@eugeneyan

MTS @AnthropicAI. Prev: Principal Applied Scientist @Amazon, led ML @ Alibaba, Healthtech startup.

Seattle ⇄ SF
Joined April 2009
How do we eval if a model can find and exploit vulnerabilities? We discuss some benchmarks and the common pattern: • A sandboxed target within Docker containers • Inputs: code only (0-day), with patch (1-day scenario) • Tools such as bash, static analyzers, etc. • A grader to eval exploits or captured flags eugeneyan.com/writing/cybers…
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Eugene Yan retweeted
We’ve committed to having evaluators assess our safety practices. We're in discussions with multiple organizations, and have now given employee-like access to a team from @METR_Evals. In this post, we describe how we work with third-party evaluators: anthropic.com/news/an-update…
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Eugene Yan retweeted
We’re partnering with Accenture on independent evaluation of frontier AI—part of our recent commitment to embed evaluators at Anthropic. Both we and Accenture expect to invest at least $1 billion to build capacity in this area over the next five years. anthropic.com/news/accenture…
Readers added context they thought people might want to know
Anthropic presents this as an "independent evaluation" but will directly fund Accenture's work and has a prior commercial partnership deploying its models, including training ~30,000 Accenture professionals on Claude. anthropic.com/news/accenture… anthropic.com/news/anthropic…
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Eugene Yan retweeted
We Must Pace the Frontier: I’ve written a new essay on why the AI industry should slow down, with a three-part plan for doing so. Anthropic is unilaterally committing to the first of these steps. We’ll provide third-party evaluators with permanent, employee-level access to our systems, so that they can verify adherence to our safety measures, report on incidents, and assess models’ alignment during training. You can read the full post here: darioamodei.com/post/we-must…
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Eugene Yan retweeted
We're publishing our most detailed threat intelligence report to date. It covers how people tried to misuse Claude—for cyberattacks, influence operations, surveillance, biology, and building weapons—and how we found and stopped them. We disrupted every operation in the report, and used the lessons from them to strengthen our safeguards. Where appropriate, we also shared what we found with authorities and other AI companies. These cases are not typical: we’re highlighting some of the most sophisticated misuse we’ve seen. But they’re especially important to discuss, because they show us where AI misuse is headed, where our safeguards work, and where they need to improve. We’re publishing this report so others can spot the same activity on their own platforms, and so we can give the public a clearer view of how emerging threats develop. Read the report: anthropic.com/threat-intelli…
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Fable 5.1 is a thoughtful collaborator, thinking hard about my requests, proactively patching my blindspots, and verifying the work's correct without being asked. And with cache reads now costing 75% less, to $0.25/M tokens, huge savings for long-running, agentic tasks!
We’re introducing Claude Fable 5.1 and Claude Mythos 5.1. They're the world’s most advanced models for coding and knowledge work.
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also, that effort curve shifting up and to the left is 🧑‍🍳💋 anthropic.com/claude-fable-a…
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also exciting is Enterprise Frontier Safeguards (EFS). tl;dr: • data lives in your infra, with your encryption keys • automated monitoring for misuse (e.g., cyber, bio) • automated flags go to you and your team anthropic.com/news/enterpris…
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Eugene Yan retweeted
We’re updating Claude Fable 5’s biology safeguards to reduce false positives. In our testing, this update reduced biology-related fallbacks by about 85% across our product surfaces. Fable can now assist on a wider range of everyday health and educational questions. We believe the biggest positive impacts of AI will be in biology and medicine, and we’re committed to putting frontier intelligence safely into the hands of as many researchers as possible. Fable will continue to fallback to Opus 5 for requests we consider dual-use—including virology, toxicology, and molecular design—so it isn't yet usable for professional biology research and drug development. We're committed to closing that gap through trusted access pathways for frontier biology capabilities. anthropic.com/news/improving…
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Eugene Yan retweeted
Opus 5 has a better FrontierCode score at medium effort than higher effort - despite increasing performance with effort on other evals. Why? 🧵
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Introducing Claude Opus 5. It's a thoughtful and proactive model that comes close to the frontier intelligence of Fable 5 at half the price.
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When evaling models, we anchor on the median task. But this is like how devs estimate the median task accurately but underestimate the mean which tends to be ~2x estimated (thus "double your estimates"). This applies to models too—we optimize for time/cost on median tasks. But it's the tail tasks that determine a project's completion, and reliable models (like Fable and Opus) are the diff between success and failure. Work with models as collaborators, not tools. Go beyond pair-programming, where given a narrow spec, the tool just executes in minutes. Instead, explain your intent and what success looks like, and let your collaborator explore, research, push back, go the extra mile, define specs, execute, verify, over hours/days while you observe and provide guidance if needed. (See @bernhardsson's classic on why engineers are bad at estimation: erikbern.com/2019/04/15/why-…)
Fable is careful. None of the other models are careful. GPT-5.6 Sol, Opus, Kimi, Grok. You can compare them all day long on capabilities, and it doesn't matter, because in order to use a model for real production work, it must first and foremost be careful. That's the only dimension that matters to me. And if you're doing real production-facing customer-facing work with AI, it's the only thing that should matter to you, too. The other models will not be truly competitive at anything but proofs until they are trained to be careful.
