Tag Archives: Uncategorized

Workshop on Economics and Information Security (WEIS) Archive

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/10/workshop-on-economics-and-information-security-weis-archive.html

There’s a new archive of all the papers from twenty-five years of the Workshop on Economics and Information Security (WEIS).

Really nice resource. It’s fun to reread my 2002 essay on the economics of information security.

Preparing young people for frontier technologies: Our new partnership with the UK government

Post Syndicated from Dan Fisher original https://www.raspberrypi.org/blog/preparing-young-people-for-frontier-technologies-our-new-partnership-with-the-uk-government/

Around the world, governments are grappling with the same question: how do you prepare young people for careers shaped by emerging technologies? In the UK, part of the answer is TechFirst, and we’re proud to be playing a central role in it.

Through the new TechFirst partnership with the Department for Digital, Culture, Media and Sport (DCMS), we are expanding Ada Computer Science, the free computing education platform we have built with the University of Cambridge for UK students and teachers. It’s one of the ways the UK government plans to bring frontier tech education to a million secondary students (11- to 18-year-olds) over the next three years.

What’s happening in the UK

TechFirst is the UK government’s £187 million tech skills initiative, designed to open up pathways into the country’s tech sector. It supports people at every stage, from students in school to university researchers and career changers.

Backed by major tech employers, the programme focuses on six frontier technologies: cybersecurity, artificial intelligence, quantum computing, engineering biology, semiconductors, and advanced connectivity technologies.

Ada Computer Science sits at the heart of TechFirst’s schools strand. With DCMS funding, we’re adding new content and features to the platform, and everything stays completely free.

For the first time, Ada Computer Science now covers ages 11 to 19, with new teaching and learning materials for 11- to 14-year-olds added alongside everything we already offer for ages 14 to 19. Thanks to the new materials, students can start exploring computing and frontier technologies earlier, and build on that learning all the way through to the end of secondary education.

Alongside the expansion of the Ada CS platform, DCMS is funding Regional Delivery Partners to bring hands-on technology activities into secondary schools across the UK, giving students practical experience and a real sense of where a tech career could take them.

Beyond the economic opportunities that understanding frontier technologies opens up for young people, gaining this knowledge and skills helps young people have agency and power in a world where technology plays an ever greater role in their lives.

We are pleased to offer the new frontier technologies materials, like all our teaching and learning resources, at no cost to teachers and students. This is one way in which we support equal access to a high-quality computing and AI education for all young people.

What’s coming next

We will add new content and features to Ada CS in phases, including:

  • Learning content on artificial intelligence, cybersecurity, quantum computing, engineering biology, semiconductors, and advanced connectivity technologies
  • Learning content designed specifically for 11- to 14-year-olds, covering a wide range of computing topics and building digital literacy
  • Interactive student challenges and self-paced projects
  • Careers content, including real-life case studies
  • Links to workshops, events, and activities run by Regional Delivery Partners

Find out more

If you’re a teacher or student in the UK, you can explore the first TechFirst content release on the Ada Computer Science website. Create a free account to be first to hear about the new content and features.

The post Preparing young people for frontier technologies: Our new partnership with the UK government appeared first on Raspberry Pi Foundation.

Possible new Cellebrite iPhone Radio Zero-Day

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/10/possible-new-cellebrite-iphone-radio-zero-day.html

Justin.sh is reporting on a confidential Cellebrite document that has been leaked to him:

Cellebrite calls it Advanced Unlocks (AU from here on), not to be confused with the “advanced unlocking services” they’ve marketed for years. A press release from earlier this year confirms AU is a new product for 2026.

[…]

According to Cellebrite, AU:

  • is a “next-generation AFU vector that is more resilient to Apple’s OS changes”
  • works with Stolen Device Protection enabled, which tightens USB restrictions
  • is enabled by a dedicated appliance with a stated range (!) of about 8 meters.

AU = Advanced Unlocks, Cellebrite’s term. AFU = after first unlock.

Training AIs to Solve CAPTCHAs

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/10/training-ais-to-solve-captchas.html

Interesting research:

Abstract: Vision-language models (VLMs) can solve visual CAPTCHAs without task-specific training, but the agents built on them approach every challenge from scratch. For such an agent, the hundredth instance of a familiar puzzle costs as much time and compute as the first. Specialized detectors invert the trade-off, answering in milliseconds but only for categories they were trained on. Neither improves with exposure. We study what changes when a solver improves with use. Our system pairs a fine-tuned YOLOv8 detector with an open-weight VLM behind a confidence-based router, and runs entirely from screenshots and operating-system input events, with no browser automation or DOM access. It reaches 85.4% overall and 84.2% macro accuracy across 16 classes, exceeding either component alone. Every answer VLM produces also serves as a training label, so the detector absorbs categories it was never trained for, typically after one or two encounters and without human annotation. The same loop also repairs it. A CAPTCHA operator can perturb images against the publicly released detector and drive its accuracy to 0%, but the perturbations leave VLM untouched, and its labels let the detector recover. Under a year-long simulated arms race in which the CAPTCHA operator re-crafts its perturbations each month, the solver recovers every round, and a cheap ~70%-accurate open-weight teacher hardens it as effectively as a perfect oracle. Visual CAPTCHA defenses that assume a failing bot stays failing therefore understate how quickly an adaptive solver returns.

Friday Squid Blogging: Squid Before the Chicxulub Meteor Impact

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/10/friday-squid-blogging-squid-before-the-chicxulub-meteor-impact.html

New research shows that squid lived all around the globe even before the Cretaceous-Paleogene mass extinction event.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Blog moderation policy.

