Min-Si Wang @minsiw

Zentropi, a specialized classification model for moderation: blog.zentropi.ai/how-koko-uses-…

Introducing Bread, a wallet by Miden

Bread @joinbread

7 days ago

Privacy made simple. Early access 👉 miden.xyz/bread

125 71 491 82K 153

🤯🤯🤯

Zodiac @zodiaceco

3 weeks ago

Incredible work by the @Veritylab team, formally verifying one of the most complex and security-critical components of our latest Roles Modifier. We're excited to expand formal verification across Zodiac contracts, so you can transact with your Safe with the highest level of

0 3 16 3K 2

Today we’re announcing a historic milestone: The first ever double-blind evaluation of a proprietary language model. This was made possible by a unique collaboration between AVERI, @GoogleDeepMind, @OpenMinedOrg, and @MLCommons. We tested Gemini 2.5 Flash-Lite using never-before-used prompts from the MLCommons safety benchmark family, AILuminate, inside a secure enclave, a form of hardware isolation that protects sensitive computations. Each organization involved played a unique role in making strong privacy guarantees possible. High-stakes independent evaluation runs into a structural problem: developers and evaluators both hold assets they have good reason to protect. Developers sharing model weights risk theft and leakage. Evaluators who publish benchmarks or provide them directly to companies run the risk of them being trained against, and the benchmark then stops being independent. Secure enclaves address this constraint. The hardware attests to exactly what code will run before either party's assets enter, and both sides review and approve that code. Then the enclave executes it, releasing only the agreed outputs to the agreed recipients. A 2024 pilot by OpenMined, @Anthropic, and the @AISecurityInst proved this mechanism, using GPT-2 as a stand-in and a five-row eval. The pilot we’re announcing today moved to a production model and a more comprehensive evaluation. AVERI encrypted the prompts using a private key that no other party could see, then AVERI and Google DeepMind jointly ran the evaluation, using software originally produced and adapted by OpenMined, in an enclave environment configured by Google. Finally, AVERI alone decrypted the outputs and graded them with the AILuminate benchmark criteria to inform a qualitative and (small-scale) quantitative assessment of the model properties. Today, companies provide contractual commitments to not monitor certain interactions with their models – including evaluations conducted by third party evaluators. But stronger, technically-backed guarantees could provide greater assurance that those commitments are being honored, and will be especially valuable for scenarios such as international verification. These results come at a critical time in the development of AI policy. Laws like SB 315 and standards like the EU’s General-Purpose AI Code of Practice rightly demand that security and privacy be respected in the process of third-party assessments, but provide little guidance on how to achieve this. Policymakers considering audit requirements should feel encouraged by these results to be ambitious in requiring that deep, secure access be provided. Secure enclaves are just one of many technologies under rapid development that can help enable such access. Furthermore, a demand signal from lawmakers will further accelerate the maturation of such technologies, along with complementary “low-tech”approaches such as embedding auditors within companies. Today also marks the beginning of a more public phase for AVERI’s pilot work. AVERI's strategy is a flywheel: we conduct pilot audits with leading AI companies, carry out technical and policy research to inform audit design, and convert the insights of each into open source tools, industry-wide auditing standards, and policy analysis. Over time, we hope for improved standards, stronger policy demand signals, and tooling to enable even more ambitious pilots and more informed research – bringing us closer to our mission of making frontier AI auditing effective and universal. This pilot is one of several underway, with more findings to follow in the coming months. We are grateful to our collaborators on this pilot for working with us to advance the frontier of AI governance. Read more in our blog post and joint technical report. AVERI blog post: averi.org/ourwork/averi-… Joint technical report: storage.googleapis.com/deepmind-media…

Google DeepMind @GoogleDeepMind

4 weeks ago

In an industry first, we’re piloting double-blind evaluations for frontier AI. By creating a secure environment where neither test prompts nor model weights are revealed, we can ensure external safety and performance evaluations of our models remain private, robust, and

121 102 855 352K 199

@auryn_macmillan @clickskeyboard @MinimalCompany Love this

Most AI evaluations focus on an individual agent, even though many key threat models and safety-critical domains involve multiple interacting agents. We’re excited to be supporting Orbit, a new framework (built on Inspect) to help researchers address this gap.

