Introducing AlphaProof Code: a breakthrough in verifying complex software at depth

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Introducing AlphaProof Code: a breakthrough in verifying complex software at depth

How Google DeepMind researchers built a new agentic framework that formally verified the code of Signal’s cryptographic protocol — long considered to be a grand security challengeToday, we’re sharing a milestone in software security long considered to be a grand security challenge: Google DeepMind researchers guided AI agents to mathematically prove key security properties of the actual code underlying the Signal protocol, the technology that protects billions of private conversations in apps like Signal and Google Messages.This breakthrough discovery was made using AlphaProof Code, a novel agentic framework for formal verification at unprecedented depth that combines human expertise with AI agents. Formal verification—mathematically proving code runs correctly without flaws—is the highest standard of security assurance; however, it is difficult and often labor-intensive.Our work marks the first time AI has been used to formally verify a widely deployed cryptographic protocol at this depth. It’s also the first time the implementation of a widely deployed protocol like this has been formally verified by any method, with or without AI.How we verified SignalAlphaProof Code is a multi-LLM agentic framework that splits the work of formal verification the way a research team would: human experts work with AI to decide what to prove, and a group of AI agents does it.First, our researchers collaborated with a mix of models to translate Signal’s security goals into formal mathematical specifications, with human experts reviewing every one. From there, agents powered by Gemini Carbon did the proofs, generating over six million lines of Lean, a computer language that mathematically proves the accuracy of code.Next, the agents audited the proof and highlighted areas that needed a second look by a human – just one percent of the codebase, a massive time savings. Because Lean automatically checks the rest, our experts could focus their review where human judgment matters most.Cryptographic protocols underpin nearly all digital communication, from online banking to web browsing, and a single flaw can expose millions of people at once. That’s why experts have spent decades formally analyzing them.But most protocol verification stops at the design level. Our agents went further, proving that the compiled code fulfills the key security properties – such as keeping messages protected before and after a device is temporarily compromised – down to the exact code running on devices. Because real-world vulnerabilities often hide in the gap between design and implementation, bridging this gap makes these proofs vastly more complex.Even at this complexity, our verification process took just four weeks – a task that historically takes expert teams years – proving that Signal’s core cryptographic code delivers the security protections our experts specified.To confirm that AlphaProof Code can tell correct code from buggy code, we reintroduced two real, previously fixed bugs into an isolated version of Signal’s code. In both cases, the system produced a machine-checked proof that the buggy version was insecure.

How Google DeepMind researchers built a new agentic framework that formally verified the code of Signal’s cryptographic protocol — long considered to be a grand security challenge

Today, we’re sharing a milestone in software security long considered to be a grand security challenge: Google DeepMind researchers guided AI agents to mathematically prove key security properties of the actual code underlying the Signal protocol, the technology that protects billions of private conversations in apps like Signal and Google Messages.

This breakthrough discovery was made using AlphaProof Code, a novel agentic framework for formal verification at unprecedented depth that combines human expertise with AI agents. Formal verification—mathematically proving code runs correctly without flaws—is the highest standard of security assurance; however, it is difficult and often labor-intensive.

Our work marks the first time AI has been used to formally verify a widely deployed cryptographic protocol at this depth. It’s also the first time the implementation of a widely deployed protocol like this has been formally verified by any method, with or without AI.

How we verified Signal

AlphaProof Code is a multi-LLM agentic framework that splits the work of formal verification the way a research team would: human experts work with AI to decide what to prove, and a group of AI agents does it.

First, our researchers collaborated with a mix of models to translate Signal’s security goals into formal mathematical specifications, with human experts reviewing every one. From there, agents powered by Gemini Carbon did the proofs, generating over six million lines of Lean, a computer language that mathematically proves the accuracy of code.

Next, the agents audited the proof and highlighted areas that needed a second look by a human – just one percent of the codebase, a massive time savings. Because Lean automatically checks the rest, our experts could focus their review where human judgment matters most.

Cryptographic protocols underpin nearly all digital communication, from online banking to web browsing, and a single flaw can expose millions of people at once. That’s why experts have spent decades formally analyzing them.

But most protocol verification stops at the design level. Our agents went further, proving that the compiled code fulfills the key security properties – such as keeping messages protected before and after a device is temporarily compromised – down to the exact code running on devices. Because real-world vulnerabilities often hide in the gap between design and implementation, bridging this gap makes these proofs vastly more complex.