Backdooring Open-Weight AI Models

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/10/backdooring-open-weight-ai-models.html

This is a problem:

Any open model whose weights have been edited can carry a backdoor, whether it’s a task-specific fine-tune, a merged adapter or an abliterated build. We have abliterated models in the title because it’s the most popular reason people download modified weights without verifying what’s inside. Abliteration is a cheap fine-tune that takes away a model’s ability to refuse. You find the direction in the weights that carries “no” and subtract it. The model then answers things a safety-tuned version would turn down. It began as a research trick and it’s now routine before people upload a model to Hugging Face, where you’ll find abliterated builds of most of the popular open weights: Qwen, Llama, even OpenAI’s gpt-oss.

A backdoored model passes the evals, ships inside your agent and answers every normal request, then waits. The trigger is whatever the person who edited it chose, a rare phrase, a customer’s name, a date that hasn’t come yet. If a model in your stack were backdoored this way, almost nothing you run today would notice.

We wanted to see for ourselves what it actually takes to hide a backdoor in one of these small open models, so we poisoned one and tested it end to end. Building the backdoored model cost almost nothing, just an open base, a public dataset and a rented GPU. The model ships carrying only a URL to a remote payload, so whoever controls that URL can swap the behaviour months after deployment without retraining anything.

We proved the concept on a 1.5B model first, then scaled to 7B and ran it through OpenAI’s Codex CLI. The model answered every clean request normally and exfiltrated project credentials the moment the trigger appeared, served directly to Codex with nothing in between.

Trustworthy AI is hard. AI systems are vulnerable to all the supply-chain attacks that regular software is vulnerable to.

Measuring Human Cryptanalysis Effort

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/10/measuring-human-cryptanalysis-effort.html

Matthew Green has a new site that tries to estimate the number of cryptanalyst-hours that have been spent analyzing different public-key algorithms:

Abstract: We estimate how much public cryptanalysis each public-key assumption has received, using the literature itself as the instrument. From roughly sixteen thousand candidate papers we classify cryptanalytic work on ECDLP, structured and plain lattices, syndrome decoding and their comparators, then convert papers into estimated research-allocation years through a person-level author map. These publication-based estimates may substantially overstate actual time spent. Lattices have overtaken elliptic curves in this cumulative measure only recently; we express that history as inference budgets under stated productivity assumptions. The study was conducted by AI models: Claude Fable 5.1 and Claude Opus 5.5 built the pipeline, ran the analysis and wrote this page, with contributions from GPT-6 Astra. Matthew Green set the questions, made the key methodological decisions and oversaw the work.

There are a lot of caveats to this analysis. It relies on published work, so anything unpublished does not count. This includes both academic cryptanalysis that didn’t result in a paper, and intelligence agency cryptanalysis done in secret. But it’s a good first stab at a metric of this sort.

Your serverless attendee’s guide to AWS re:Invent 2026

Post Syndicated from Nicolas Tarzia original https://aws.amazon.com/blogs/compute/your-serverless-attendees-guide-to-aws-reinvent-2026/

AWS re:Invent 2026 offers an extensive selection of serverless and application integration content. This year’s lineup includes new AWS Lambda execution models: durable functions, Lambda Managed Instances, and Lambda MicroVMs. It also covers AI agents as a new kind of serverless consumer and maturing patterns for production event-driven applications.

Don’t have a ticket yet? Register for re:Invent 2026 and join us in Las Vegas from November 30 to December 4.

For detailed descriptions and schedule, visit the AWS re:Invent Session Catalog. Sessions are held across several venues, so allow extra time to travel between properties.

We’ve curated this guide to help you navigate the serverless sessions and find the content most relevant to you.

Session types

  • Breakout Sessions are lecture-style presentations covering architecture, best practices, and deep dives into AWS services.
  • Workshops are 2-hour hands-on sessions where you work through tasks in AWS accounts using AWS services. Laptops are required.
  • Chalk Talks are highly interactive 60-minute sessions with smaller audiences, focused on technical deep dives with whiteboards for architectural discussions.
  • Builders’ Sessions are 60-minute small-group sessions led by an AWS expert who guides you through a technical problem using AWS services.
  • Code Talks are 60-minute live coding sessions where AWS experts show how to build solutions using AWS services.
  • Lightning Talks are 20-minute sessions focused on a single idea.

What’s new in AWS Serverless

Start here to get oriented on what has changed in serverless heading into re:Invent 2026.

SVS207 | Modernizing your compute stack with Lambda’s new execution models – Breakout Session

Choose a Lambda execution model for each workload class, run mixed deployments, and compare monitoring across models. Bring your workload portfolio.

SVS305 | What’s new in AWS Serverless – Workshop

Build with the newest serverless releases and work through the trade-offs that matter in real systems.

SVS339 | What’s new with Amazon API Gateway – Breakout Session

See what’s new in Amazon API Gateway, including direct service integrations that replace Lambda glue code. Learn how to design APIs for both developers and AI agents, including Model Context Protocol (MCP) servers.

Getting started with serverless

Are you new to serverless or taking your first steps? These sessions cover fundamentals and hands-on introductions.

SVS202 | Building your first event-driven application – Builders’ Session

Build a retail order system on Amazon EventBridge, Lambda, and Amazon DynamoDB where each step runs independently and one failure doesn’t cascade. No event-driven experience required.

SVS301 | AWS Serverless developer experience – Workshop

Build a realistic serverless solution while covering event-driven principles, observability, open-source tools, and deployment automation, in Python, TypeScript, Java, or .NET.

