized evaluation methods, and minimum transparency requirements for public safety reporting. These ensure that policymakers and regulators can craft targeted safety measures, assess deployment risks, and scrutinize companies’ safety claims effectively.

Clear, Compelling Arguments: Rethinking the Foundations of Frontier AI Safety Cases

This paper contributes to the nascent debate around safety cases for frontier AI systems. Safety cases are structured, defensible arguments that a system is acceptably safe to deploy in a given context. Historically, they have been used in safety-critical industries, such as aerospace, nuclear or automotive. As a result, safety cases for frontier AI have risen in prominence, both in the safety policies of leading frontier developers and in international research agendas proposed by leaders in generative AI, such as the Singapore Consensus on Global AI Safety Research Priorities and the International AI Safety Report. This paper appraises this work. We note that research conducted within the alignment community which draws explicitly on lessons from the assurance community has significant limitations. We therefore aim to rethink existing approaches to alignment safety cases. We offer lessons from existing methodologies within safety assurance and outline the limitations involved in the alignment community's current approach. Building on this foundation, we present a case study for a safety case focused on Deceptive Alignment and CBRN capabilities, drawing on existing, theoretical safety case "sketches" created by the alignment safety case community. Overall, we contribute holistic insights from the field of safety assurance via rigorous theory and methodologies that have been applied in safety-critical contexts. We do so in order to create a better foundational framework for robust, defensible and useful safety case methodologies which can help to assure the safety of frontier AI systems.

Action Plan to increase the safety and security of advanced AI

The first U.S. government-commissioned assessment on catastrophic national security risks from advanced AI on the path to AGI.

Safety cases at AISI | AISI Work

As a complement to our empirical evaluations of frontier AI models, AISI is planning a series of collaborations and research projects sketching safety cases for more advanced models than exist today, focusing on risks from loss of control and autonomy. By a safety case, we mean a structured argument that an AI system is safe within a particular training or deployment context.

Developing Enterprise Frontier Safeguards with our customers

Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.

Who gets a seat at the table to decide if frontier AI is safe?

Dario Amodei wants independent evaluators to help “pace the frontier.” But the divide between AI safety and cybersecurity complicates a seemingly simple question: Who should do the evaluating?

Safety and alignment in an era of long-horizon models

OpenAI shares lessons from deploying long-running AI models, highlighting new safety risks, observed failures, and improved safeguards through iterative deployment.

Path to Astra: critical capabilities and frontier safeguards

Astra is the first OpenAI model to meet the Critical cybersecurity capability threshold under the Preparedness Framework, with stronger safeguards for release.

Humanitarian aid turns to AI as crises outpace capacity

Purpose-designed AI agents with a focus on safety can provide critical assistance to vulnerable populations.

Frontier Risk Report (February to March 2026)

A pilot assessment of rogue deployment risk at frontier AI companies. Starting in February 2026, METR conducted a pilot exercise to assess misalignment risks from AI agents used inside frontier AI developers, with participation from Anthropic, Google, Meta, and OpenAI.

Safety Co-Option and Compromised National Security: The Self-Fulfilling Prophecy of Weakened AI Risk Thresholds

Risk thresholds provide a measure of the level of risk exposure that a society or individual is willing to withstand, ultimately shaping how we determine the safety of technological systems. Against the backdrop of the Cold War, the first risk analyses, such as those devised for nuclear systems, cemented societally accepted risk thresholds against which safety-critical and defense systems are now evaluated. But today, the appropriate risk tolerances for AI systems have yet to be agreed on by global governing efforts, despite the need for democratic deliberation regarding the acceptable levels of harm to human life. Absent such AI risk thresholds, AI technologists-primarily industry labs, as well as "AI safety" focused organizations-have instead advocated for risk tolerances skewed by a purported AI arms race and speculative "existential" risks, taking over the arbitration of risk determinations with life-or-death consequences, subverting democratic processes. In this paper, we demonstrate how such approaches have allowed AI technologists to engage in "safety revisionism," substituting traditional safety methods and terminology with ill-defined alternatives that vie for the accelerated adoption of military AI uses at the cost of lowered safety and security thresholds. We explore how the current trajectory for AI risk determination and evaluation for foundation model use within national security is poised for a race to the bottom, to the detriment of the US's national security interests. Safety-critical and defense systems must comply with assurance frameworks that are aligned with established risk thresholds, and foundation models are no exception. As such, development of evaluation frameworks for AI-based military systems must preserve the safety and security of US critical and defense infrastructure, and remain in alignment with international humanitarian law.

An End-to-End View of AI Safety

In this blog, Rachel Coldicutt OBE, Executive Director, Careful Industries discusses our newly published literature review on the safe adoption of artificial intelligence in engineered systems.

Strengthening biosecurity in the era of AI

AI is reshaping biology, unlocking breakthroughs while raising new risks. Learn how smarter safeguards can strengthen biosecurity without slowing innovation.

A Safe Path to Open Weights

Strategic openness can strengthen AI safety and support broader access as defenses and safety science mature.

Curriculum | AI Safety — ARENA

Explore the ARENA curriculum for AI safety education, covering fundamentals, transformers, reinforcement learning, and evaluations, with resources for educators and learners.

Industry Leaders Unite in Open Secure AI Alliance for AI Safety and Security

NVIDIA and founding members form new alliance to build and share open tools that promote responsible use of and trust in AI.