Ben Hagag @BenHagag20

2 months ago

Today we're releasing Orbit v0: a framework for multi-agent safety and security research. If you've tried running multi-agent experiments, you know the drill - weeks of infra before your first result. Built on Inspect, Orbit lets you swap environments, threat models, and

3 3 47 6K 17

New @StoffelMPC article coming this week. There's about 2-3 articles left to round out our introductory MPC series before moving onto more advanced concepts 🥳 Can't wait to talk more about our VM!

x.com/i/article/2084…

@0xHermes_ Very cool

1/7 Published #COLM2026 🎉 🚨 Can LLMs hide harmful behaviour from white-box monitors? Yes — they relocate it into subspaces the monitor isn't watching, and we show mechanistically how. Our monitors catch them with 97–100% accuracy, where prior methods fall below 10%. 🧵

Agent’s Last Exam (agents-last-exam.org) evaluates on LT, economically viable tasks across 13 industries-> avg full of pass rate is 2.6% for hardest tier

Chat controls passing today should be a huge wake-up call; a reminder that privacy is a fundamental human right. Plausible deniability and receipt-freeness are two of the most underrated features enabled by modern cryptography! This is why we built @theInterfold!

Notes from AI Security Day 2026 aisafetynode.com/system/

@badcryptobitch Drop by Berlin next time:)

Hmm. Data “officially” a factor of production

I’ve left Google DeepMind after an amazing chapter. I’m incredibly grateful for the people I worked with, the things we built, and the lessons I learned from taking frontier AI research into production. DeepMind shaped how I think about research, product, evaluation, and what it takes to build AI systems at real scale. As I wrap up this chapter, I wrote down something I’ve been thinking about a lot: evals. We’re good at evaluating the models we have. We’re much worse at evaluating the models we’re about to build — especially if they cross into a new capability regime. We will have self-evolving models, but before that, we need self-evolving evaluations. wanglun1996.github.io/blog/your-eval…

Thanks @Marcello_AI for joining for AI Security part of the discussions at @themu_xyz . A few safety concerns that stood out from the talk: 1. The need for identity infra to prevent manipulation and Sybil attacks 2. Jailbreaks in economic setting at machine speed makes policy violations difficult to manage in agentic environments 3. Accountability and governance is esp difficult wrt fulfillment of regulatory needs

Marcello Politi @Marcello_AI

4 months ago

Just spoke at @themu_xyz about the Agentic Economy and #AI Safety. My take is that Agentic Economy will run on #Web3 infra, Ethereum is an ideal environment, agents can transact, coordinate, and own assets natively and programmatically. But multi-agent systems introduce