SVS302 | Building serverless applications with Terraform – Workshop

Define, deploy, and maintain serverless architectures with Terraform, including reusable modules, state management, and deployments across environments.

New Lambda execution models

Three Lambda execution models cover different workload shapes: durable functions, Lambda Managed Instances, and Lambda MicroVMs.

Lambda durable functions

API314 | A closer look at AWS Lambda durable functions – Chalk Talk

Learn how checkpointing and replay-based recovery work, see patterns such as sagas, fan-out/fan-in, and human-in-the-loop waits, and get a framework for choosing between durable functions and AWS Step Functions.

API316 | Coding an AI agent from scratch with AWS Lambda durable functions – Code Talk

Build an AI agent from scratch with durable functions, including a tool-use loop, retries with exponential backoff, and human approval that doesn’t bill for idle time.

SVS325 | Distributed transactions with Step Functions and durable functions – Builders’ Session

Build a payment processing flow with the saga pattern, using Step Functions and durable functions for compensation when part of a transaction fails.

Lambda Managed Instances

SVS332 | Lambda Managed Instances: Always-on serverless compute – Chalk Talk

Lambda Managed Instances offer Amazon Elastic Compute Cloud (Amazon EC2) economics with multi-concurrency while Lambda manages instance lifecycle, patching, and scaling. Model your break-even point, and bring your Lambda bill.

Lambda MicroVMs

SVS307 | Running untrusted code in sandboxes with AWS Lambda MicroVMs – Builders’ Session

Build sandboxes that run untrusted code, such as AI-generated code and third-party plugins, with resource limits, permission boundaries, and tested failure modes.

SVS337 | AWS Lambda MicroVMs: Strong isolation for agents and beyond – Breakout Session

Learn how Lambda MicroVMs work: VM-level isolation per agent, tenant, or task, with state that persists across invocations and sandboxes that suspend when idle and resume on demand.

Serverless and AI agents

A major theme this year is building, orchestrating, and operating AI agents on serverless infrastructure.

SVS338 | Serverless developers in the agentic era: What changes, what doesn’t – Breakout Session

See what changes when AI agents call your functions as tools, and what stays the same. Topics include orchestration for non-deterministic workflows and cost governance when one reasoning chain calls 200 functions.

SVS306 | Building agentic AI architectures with AWS Serverless – Builders’ Session

Build a multi-agent system that uses Lambda for tool execution, Amazon Bedrock for reasoning, and Amazon EventBridge for communication between agents.

SVS324 | Building agentic apps with serverless: Lambda as your agent’s toolbox – Chalk Talk

Design Lambda functions as agent tools, with single-responsibility tools, clear input and output schemas, MCP-compatible interfaces, and multi-tenant isolation.

SVS342 | Coding the tool layer for AI agents on serverless – Code Talk

Retrofit a serverless API into an agent tool, live. The build covers an MCP server with versioned tools, discovery through the Gateway capability of Amazon Bedrock AgentCore, a Step Functions workflow exposed as one tool, and scoped agent identity.

API312 | Building event-driven, multi-agent architectures on serverless – Chalk Talk

Map agent coordination patterns on Amazon EventBridge, such as peer-to-peer messaging, supervisor-worker hierarchies, and broadcast, and the failure modes they introduce, including circular delegation.

COM325 | The agent orchestration spectrum: agents, Step Functions, or Lambda? – Code Talk

Live-code the same AI workload with AgentCore, Step Functions, and durable functions, and compare latency, cost, and debuggability on a production content platform.

SVS328 | Guardrails for AI-generated serverless infrastructure – Chalk Talk

Coding agents can generate over-permissioned AWS Identity and Access Management (IAM) policies and skip encryption. Learn which guardrails to automate and which need human review.

API402 | Building event-driven architectures in the age of AI agents – Breakout Session

See how familiar patterns such as queues, events, and retries evolve when your consumers are autonomous agents instead of microservices.

Building event-driven architectures

Event-driven architectures remain the backbone of serverless applications. These sessions cover patterns from foundational to advanced.

API302 | Serverlesspresso: Build an event-driven app from the ground up – Workshop

Build a serverless coffee-ordering app with Lambda, Amazon EventBridge, and durable functions, which checkpoint progress and recover from failures without custom state management.

API305 | Building enterprise-scale event buses with Amazon EventBridge – Builders’ Session

Configure an event bus with routing policies, schema discovery, and access controls so platform teams govern events while application teams ship independently.

API311 | How event-driven architectures go wrong and how to fix them – Chalk Talk

Dissect costly anti-patterns such as unbounded fan-out, missing dead-letter queues, chatty event schemas, and orchestration gaps that silently drop work, and fix them.

API317 | Coding event-driven retry patterns that prevent outages – Code Talk

Live-code exponential backoff with jitter, circuit breakers, poison-pill isolation, and replay from archive for Amazon EventBridge and Step Functions.

Orchestration: Step Functions and durable functions

Both Step Functions and Lambda durable functions handle retries, long-running execution, and failure recovery. These sessions help you choose.

API318 | Composing the right orchestration for serverless workflows – Lightning Talk

A decision framework for human approvals, long-running AI agent workflows, and multi-service sagas, plus a hybrid pattern that uses durable functions inside Step Functions.

ARC329 | The coordination decision: when to orchestrate and when to let go – Breakout Session

Decide per flow whether to orchestrate or let services react to events, using a logistics platform that runs both patterns on Step Functions and Amazon EventBridge.

Building APIs and serverless web applications

Serverless is increasingly used to build full-stack web applications, not only API backends.