Even at this complexity, our verification process took just four weeks – a task that historically takes expert teams years – proving that Signal’s core cryptographic code delivers the security protections our experts specified.

To confirm that AlphaProof Code can tell correct code from buggy code, we reintroduced two real, previously fixed bugs into an isolated version of Signal’s code. In both cases, the system produced a machine-checked proof that the buggy version was insecure.

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North America rig numbers dip

Total drilling activity in North America decreased slightly the week ended Nov. 6 with a total of 827 rigs working, reported Baker Hughes. A 1-rig increase in the US helped offset a 3-rig loss in Canada. The total North American rig count is down 2 units from this time last

A mole in the White House? Residential proxies are everywhere

Residential proxies are often used by bad actors to hide the true source of internet traffic — and now one of them appears to have taken up residence in the White House. The Residential Proxy Observatory, set up by Layer3 Intel to monitor the threat of anonymized network activity, has

Broadcom has quietly changed VMware availability, support for parts of Europe

Value unclear Matt Kimball, VP and principal analyst with Moor Insights & Strategy, saw the change as sufficiently limited and short-lived to make its value unclear. “This is just delaying the inevitable slightly,” he said. “This doesn’t change that inevitable migration to VCF, VVF or an alternative. If I am

Attackers turned OpsShield’s trusted update channel into a supply-chain attack

The company disclosed that, between November 1 and 2, attackers were able to gain unauthorized access to the OpsShield update distribution infrastructure, and, for about 70 minutes, pushed out a malicious update. The threat actors began by probing OpsShield’s billing portal, registering a customer account under a false identity, and

Market Focus: Balancing AI opportunities and risks in upstream operations

Robert Plumb is an executive vice-president at CAC, part of The Baldwin Group. Robert co-founded the Energy Practice of CAC Specialty in July 2016 with Josh Kirklin. Prior to joining CAC, Robert began his career as an oil and gas underwriter, working for both Travelers and the boutique energy wholesaler & MGA outfit, J.H. Blades & Co. Through these positions, he has drafted insurance policies and written thousands of General Liability, Umbrella, Well Control, Builders Risk, Loss of Production Income, Pollution, Property, Workers Compensation, and auto policies. In his underwriting career he was responsible for an aggregate portfolio of more than $500 million in gross written premium on behalf of various carriers including domestic and international markets, specifically Lloyd’s of London securities. Robert brings unique market perspective on how carriers analyze energy risks, and he specializes in property and casualty lines for on energy accounts worldwide. Robert has a bachelor’s degree in Corporate Communications and a Minor in Business Foundation from the University of Texas at Austin.

Vaalco advances to second exploration phase in block offshore Côte D’Ivoire

Vaalco Energy Inc., Houston, has been granted government approval to begin the second exploration phase at Block CI-705, offshore Côte d’Ivoire. News comes following encouraging results following completion of the first exploration phase work commitments, which included the evaluation and interpretation of existing seismic and well data, the company said in a release Nov. 9. Work on the second exploration phase includes additional seismic reprocessing, relinquishing 25% of the exploration block to the government, and commitment to drill an exploration well by yearend-2028. In March 2025, Vaalco farmed into and became operator of the exploration license with a 70% working interest. The block lies in the Ivoirien basin about 70 km to the west of Vaalco’s CI-40 Block, which hold Baobab and Kossipo oil fields. “We see encouraging prospectivity on the block in the play types proven throughout the Ivoirien Basin including both structural and stratigraphic traps in the Upper Cretaceous and Albian sections,” said George Maxwell, Vaalco’s chief executive officer. “Recent nearby discoveries are on trend with CI-705 and provide additional validation of our play concepts and our view of the block’s prospectivity,” he continued.