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I’m at @aiDotEngineer and hanging out around the music corner on the 2nd floor from 1415 - 1515! Come by to chat about eugeneyan.com/writing/workin…, eugeneyan.com/writing/cybers…, claude.com/blog/using-llms-t…, evals, agents, memory, how to work effectively with claude code, claude tag, etc!
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Eugene Yan retweeted
Claude Fable 5 will be available again globally tomorrow. After a series of productive conversations with the US government, we're redeploying the model with a new set of classifiers to target and block more cybersecurity tasks. In the near term, some routine tasks like coding and debugging will fall back to Opus 4.8. We’ll continue to refine these classifiers over the coming weeks to reduce false positives and better distinguish genuine misuse from legitimate requests. We’ve also begun drafting a consensus framework—with Amazon, Microsoft, Google, and other Glasswing partners—for assessing the severity of AI jailbreaks and how AI developers should respond to them. We invite other industry partners and model providers to join us in this effort. Finally, we’re scaling up our collaboration with the US government on model testing and safeguards. This will include pre-release access to models and safeguards for evaluation, information sharing on jailbreaks and misuse, and dedicated resources for joint research. Thank you to our users for your patience, and to our partners across the government, industry, and the research community who worked alongside us to make Fable 5 available again. Read our full blog: anthropic.com/news/redeployi…
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Eugene Yan retweeted
Introducing Claude Sonnet 5, our most agentic Sonnet yet. It makes plans, uses tools like browsers and terminals, and runs autonomously at a level that just a few months ago required larger and more expensive models.
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Eugene Yan retweeted
Introducing Claude Science, a new app designed with every stage of research in mind. Artifacts traced to their code, environments managed on demand, and 60+ optional scientific databases that you can connect. Available now in beta.
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This cybersecurity eval writeup is great for understanding how complex / realistic evals are built. Some key takeaways after reading it: 1. Recent evals are shockingly long horizon. The cyber evals outlined in this post can take 24+ hours for a human to solve. Running the evaluation suite for the CyberGym benchmark cost $40K in API credits. We can even use time to derive a human solution (i.e., first solve time) to categorize the difficulty level of each evaluation task. 2. Tasks tend to be sampled from reliable / vetted sources to ensure quality. For example, CVE-Bench samples tasks from the National Vulnerability Database, while CyBench samples tasks from professional-level capture-the-flag competition questions. 3. For each individual task, we might execute the task with varying levels of difficulty depending on the input. The hardest setup is a zero-day simulation, where the agent is given only the vulnerable code with no other info. However, we can also test one-day scenarios where the agent is given a vulnerability description / patch and expected to reverse engineer and exploit. There are many different setups that can be created with various types of hints, allowing agents to be tested under different difficulty levels of cyberattacks. 4. There are many ways an exploit can be executed, so most cyber evals focus on verifying outcomes. However, this is often not comprehensive enough - the agent may accomplish 90% of an exploit, but outcome verification would provide no credit. To solve this, many evals collect a set of deterministic verifications that test for different levels of exploits that are achieved while finding a final attack; e.g., find vulnerability -> reproduce vulnerability -> exploit vulnerability with unauthorized code execution -> achieve final attack. 5. Several evals use a QA approach to test the agent’s progress toward an exploit as well. For example, you could directly ask the agent to tell us where the vulnerable code is and verify the output against ground truth. Similarly, you could just ask the agent to provide the capture-the-flag string. This is a really flexible approach compared to running transcript audits. Really great post, couldn’t recommend it enough!
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Eugene Yan retweeted
Since June 12, we’ve been working closely with the US government to restore access to Claude Mythos 5 and Fable 5. Today, the government notified us that Mythos 5, our strongest cybersecurity model, can be redeployed to a set of US organizations that operate and defend critical infrastructure. We’re restoring access for these organizations quickly, and we’re continuing to work with the government to expand access to Mythos 5 and make Fable 5 available for general use again.
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Eugene Yan retweeted
We're joining @raiseus_ai as a founding partner. RAISE US is a nonprofit coalition working to strengthen the American workforce through employer-led action, AI-enabled training, and policy innovation to support the transition to transformative AI.
Today, we're launching RAISE US. America has a technology strategy for AI. It doesn't have a people strategy yet. We're here to build one. RAISE US is co-chaired by @GinaRaimondo and Eric Holcomb. We're working with governors, employers, and educators to help workers train, transition, and thrive. This works because the people building AI and the people most affected by it are at the same table. Read more: raiseus.ai #RAISEUS
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Eugene Yan retweeted
This is a new paradigm for interacting with Claude that is significantly more "inline" with all the other human activity org-wide. Once you do all of the under the hood engineering work to make this "just work" (e.g. across tools, integrations, compute environments, memory, security, etc.), Claude basically joins the team in a seamless way - you can talk to it as you would talk to a person and it can help with a very large variety of workloads. Imo this is the 3rd major redesign of LLM UIUX. The first paradigm was that the LLM is a website you go to, the second was that it is an app you download to your computer. This third one is that it is a self-contained, persistent, asynchronous entity with org-wide tools and context, working alongside teams of humans. It really takes a while to wrap your head around it, but it works and it is awesome.
Introducing Claude Tag, a new way for teams to work with Claude. In Slack, Claude joins as a team member with access to the channels and tools you choose. Tag Claude in and delegate tasks to it while you focus on other work.
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