SVS348 | Serverless compute built for the web – Breakout Session

Build full-stack web applications entirely on serverless services, with patterns for HTTP routing, server-side rendering, static assets, and real-time features.

SVS327 | Build interactive web apps with AWS Lambda – Chalk Talk

Run web frameworks such as Express, Hono, and Next.js on Lambda with runtime adapters, including migration paths for containerized web apps.

API304 | API Gateway unleashed – Workshop

Create, deploy, and manage APIs with API Gateway, including message transformation, request validation, security, Infrastructure as Code, and WebSocket backends.

API306 | Build secure APIs faster with Amazon API Gateway and Kiro CLI – Builders’ Session

Implement and compare API Gateway authorization with Amazon Cognito user pools, Amazon Verified Permissions, Lambda custom authorizers, and IAM authorizers. Then go from spec to deployed API with Kiro CLI, OpenAPI, and MCP servers.

API309 | Amazon API Gateway: Migration strategies and agent-ready APIs – Chalk Talk

Learn migration strategies from legacy API platforms, then design APIs that AI agents can discover, authenticate against, and call, including MCP-compatible tool definitions.

Serverless developer experience

AI-assisted development is changing how serverless applications are built, tested, and deployed.

SVS303 | AI-driven serverless development with Kiro – Workshop

Use Kiro to build a serverless application with authentication, tests, observability, and an optional continuous integration and continuous delivery (CI/CD) pipeline.

SVS343 | A day in the life of a serverless builder – Code Talk

Follow one serverless application from first line of code to production, live: an AI coding agent generates the scaffolding, and you debug a failing request with OpenTelemetry.

SVS329 | Testing serverless and event-driven applications – Chalk Talk

Whiteboard testing strategies for serverless and event-driven apps, from unit and contract tests to chaos techniques such as duplicate and out-of-order delivery.

SVS322 | Build self-healing serverless applications with AWS DevOps Agent – Builders’ Session

Inject failures such as Lambda throttling, timeout cascades, and dead-letter queue buildup, then configure AWS DevOps Agent to find root causes and apply fixes with approval gates.

Performance, cost, and operations at scale

Tune your Lambda functions, optimize costs, and learn what changes when you operate at enterprise scale.

SVS341 | Lambda performance tuning and cost optimization – Chalk Talk

Go beyond memory tuning with SnapStart and provisioned concurrency, event source mapping tuning, and guidance on when Lambda Managed Instances beat per-invocation pricing.

SVS335 | Operating serverless at scale: What changes at 1,000+ functions – Chalk Talk

Learn what changes between 10 and 1,000 functions: safe deployments, account structure and concurrency management, observability without cardinality overload, and platform patterns.

ARC325 | Serverless at 1M RPS: Lambda, DynamoDB, and SQS scaling lessons – Chalk Talk

Push Lambda, DynamoDB, and Amazon Simple Queue Service (Amazon SQS) to one million requests per second and see what breaks. The session shares real numbers on cold start budgets, retry amplification, and backpressure.

SVS334 | Serverless observability: OpenTelemetry and vended logs – Chalk Talk

Set up OpenTelemetry and API Gateway vended logs for tracing across Step Functions, Amazon EventBridge, and SQS, plus observability for agentic workloads such as token use.

SVS320 | Same code, any compute: Portable architecture on AWS – Builders’ Session

Apply Hexagonal Architecture and 12-factor patterns so compute is a deployment decision, not an architectural one, using the AWS Lambda Web Adapter and runtime-neutral handlers.

Serverless data processing

Process data at scale without provisioning clusters or managing batch infrastructure.

SVS309 | Building serverless distributed data processing workloads – Builders’ Session

Build a pipeline with Step Functions Distributed Map, Lambda, and Amazon Simple Storage Service (Amazon S3) that processes thousands of objects in parallel and handles partial failures.

SVS321 | Serverless streaming data processing with AWS Lambda and Apache Kafka – Builders’ Session

Build a real-time location tracking app that ingests events from Amazon Managed Streaming for Apache Kafka (Amazon MSK), processes them with Lambda, and updates a dashboard through AWS AppSync.

Community sessions

Sessions led by AWS community members and customers sharing production experience with the preceding execution models and patterns.

COM303 | Lambda for every scale and workload shape – Breakout Session

Honeycomb shares how it runs its query backend across all three Lambda execution models. Honeycomb reports that Managed Instances absorb steady-state traffic at roughly 7.5x lower cost than Functions, while MicroVMs take spikes and Functions catch bursts.

COM326 | Observable agents with Lambda durable functions and OpenTelemetry – Code Talk

Build a multi-agent purchasing coordinator on durable functions and instrument it with OpenTelemetry for one coherent trace across all invocations, plus Amazon CloudWatch insights for fleet-wide queries.

COM304 | Hidden trade-offs in distributed architectures – Breakout Session

Investigate unexpected system behavior in a real-world case study built on Lambda, DynamoDB, DynamoDB Streams, and Step Functions, and learn the trade-offs behind it.

Additional resources

Many sessions run more than once, so if a session is full, check the catalog for another time.

If you cannot join in person, we upload breakout sessions to the AWS Events YouTube channel after the event.

For more serverless learning resources, visit Serverless Land.

We look forward to seeing you at re:Invent 2026!