BSR, consortium enter talks for potential downstream collaborations

Vietnam Refining and Petrochemical Corp. (BSR) has entered talks with a consortium of FutureTec Co. and Zhejiang Hanghua Technology Co. Ltd. (Hanghua) to discuss and explore development opportunities along the refining and petrochemical value chain. At a Nov. 5 meeting between the parties, officials from the companies weighed potential collaborations on the deep processing of petroleum products, the development of value-added products, and research into related industrial infrastructure projects, BSR said in a release Nov. 6. While specific projects discussed at the meeting were not revealed, BSR said the event has laid the groundwork for further in-depth talks on potential areas of collaboration that would contributing to expanding the petrochemical refining value chain, improving resource utilization efficiency, and moving operations towards sustainable development. The proposed collaboration discussion comes amid shared interests of BSR and the FutureTec-Hanghua consortium in developing green, clean, and environmentally friendly products, as well as researching and developing new higher-value products, BSR said. With its experience in operating and managing refineries, BSR offers a favorable platform for expanding cooperation efforts with partners such as FutureTec and Hanghua that bring specialized capabilities in technology, research, and product development, added Nguyen Viet Thang, BSR’s general director. Potential collaborations with external partners come alongside BSR’s upgrading and expansion project now underway at the operator’s the 6.5-million tonne/year (tpy) refinery in the Dung Quat Economic Zone of Binh Tri Commune in Binh Son District, Quang Ngai Province, Vietnam. During the Nov. 5 meeting with the FutureTec-Hanghua consortium, BSR said its near-term plans for the refinery—which is currently operating at about 121% capacity—include further improving operational efficiency; optimizing technological processes; diversifying crude oil and product sourcing; as well as developing additional cleaner, sustainable fuels, petrochemicals, and high-value products. The operator said it will also continue focusing on efforts further efficient energy use,

Every virtual megawatt needs a meter

Last month, Google announced a deal with Voltus for a virtual power plant (VPP) of up to 1,070 MW in PJM, roughly the output of a large nuclear reactor. Rather than build a plant, Voltus will offer homes and businesses financial incentives to trim usage when the grid is strained, through smart thermostats, batteries and flexible equipment. The deal represents an inflection point for the power and utilities sector, where “speed to power” innovations are defining the moment. It also underscores the importance of the equipment that will decide how far such programs go: the electric meter. Capacity you find instead of build NEMA’s 2026 Grid Reliability Study projects U.S. economy-wide electricity demand as much as 55% by 2050, with data center consumption alone growing 300% over the next decade. New generation and transmission can’t keep pace, as permitting, capital needs and equipment and skilled labor backlogs protracting project delivery timelines. As a result, operators are looking for capacity in the system they already have. On the transmission side, grid-enhancing technologies show what’s possible: PJM modeling finds they could unlock between six and seven GW of usable capacity. At the grid edge, the opportunity may be larger. Demand response contributed 33.3 GW of enrolled wholesale capacity in 2024, about 6.5% of peak demand in organized markets and could reach 20% of U.S. peak by 2030. Behind-the-meter resources, which include microgrids and aggregations of customer-sited resources (i.e., VPPs), are scaling too. What these grid-edge resources share is a need for measurement. A demand response event or VPP dispatch only has value if someone can prove the load dropped and pay the customer who dropped it. That job falls to the electric meter. Barriers to scale Despite the momentum, only 6.5% of U.S. electricity customers are enrolled in retail demand response programs.

The next wave of energy storage capacity is already on the grid

Next week, much of the industry will gather in Las Vegas for RE+ to talk about what gets built next. It’s an important conversation, but I’d encourage asset owners to bring a second question with them: what more can we get from what’s already been built? This question matters more than ever as electricity demand climbs and customers feel it on their monthly bill. EIA expects consumption to grow another 3.1% in 2027, driven largely by data centers and other commercial loads, while residential customers will pay nearly 5% more for power this year. The industry is working hard to add capacity, but getting it built takes time. According to Lawrence Berkeley National Laboratory and GridTracker, the median project built in 2025 spent more than five years moving from interconnection request to commercial operation and only 13% of the capacity that requested interconnection between 2000 and 2020 had come online by the end of 2025. New generation is essential, but the fleet already operating is an opportunity we can’t afford to pass up. With nearly 52 gigawatts (GW) of utility-scale battery storage operating in the U.S. as of mid-2026, the installed base is too large to overlook and performance across it varies widely. When a battery energy storage system (BESS) falls short, the instinct is often to blame the battery and reach for augmentation or replacement, but in many cases the bottleneck is the controls, software and integration around it. Operating assets are under more pressure Higher prices should raise the value of delivering power when and where the grid needs it, yet revenues for many operating assets are moving in the other direction. In ERCOT, the average battery earned 84% less in 2025 than at its 2023 peak, as more storage competed for the same services. With incentive economics