0 0 4 241 0

Building an AI-native @Coinbase means rebuilding everything, especially the hardest parts. We've put a lot of time into redefining compliance, where the stakes are incredibly high, and we have to be extremely thoughtful about implementation. We have invested heavily in rebuilding our compliance ops around AI with that reality as our starting constraint, not an afterthought. Here is an overview of what we've learned and what we built. Most people assume compliance work is mostly checking whether a name appears on a sanctions list. That is the easy 5%. The other 95% is interpretive judgment under uncertainty: a customer claims their wealth came from real estate. Do the property records actually support it? Does the timeline hold? Is the documentation legitimate, or does it feel too polished? You need compliance staff and investigators who understand what “suspicious” actually looks like in context. That's part of why compliance is so hard to automate—and so expensive. The first obvious AI approach is to hand the model the existing procedures and ask it to run them faster. That approach misunderstands what procedures are for. Good procedures are not bad investigations; they are deliberately incomplete investigations. Their job is to create consistency, auditability, and a minimum standard across thousands of cases. They excel at saying what must happen. They are far worse at capturing everything a strong analyst actually notices: which sources they trust, when they widen the search, when a document feels off, when an explanation technically fits but still does not feel earned. Procedures also carry the shape of the old operating model: fragmented systems, time pressure, queue pressure, and the hard limit of how much one human analyst can read, cross-reference, and hold in working memory at once. That is not a flaw in the procedure. It is how you design a process for humans. AI changes the constraint set. Reading, searching, comparing documents, and tracing inconsistencies no longer have to be treated as scarce analyst time. Done carefully, with proper controls and human review, models can explore more context, test more hypotheses, and surface more inconsistencies than any single analyst could reasonably do case by case. So if you simply automate the procedure exactly as written, you may gain efficiency. You will not unlock the full value of AI. You will just make the old bottleneck run faster. The better question is not “Can AI follow the analyst playbook?” It is: once the cost of reading, cross-referencing, and testing hypotheses collapses, what should the investigation become? A second tempting approach: feed it historical Suspicious Activity Reports (SARs) and let it learn from outcomes. This breaks down too. You rarely have the full state of what the analyst actually saw during the investigation. A case that looks straightforward today might only look that way because information surfaced later. A fraud indictment that didn't exist when the original analyst made the call, news articles that hadn't been published yet. Hindsight can contaminate your training data. Also, regulators themselves acknowledge that SAR decisions can be subjective. The architecture has four layers. The first is data: continuously enhancing the coverage, quality, and architecture of the signals the system depends on. The second is classical machine learning models that cluster and classify alerts to determine what type of investigation needs to run. The third is the investigation agent itself: a multi-agent system that orchestrates specialized agents to execute the investigation end to end. The fourth is a safety filter that runs independently of typology, ensuring no risk vector is missed regardless of how the alert is classified. Each layer is independently auditable and learns from the feedback provided by human reviewers. Inside the investigation agent, specialized sub-agents run across the full case surface: alert context, customer and identity signals, access patterns, risk indicators, transaction behavior, source-of-funds, onchain activity, and public adverse media. Each writes its findings into a shared case memory. A coordinator agent reconciles and challenges them. When sub-agents disagree, such as when source-of-funds marks activity as “explained” while adverse media surfaces a recent indictment, the coordinator attempts to resolve these disagreements knowing the common patterns. The narrative agent prepares the final report with all collected evidence and suggested resolution. The last self-validation agent acts as a guardrail: if the system cannot support its conclusion with sufficient confidence or data quality, the case is routed to manual investigation instead of being surfaced as an automated result. Before any of this touched a real customer case, we built what we call a “Golden Set” - historical cases with known right answers. "Known right answers" in compliance is harder than it sounds. It meant re-investigating old cases, getting multiple senior analysts to independently agree on what the right call would have been, then debating the disagreements until consensus. Months of work before we could even start measuring. Here's an important part (for now) - cases currently get BOTH the AI's full investigation AND a senior human review. We didn't reduce scrutiny, in fact, we added more of it until it no longer proves valuable. Cases resolve significantly faster AND get more eyes than they ever did before. Every human correction feeds back into the model as a training signal. It gets better because it's wrong in front of people who know how to fix it. None of this would have shipped without clearing structural blockers most financial institutions are still stuck on. Security and privacy sign-off to send customer data to LLMs at all. Senior compliance officer alignment on AI-assisted human decision making. Model Governance team embedded since December - they observed the entire Golden-Set Evaluation process and are running a formal validation review with our Internal Audit team now. Today this handles roughly 55% of our US fraud case volume with significantly less analyst time per case. Time freed goes to the harder cases AI can't yet handle - and to teaching it. Our internal compliance and quality teams are the ones who are building this system with the engineers, training it, validating it, and continuing to shape how it improves. In the process, they've developed skills that are incredibly valuable: how to design evals, how to think about model bias, how to think about human bias, how to architect human-in-the-loop systems, skills that are becoming among the most valuable at any company. This entire project started ~6 months ago with a whiteboarding session between @galpa42 and I, and was built by an AI-pilled cross-functional and it’s just the first pod - there's a multi-month roadmap,rebuilding compliance from the ground up with AI. Huge thanks to everyone involved and congratulations to @galpa42 for shipping two babies to production this month :) The future of high-stakes work is not AI replacing judgment. It is AI making judgment scalable, auditable, and continuously improvable.

Introducing Privacy Cash SDK for Base, audited by @zigtur and @hashcloak. Integrate privacy into your product with just a few lines of code, for private transfers and bridging. Start building now. github.com/Privacy-Cash/p…

How Roost 🐓work with ai agents as part of their open source development in complex safety systems. Looking fwd to learning more soon. #aisecurity roost.tools/blog/navigatin…