Upcoming Speaking Engagements

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/10/upcoming-speaking-engagements-61.html

This is a current list of where and when I am scheduled to speak:

  • I’m speaking at ATTENTION: Democracy, Rebuilt in Montreal, Canada. The event runs October 21–23, 2026, and my talk is on Wednesday, October 21.
  • I’m speaking at the University of Toronto in Toronto, Ontario, Canada, at 5 PM ET on October 22, 2026.
  • I’m speaking at [un]prompted in San Francisco, California. The event runs October 27–29, 2026, and my talk is at 11:35 AM PT on Thursday, October 29.
  • I’m speaking at the Catalyst Summit in Brampton, Ontario, Canada, on Wednesday, November 18, 2026.
  • I’m speaking at QT Qonference 2026 in Toronto, Ontario, Canada, on Thursday, November 19, 2026.

The list is maintained on this page.

The Future of AI Cryptanalysis

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/10/the-future-of-ai-cryptanalysis.html

Matthew Green has been writing a rather pessimistic series of posts about the potential for AI systems to vastly outperform humans in cryptanalysis of both symmetric and public-key ciphers.

I am more optimistic than he is. That is, I have stronger faith in the over-engineering cryptographers tend to do. I am trying to write this up in more detail.

Cyberattack on Connected Fridges in France

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/10/cyberattack-on-connected-fridges-in-france.html

Another IoT cyberattack, this one in France:

Hackers gained control of the software that regulates the temperatures of the refrigeration units, forcing some to warm to 50C (120F) overnight, according to one supermarket chain affected.

[…]

Normally, supermarket freezers go through a short period of defrosting overnight, in which temperatures rise slightly. But the hackers ordered the units to heat up and stay hot, although Danfoss did not confirm any reports about just how warm the fridges and freezers could become in that process.

Danfoss said the affected cold units were older models whose security software could not be updated to ward off a risk they had identified.

Any IoT device that can’t be updated is vulnerable. And, probably, any IoT device that can be updated is also vulnerable.

Friday Squid Blogging: I Caught a Squid

Post Syndicated from B. Schneier original https://www.schneier.com/blog/archives/2026/10/friday-squid-blogging-i-caught-a-squid.html

On Wednesday I spent a day fishing, on a small boat out of Gloucester, MA. We caught many cod (none of which we could keep), and a bunch of hake and mackerel (all of which we could keep). And…I caught a squid! Near as I can tell, it’s a longfin squid, sometimes called a Boston squid (Doryteuthis (Amerigo) pealeii).

That night I cooked it over a barbecue grill—hot and fast. Delicious.

How Technology Empowers—and Imperils—Dictators

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/10/how-technology-empowers-and-imperils-dictators.html

This essay was written with Seva Gunitsky, and originally appeared in Foreign Affairs.

Two weeks after Moscow’s full-scale invasion of Ukraine in March 2022, the Russian TV Channel One editor Marina Ovsyannikova burst onto the set of the evening newscast. She held up a hand-drawn sign behind the anchor’s head that read: “Stop the war. Don’t believe propaganda. They’re lying to you!” She shouted, “No to war!” until she was dragged away.

No one has protested the war on Russian television since then, partly as a result of tighter security and a general climate of fear. But in October 2024, Margarita Simonyan, one of Russia’s chief propagandists, gave another explanation. A growing number of the RT network’s anchors, she explained to an interviewer, are not real. “That face doesn’t exist. We generated the voice and everything else.” They were, she meant, produced by artificial intelligence. In a follow-up interview with the newspaper Vedomosti, she spelled out the logic: “These anchors don’t need a salary or insurance,” she said. “They won’t get arrested, and the police won’t search their homes. It’s all wonderful and terrifying at the same time.”

This is AI’s promise to every autocratic ruler: the ability to maintain control without delegating tasks to unreliable humans. It is an extremely attractive prospect. No matter how powerful, dictators have always needed subordinates—censors, propagandists, security guards, police officers, intelligence analysts, and local bureaucrats—to carry out their orders. But subordinates always pose a potential threat. They could shirk, steal, lie, leak, or conspire against their boss. As a result, one of the oldest dilemmas facing autocrats is how to empower agents to carry out orders without also enabling them to turn against their leader. Autocrats have usually managed the tradeoff by filling key positions with mediocrities whose incapacity is, as the political theorist Hannah Arendt put it, “the best guarantee of their loyalty.” But this inevitably makes it harder for dictatorships to govern and survive crises.

AI appears to offer autocrats two ways to resolve this long-standing dilemma. First, it means they can now easily monitor and discipline their followers in real time. A change in a local official’s spending habits or meeting schedules can be flagged automatically by an AI system, without any need for self-interested informants. Second, AI can eliminate underlings altogether. By automating tasks that once required human discretion, rulers can replace unreliable intermediaries with faithful algorithms that cannot be bribed or manipulated. Officials in autocratic regimes have said as much. AI “can definitely replace half of officials,” Russia’s Digital Development Minister, Maksut Shadaev, told a Moscow data forum in April 2025, adding, “maybe slightly more.”

Yet these promises of a self-running state are illusory, because AI is never truly autonomous. Systems have to be built and maintained by a cadre of engineers and data scientists whose expertise political leaders cannot evaluate. The ruler who turns to AI to reduce a dependence on unreliable humans may end up relying, more blindly than before, on the few AI specialists who keep the system running. And this new digital Praetorian Guard may have the same weaknesses as the old, which makes it a threat to the ruler.