Beyond megawatts: Nuclear’s other capacity expansion challenge

Nuclear growth plans are accelerating as data centers, large-load customers, uprates, restarts, extended operations and new generation projects reshape the industry’s outlook. But the challenge of capacity expansion extends far beyond megawatts. Every utility must also expand the capacity of its workforce. The workforce challenge is not simply about hiring more people. Recruiting is critical, but so are retention strategies, qualification timelines, knowledge transfer processes and the availability of experienced employees and contractors. Utilities must continue operating existing plants safely and reliably while extending asset life, modernizing infrastructure, preparing for growth and developing the next generation of workers. In many cases, the same people are asked to do all of it. An experienced engineer supporting an uprate may also be needed for aging management reviews. The supervisor helping qualify new employees may also be responsible for outage preparation. The skilled craft resources needed for capital projects are often the same resources supporting ongoing plant reliability and maintenance activities. To execute an ambitious growth plan, your workforce needs support and clear expectations as it navigates the demands of the present and the future. That reality makes employee engagement and retention critical operational necessities. Keeping today’s fleet running while preparing for tomorrow’s expansion Across the industry, organizations are managing a growing list of demands. Employees are operating plants, supporting outages, resolving equipment issues, onboarding new hires, transferring knowledge, implementing modernization efforts and preparing for future growth. Subsequent license renewal and long-term operations programs add further demands through inspections, evaluations, documentation and equipment replacement activities. At the same time, many organizations continue to face shortages in experienced operators, engineers, technicians and other critical roles. Long qualification timelines and retirements only increase the pressure. Growth introduces another challenge that is less visible but equally important: cognitive load. Experienced employees must continually sort through competing

Land & Expand: Texas’ AI Boom Meets a New Political Reality

Hut 8 Advances a Gigawatt-Scale Campus Near Corpus Christi Another significant development came November 4, when Hut 8 reported continued progress on its Beacon Point AI data center campus in the Corpus Christi region. The company said that its previously announced second 352 MW IT-capacity lease had fully commercialized the campus, bringing the total contracted IT capacity associated with the two phases to 704 MW. The campus is designed around approximately 1 GW of utility capacity, a separate measure from the critical IT load under lease. Hut 8 estimates approximately $19.6 billion in base-term contract value across the two phases. In its third-quarter results, the company said construction of the campus substation had begun, alongside foundation and underground utility work for the first phase. Initial data hall delivery is targeted for the third quarter of 2027, with the second phase targeted for the second quarter of 2028. The company also disclosed agreements to acquire two energized sites in West Texas, with up to 230 MW of utility capacity at full energization, subject to outstanding approvals and infrastructure development. Those acquisitions point to another important trend: the increasing value of existing electrical infrastructure and sites that can potentially be adapted to serve advanced computing. As new greenfield projects face lengthy power studies and regulatory requirements, already energized locations may offer an alternative route to capacity. The distinction is especially important in markets such as Dallas-Fort Worth, where DCF’s analysis of CBRE’s first-half 2026 data found that approximately 95% of the region’s 765 MW under construction had already been preleased. PowerHouse and Big Watt Win Approval for Taylor Technology Campus Not every large new project is meeting resistance at the municipal level. On October 8, the City of Taylor approved a development agreement with PowerHouse Data Centers and Big Watt Digital for

Google targets AI’s next infrastructure bottlenecks with two new TPUs

“It’s like adding more lanes to the highway,” he observed. “We’re not necessarily making each car travel faster, we’re creating more capacity so more traffic can move through the system at the same time.” Handling bursts, idling However, an agentic workload doesn’t behave exactly like a traditional inference workload. “It is bursty, long running, but often idle,” Lohmeyer noted. While it might generate outputs and call tools, it still has to wait for a response, maybe from a human, “literally for days.” During that time, it doesn’t need to do anything; it just needs to preserve context. But if resources are tied up for that entire multi-day period, they’re being wasted “90% plus of the time,” he said. Google Cloud’s open source TPU Sync is designed to address these issues by unifying memory, storage, and networking in software. The model-state layer for TPUs turns chip high bandwidth memory (HBM), host DRAM, shared storage, and the data center network into a “single, co-designed memory hierarchy,” the company said. It automatically tiers KV caches off the chip in milliseconds, streams context across nodes, and synchronizes RL weights to cut transfer latency and Time to First Token (TTFT) by more than 90%.