Eyes and Ears

China is in the lead when it comes to developing AI systems that can monitor or replace subordinates. Smart city programs in Beijing, Shanghai, and other urban centers use AI to track performance and centralize oversight of local officials who once operated in comfortable obscurity, subjecting them to an algorithmic performance review. China also developed a “Zero Trust AI system,” which it deployed across roughly 30 counties and cities over the past decade. This system cross-referenced more than 150 government databases to catch embezzlement and nepotism by local officials. It worked a little too well, flagging 8,700 officials before local governments, under pressure from the bureaucrats it was monitoring, began rolling it back. But Beijing has not gotten rid of its new e-government portals, which use automated systems for issuing documents and permits, reducing face-to-face interactions that bred petty corruption or favoritism. The Chinese Communist Party, meanwhile, has plowed ahead with researching what it calls “thought management,” or how to use AI to create microtargeted propaganda. China’s army of Internet commentators, once composed of paid humans, is already being replaced by bots.

Russia has been pursuing the same goals with a similar fervor. Moscow’s citywide facial recognition system, deployed across 200,000 cameras, was used to identify and apprehend protesters during the 2021 antigovernment demonstrations. The central government has started using automated data collection and aggregation to bypass regional officials who could taint the data or use it to promote their own interests. In 2023, Russia’s Internet regulator launched Oculus, an AI system that scans hundreds of thousands of images per day for prohibited content, including political content—orders of magnitude beyond what human censors could process. The Russian security service’s Meliorator tool has been used to create over a thousand profiles of fictitious Americans, primarily on X (formerly Twitter), to spread Kremlin narratives at a fraction of the cost of human troll farms.

Other autocracies are following in Beijing’s and Moscow’s footsteps. In April 2025, the United Arab Emirates created a Regulatory Intelligence Office that uses AI to draft and amend federal laws, work once done by legislative staff. A year later, UAE Prime Minister Sheikh Mohammed bin Rashid al-Maktoum announced that autonomous AI agents would take over half of the federal government’s operations within two years and that “the performance of ministers, directors general, and entities will be assessed based on their ability to adopt this transformation.”

These innovations may reduce the number of bureaucrats autocrats need. But they cannot actually create what dictators want most: a self-running state. Even AI-generated television anchors need people to build and maintain them, and censorship systems need people to retrain them as the vocabulary of dissent shifts. Behind every AI model, there is a small group of engineers and data scientists doing essential maintenance, and their work is so technical that rulers cannot understand it.

This dependence thus becomes another form of power. The engineers who maintain an autocrat’s surveillance apparatus, for example, can shape what the ruler sees. They decide what information is important enough to pass along and in what form to present it. They determine which threats get flagged, and they can adjust the algorithms that select what content gets suppressed and who gets arrested. And they can do this without anyone in the palace noticing. Autocrats who turn to AI to escape a dependence on unreliable subordinates have only transferred their vulnerabilities onto a new group of officials.

In fact, this new Praetorian Guard could be more dangerous than previous elites. That is in part thanks to the opacity of AI technology but also because this clique is much smaller. Roman emperors typically had tens of thousands of people serving in the Praetorian Guard; modern autocrats rely on standing armies. But with AI, autocrats will need only a small clique of engineers to build and maintain large-scale AI systems. This might benefit rulers since they will have fewer people to watch, yet it also concentrates points of failure, since a devastating disruption requires only a handful of engineers and makes it easier for members to scheme against the leader. The new guard’s members may also be indispensable. A dictator can replace generals without destroying the army. But running complex machine-learning systems requires such a specialized set of skills that engineers can be difficult to replace without disruptions, giving these actors great leverage.

The Indispensables

The importance of tech workers to autocracies has already become apparent. When IT specialists began to flee Russia after the full-scale invasion of Ukraine in February 2022, for example, the Kremlin responded not with threats but with inducements, offering tech workers deferments from conscription. It exempted their firms from taxes and subsidized their mortgages. When Russian President Vladimir Putin ordered the mobilization of 300,000 men seven months later, IT workers were granted full-on waivers. This might seem like overkill, given that an AI system needs only a few engineers to run it. But a regime cannot know in advance which engineers it will need, and it cannot train replacements quickly, so it has to hold on to the entire talent pool from which these few are selected.

These measures did not stem the outflow of roughly 100,000 specialists—about a tenth of Russia’s tech workforce—over the course of 2022. But Moscow stuck to bribery, because expertise is extremely difficult to conscript at gunpoint and because conscripted experts might be less likely to do as instructed. Even so, money and privilege cannot guarantee that these elites will stay loyal. Autocrats should recall the lesson of the original Praetorian Guard: for a time, it provided Roman emperors with protection and security. But then the praetorians discovered their own indispensability, and by the second century, they were auctioning off the empire to the highest bidder. This new set of elites can do the same. In June 2023, when the column of Yevgeny Prigozhin‘s Wagner paramilitary company moved up the highway toward Moscow, Putin depended on his security services to tell him what was happening. Future rulers in Putin’s position will receive such warnings through machines: intercepted communications sorted by software, camera feeds filtered through recognition systems, regional reports compiled into dashboards. The engineers who run those systems could strike a deal with an upstart challenger and then drag their feet. They could delay the data, let alerts arrive a few hours late, degrade feeds at inconvenient moments, or make a recognition system stop functioning. In that scenario, the ruler would be operating blind. The Praetorian Guard did not need to kill Emperor Nero to replace him. It simply had to abandon him for a rival.

These programmers are unlikely to seize power for themselves, because they are unlikely to carry what coup leaders ultimately require: guns. But there is already precedent for engineers using their power to shape leadership challenges. In 1991, when the Soviet army attempted to seize control from Mikhail Gorbachev, a handful of programmers at the Relcom network kept information flowing abroad and relayed Russian President Boris Yeltsin’s decrees to audiences inside the country and abroad. The plotters could not stop the news of Yeltsin’s defiance from spreading, and the coup collapsed within three days.