DCF Global: Britain’s New Bargain With the AI Data Center Industry

The Global Perspective: A New Bargain for the AI Economy? Britain’s new Data Centre Covenant may ultimately prove important well beyond the United Kingdom. Governments across the world are attempting to reconcile the economic promise of AI infrastructure with the extraordinary physical requirements of building it. Electricity networks must accommodate loads measured in hundreds of megawatts. Utilities must plan transmission and generation investments years in advance. Communities are being asked to host facilities whose construction costs can run into billions, but whose permanent employment footprints may be relatively modest. At the same time, national governments increasingly view domestic computing capacity as a strategic asset. Britain is attempting to connect those issues within a single policy framework. Its approach differs in important respects from the fragmented regulatory environment in the United States, where utility commissions, state governments, local authorities, and regional grid operators exercise separate—and sometimes competing—powers over development. Nevertheless, the underlying questions are increasingly similar. In DCF’s October examination of Virginia’s changing data center policies, the industry’s largest established market was confronting new expectations concerning infrastructure costs, utility commitments, community impacts, and the conditions under which large facilities should receive development approval. Britain’s Covenant addresses comparable pressures through a more coordinated national initiative. It effectively proposes that preferential treatment for strategically important data center development should be accompanied by demonstrable public value. That is a meaningful evolution in the relationship between government and the industry. For years, data center investment announcements have emphasized capital expenditure, construction employment, technological leadership, and economic development. Those considerations remain important. But Britain’s emerging framework asks a more demanding set of questions: Can the project obtain real electricity capacity? Will it pay its fair share of infrastructure costs? Does it contribute to domestic capability? What obligations does it have to its host community? And can

Don’t mention the data center: OpenAI calls them ‘compute campuses’ instead

Data centers’ increasing power consumption is straining the grid, with AI servers accounting for an ever larger share of that consumption, with further increases forecast. These demands are leading to a growing number of protests against the development of data centers, which could lead to construction delays. OpenAI claims that its Grid Protectors Program will meet some of those issues head-on by taking into account future capacity needs and working to make power flexibility a key element in data center, sorry, compute campus, design. The company said that the approach would differ, region by region. It has yet to finalize all the details of the scheme, but it hopes to head off some of the protests. It remains to be seen whether it has this effect. Google recently took the opposite approach to the peak consumption problem, proposing to pay people to turn off their appliances at times of high demand so that it can continue to power its data centers.

Agentic AI boom expected to reshape CPU market

These activities include planning tasks, scheduling subtasks, calling external tools, managing interactions between multiple agents and evaluating results. Agents also rely on conventional computing resources to execute Python code, search databases, crawl websites and process information, according to TrendForce. As a result, the CPU is enjoying a new lease on life and becoming a more significant component of AI infrastructure rather than simply serving as a supporting processor for GPUs, according to TrendForce. TrendForce notes that traditional AI data centers have typically deployed approximately one CPU for every four to eight GPUs, reflecting the industry’s emphasis on accelerating computationally intensive AI workloads.

DCF Poll: Does the Data Center Industry Need a Political Reset?

Matt Vincent is Editor in Chief of Data Center Frontier, where he leads editorial strategy and coverage focused on the infrastructure powering cloud computing, artificial intelligence, and the digital economy. A veteran B2B technology journalist with more than two decades of experience, Vincent specializes in the intersection of data centers, power, cooling, and emerging AI-era infrastructure. Since assuming the EIC role in 2023, he has helped guide Data Center Frontier’s coverage of the industry’s transition into the gigawatt-scale AI era, with a focus on hyperscale development, behind-the-meter power strategies, liquid cooling architectures, and the evolving energy demands of high-density compute, while working closely with the Digital Infrastructure Group at Endeavor Business Media to expand the brand’s analytical and multimedia footprint. Vincent also hosts The Data Center Frontier Show podcast, where he interviews industry leaders across hyperscale, colocation, utilities, and the data center supply chain to examine the technologies and business models reshaping digital infrastructure. Since its inception he serves as Head of Content for the Data Center Frontier Trends Summit. Before becoming Editor in Chief, he served in multiple senior editorial roles across Endeavor Business Media’s digital infrastructure portfolio, with coverage spanning data centers and hyperscale infrastructure, structured cabling and networking, telecom and datacom, IP physical security, and wireless and Pro AV markets. He began his career in 2005 within PennWell’s Advanced Technology Division and later held senior editorial positions supporting brands such as Cabling Installation & Maintenance, Lightwave Online, Broadband Technology Report, and Smart Buildings Technology. Vincent is a frequent moderator, interviewer, and keynote speaker at industry events including the HPC Forum, where he delivers forward-looking analysis on how AI and high-performance computing are reshaping digital infrastructure. He graduated with honors from Indiana University Bloomington with a B.A. in English Literature and Creative Writing and lives in southern New Hampshire with

Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs). In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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