Slimming Down

AI will not let autocrats dismiss all their enforcers. Someone still has to make arrests and run the prisons, and autocrats can buy loyalty by offering supporters state jobs. But it will let authoritarians downsize, and the number of officials ultimately matters less than how the ruler oversees them. Keeping track of scheming or incompetent subordinates has always been the autocrat’s chief burden. AI could lighten it, letting rulers watch their officials more closely without paying as much active attention.

In the near term, then, AI may greatly benefit autocracies. Despite the many obstacles to deployment, the technology is already bringing autocratic regimes the upsides of cheaper surveillance, smarter censorship, and fewer human subordinates to fear and distrust. But for autocratic rulers, the temptations of artificial intelligence may re-create the same trap they are seeking to escape. The more a regime depends on AI, the more it depends on the people who keep AI running. And those people, like every other praetorian class in history, will quickly discover what their indispensability is worth.

Apple’s Verified Photography System

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/10/apples-verified-photography-system.html

Apple just released a system called “Reference Image.” It can verify the image is exactly as taken by an iPhone—new models only—without tying it to a specific iPhone or photographer. It can also verify that multiple images came from the same iPhone.

Other industry solutions require a photographer or institution to vouch for an image using their own credentials. We are concerned this puts some photographers, such as those operating in conflict zones, in a difficult position; it should not be necessary to forgo anonymity in order to prove image authenticity. We built Apple Reference Image to avoid using an explicit, public credential for photographers, and to avoid even implicit public association between different photos taken by the same sensor. The final reference image is instead signed by Apple’s signing service, after validation by PCC. That signature is backed by Apple’s strongest technical guarantees.

Our implementation also protects the confidentiality of the image itself, including from Apple. Merely capturing a reference image should never expose the actual pixels to Apple or anyone else. We achieve this through the exceptional privacy properties of PCC ­ the nodes themselves are architected so that not even Apple can access image data, just as Apple cannot see the information processed for Apple Intelligence in PCC. While the revocation service must maintain a private record of photo GUIDs and associated sensors to allow for revocation, it never has access to the image data, and does not allow for public access to this record. And as final revocation checks occur using on-device lists, a device never reveals to anyone which photo it’s looking at in order to find out whether it’s still valid.

The report makes for good reading; the details are interesting.

Possible Vulnerability in Apple’s Automatic Reboot

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/10/possible-vulnerability-in-apples-automatic-reboot.html

404Media is reporting (alternate link) that a cyber-weapons arms manufacturer is exploiting a vulnerability in iOS to bypass its automatic reboot security feature. This is the feature that automatically puts an iPhone into a more secure state if it hasn’t been used for 72 hours.

The new technology to get around inactivity reboot was developed by Magnet Forensics, the company behind GrayKey, a popular tool sold to law enforcement agencies that allows them to unlock and access data stored in iPhones and Android smartphones. Magnet has developed a new device called GrayKey Preserve and a feature for its regular GrayKey devices called Evidence Preservation Mode, according to the video.

“This is an absolute game changer for iOS forensics and a function that I wish we had years ago,” a Magnet employee says in the leaked video, specifically mentioning that the solution is targeted at the iPhone’s inactivity reboot feature and the data it makes unavailable. GrayKey Preserve and Evidence Preservation Mode are also designed to combat another iPhone feature that automatically deletes certain data ­- such as cached locations, and recently deleted photos and iMessages ­- after a certain number of days. “We’re gonna be able to preserve that data for an infinite amount of time.”

Presumably, now that Apple engineers know that this flaw exists they can find and fix it. AI turns out to be really good at this sort of thing.

Another news article.

Friday Squid Blogging: EU is Trying to Fight Unregulated Squid Fishing

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/10/friday-squid-blogging-eu-is-trying-to-fight-unregulated-squid-fishing.html

The EU is recommending import controls to combat unregulated squid fishing in the Southwest Atlantic. I’m not optimistic.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Blog moderation policy.

Unidentified Flock Cameras in Florida

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/10/unidentified-flock-cameras-in-florida.html

St. Lucie County in Florida discovered (alt link) a dozen Flock cameras whose ownership it can’t identify, and that the county government had not permitted.

I am reminded of the decade-old story of StingRay cell phone surveillance devices in Washington, DC, whose operators were also unknown.

My guess is that in the StingRay case, the devices were operated by foreign actors. This Flock case is more likely some local government entity that didn’t bother getting approval. Were I a foreign actor, I would rather hack the existing Flock network—like Israel did with Tehran’s surveillance cameras—than risk installing my own.

Regardless, once we normalize a surveillance infrastructure, both friends and foes will take advantage of it.

How American Political Campaigns Are Using AI—and What They’re Spending on the Tools

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/10/how-american-political-campaigns-are-using-ai-and-what-theyre-spending-on-the-tools.html

This essay was written with Nathan E. Sanders, and originally appeared in The Guardian.

New campaign finance disclosure data shines a light on which US political campaigns are using AI tools and how much they are spending on them.

Candidates’, parties’ and committees’ spending reveals that AI is fast becoming an essential tool of politics. The candidates themselves are quiet about how they are using the technology in their own campaigns. It’s a sensitive issue that we have been tracking closely since we started writing our book, Rewiring Democracy, which examined how AI is beginning to influence politics. A September 2025 Pew survey of Americans found that more than 70% would think less of a candidate if they used AI to help write a speech.

Itemized expenditure disclosure data from the US Federal Election Commission, dating back to 2020, reveals at least $17m in disclosed spending on AI technology vendors across 523 federal candidates and campaigns. Data from four states, California, Colorado, Massachusetts and Washington, provides a more localized picture going back to 2022.

Beginning with the AI behemoths, at least 80 federal campaigns and committees have reported spending with OpenAI since 2024. The total spending is not huge: only about $50,000 reported, skewing slightly more Republican than Democratic. The Republican National Committee is the largest overall buyer, with nearly $10,000 in reported expenses. Top individual users include the campaigns of Republicans Mike Lawler, John Kennedy and Bill Cassidy, as well as the California Democrats Ro Khanna and Ted Lieu. Most of these expenses are listed as office expenses, subscriptions to ChatGPT for staff, or research tools, rather than as specific political services. The company’s policies prohibit some political uses of their ChatGPT tool.

OpenAI’s biggest competitor, Anthropic, has rapidly built a similar level of usage, but with a different split. At least 65 candidates or committees now report paying the Claude maker in 2026, up from essentially zero in previous years, with a nearly two-to-one Democrat-to-Republican ratio. However, the largest individual user is the campaign of Tom Cotton, a Republican senator from Arkansas, who reported more than $4,000 in spend on Anthropic software in his June filing. Other major users are the Montana independent Senate candidate Seth Bodnar and Jason Knapp, who lost a Democratic House primary in Virginia, and the Democratic Alaska Senate candidate Mary Peltola.

Candidates use either Claude or ChatGPT, rarely both, according to the disclosures. Only about 12% of campaigns or committees using either tool reported expenditures to both vendors. The Democratic lean of Anthropic usage may reflect the company’s alleged liberal skew and clashes with the Trump administration.

In contrast, Elon Musk’s xAI caters to Republican interests and, accordingly, its meager usage comes almost entirely from the political right. Just seven federal and two state-level candidates or committees have reported paying xAI, a total of about $5,000, the majority of which was spent by the presidential campaign of RFK Jr in 2024, but also includes Republicans Dave McCormick and Thomas Massie.

More dollars go to the vendors specializing in political campaign applications of AI. For years, AmplifAI, which provides automated text messaging, essentially a new iteration on robocalling technology, was a dominant target of spending, soaking up $4.7m in campaign spending in the 2022 cycle alone. It was used heavily by Democratic candidates including Mark Kelly, Joe Biden, Bernie Sanders and Adam Schiff. Spending on AmplifAI, now owned by the troubled media conglomerate Triller, seems to have tapered off in the years since 2022.

The new rising Democratic solution for AI-powered text messaging is Daisychain, which has so far garnered about $300,000 in reported candidate spend in the 2026 cycle—up from only about $50,000 reported in 2024. More than half of this year’s spending comes from the Senate campaign of Democrat Abdul El-Sayed in Michigan.

The closest equivalent on the Republican side has been Campaign Nucleus, associated with former Trump campaign manager Brad Parscale. The AI-powered voter engagement tool has attracted six-figure spending from the Republican National Committee, multiple PACs aligned with Donald Trump, and five-figure investments from Mike Johnson, Kari Lake and other candidates. It is displacing the legacy Republican-serving texting vendor Prompt.io, which has retained about $375,000 in 2026 spending to date, down from more than $500,000 in the 2022 cycle. But it continues to be used: the A More Affordable California PAC sponsored by Uber has single-handedly spent more than $1m on Prompt.io in 2026. The Republican Massachusetts gubernatorial nominee Michael Minogue has been a recurring customer, as has the failed Republican California gubernatorial candidate Ché Ahn and Republican-aligned Super PAC Neighbors for a Better Colorado.

At the state level

At the state level, the AI spending is smaller but growing fast. Across the four states studied, we found a total of at least $92,000 in spending confidently attributable to modern generative AI vendors since 2022. The spending is spread across at least 108 candidates and committees. The growth has been explosive; there has already been about 10 times the amount of state-level AI spending reported in 2026 as there was in all of 2024.

Much of the state spending mirrors federal patterns. Daisychain again has the highest overall spend, and OpenAI and Claude dominate among the general-purpose AI vendors. DonorAtlas—the AI-powered prospect research tool—sticks out for its usage in these states, sitting behind only Daisychain and OpenAI and buoyed up by nearly $4,000 in spending by the California Democratic party.

Even though it has dominated so much media conversation, few candidates seem to be reporting spending on AI tools designed specifically to create synthetic audio and video, also known as “deepfakes”. We found just six federal candidates or committees reporting spending on the popular AI audio generator tool from ElevenLabs, with total spending of about $1,400 led by independent candidate for Colorado’s sixth congressional district Samir Witta. The AI image generator service Midjourney has five reported federal campaign or committee users reporting about $1,600, led by Sholdon Daniels, the Republican primary runner-up in the Texas 30th district. Combined, those two firms had less than $100 in reported spend across the four states.

However, recent data from the Wesleyan Media Project shows that at least 164 political ads in this cycle have included AI-generated media, supported by at least $80m in ad spending. What this illustrates is that candidate and committee disclosure reports are just the tip of the iceberg. They don’t cover spending on AI by political consultants, media firms and other vendors hired by the campaigns or by PACs, or independent committees raising and spending money aimed at boosting candidates’ campaigns. Those entities aren’t required to disclose detailed expenditure reports, and are very likely where the bulk of campaign AI usage is happening.

Since a large fraction of all spending in the campaign cycle will happen in the final weeks leading to November, much remains to be seen about the totality of how campaigns will leverage AI and what impact its use will have on voters’ decisions.