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Legal Restrictions on Vulnerability Disclosure

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2025/11/legal-restrictions-on-vulnerability-disclosure.html

Kendra Albert gave an excellent talk at USENIX Security this year, pointing out that the legal agreements surrounding vulnerability disclosure muzzle researchers while allowing companies to not fix the vulnerabilities—exactly the opposite of what the responsible disclosure movement of the early 2000s was supposed to prevent. This is the talk.

Thirty years ago, a debate raged over whether vulnerability disclosure was good for computer security. On one side, full disclosure advocates argued that software bugs weren’t getting fixed and wouldn’t get fixed if companies that made insecure software wasn’t called out publicly. On the other side, companies argued that full disclosure led to exploitation of unpatched vulnerabilities, especially if they were hard to fix. After blog posts, public debates, and countless mailing list flame wars, there emerged a compromise solution: coordinated vulnerability disclosure, where vulnerabilities were disclosed after a period of confidentiality where vendors can attempt to fix things. Although full disclosure fell out of fashion, disclosure won and security through obscurity lost. We’ve lived happily ever after since.

Or have we? The move towards paid bug bounties and the rise of platforms that manage bug bounty programs for security teams has changed the reality of disclosure significantly. In certain cases, these programs require agreement to contractual restrictions. Under the status quo, that means that software companies sometimes funnel vulnerabilities into bug bounty management platforms and then condition submission on confidentiality agreements that can prohibit researchers from ever sharing their findings.

In this talk, I’ll explain how confidentiality requirements for managed bug bounty programs restrict the ability of those who attempt to report vulnerabilities to share their findings publicly, compromising the bargain at the center of the CVD process. I’ll discuss what contract law can tell us about how and when these restrictions are enforceable, and more importantly, when they aren’t, providing advice to hackers around how to understand their legal rights when submitting. Finally, I’ll call upon platforms and companies to adapt their practices to be more in line with the original bargain of coordinated vulnerability disclosure, including by banning agreements that require non-disclosure.

And this is me from 2007, talking about “responsible disclosure”:

This was a good idea—and these days it’s normal procedure—but one that was possible only because full disclosure was the norm. And it remains a good idea only as long as full disclosure is the threat.

Holiday Gift Guide 2025

Post Syndicated from Yev original https://www.backblaze.com/blog/holiday-gift-guide-2025/

An illustration of a gift box.

It’s that time of year again where the holidays are barreling towards us at an incredible rate. With so many cyber sales and new things hitting the market, it’s the most wonderful time of year to read gift guides. To help, I’ve asked my fine companions at Backblaze to list out some of their favorite gift ideas for this year, and I’ve compiled them for you here. Enjoy the suggestions, and my rambling commentary!

Couch Cady

The couch outside of my office and the recliner are where I spend the majority of my at-home time. What better way to keep your drinks, snacks, and second screens handy than this caddy?

Cereal Cup

Have you ever put cereal and milk into a yeti mug and drank it after everything’s turned into a sugary mush? Me neither, but now you don’t have to—this cup allows you to maintain two great tastes that taste great together, but without them having to touch until you’re ready!

Slow Cooker Seasoning Blends

For the chef in your life—or for the aspiring chef that never quite understood what the word “seasoning” meant—this is a great seasoning blend. Let’s face it, if you’re eating their food, it’s a gift for you too!

Schmidt Brothers Stainless Steel 10-Piece Knife Block Set

Another one for the chef’s in your life. This knife block bundle looks good, by all accounts feels good, and also cuts good—which is great. By the way, does anyone in your life use pairing knives? It’s something I’m just now getting the hang of…fun!

Timeless Six Wine Exploration Bundle

I have this, I use this, and I love this. It’s likely been in gift-guides of yesteryear but it’s just that good. Whether you’re a single human like me who doesn’t want to open a whole bottle and just wants a glass from time to time, or you’re an aficionado wanting to taste his bottles before opening them for a fancy dinner, this pumps inert argon gas into your wine bottles without having to open the cork. Great stuff.

Graze olive oil in glass bottles

Popularized by Tiktok videos, this olive oil stands up to the hype and adds a good amount of actual flavor to your dishes. Whether you’re drizzling or frying, it’s a good recommendation!

Sustainably Raised Meat (for cooks or people who BBQ)

And speaking of frying, Butcherbox allows you to order sustainability sourced meats and have them delivered right to your door. It might be too late if you’re looking for a Thanksgiving bird, but you might need a few steaks eventually!

Terra Kaffe Demi automatic espresso machine

Espresso. Some pronounce it with an “x.” And while it does give you a caffeine boost in a hurry, there’s nothing express about it…except for getting a machine like this that can really speed up and boost your home coffee consumption.

Fancy Onsen Towels

Soft, cuddly, and rapidly drying—these towels are a treat. I got to use one a few months ago while visiting a friend of mine and I still think about them, seriously! It might actually be time to rotate my towels and these are at the top of the list.

Cordless Cleaning and Scrubbing Brush

Cleaning yourself with onsen towels is great, but what about cleaning the actual shower or bath? These allow you to get all the nooks and crannies between the tiles, and because they’re cordless you can also use them on your car rims. Do you ever wash those? You should!

Guardian Kids Bike and Guardian Adult Bike

What’s one of my top-10 favorite things? Assembling Ikea furniture while listening to music. But I also enjoy biking! This combines both as the adult and children bikes are shipped to you and you get to assemble them at home before taking them on the road! It keeps costs low, and satisfaction high!

Oura Ring 4

I have been wearing fitness trackers for a very long time, from smartwatches to fitbits to step-counters, and this is my favorite one so far! I love that I can quickly charge it every 4-5 days and it gives me great insights into activity and sleep, without having to wear something bulky to bed.

Patagonia Black Hole Duffel

I am often envious of people who travel with duffel bags because of all the stuff you can fit into them. I still use my old-timey rollaboard, but I do have one of these that I throw in the car with me when I hit the ski slopes. These are great, come with backpack straps, and also have a lot of clips where you can hook carabineers up to (which is a great way to bring shoes you don’t want to pack).

Bev Ledge

Airplanes have come a long way but unless you’re sitting in your own personal pod up front (must be nice), there never seems to be enough room for all your stuff! This ledge fits neatly onto your window sill and allows for you to get a little more utility out of your surroundings! Just…don’t get an aisle seat.

Trtl Travel Pillow

As the Wu-Tang Clan once sang, “protect ya neck.” This travel pillow helps do just that when you’re on the go. No one likes falling asleep on a plane and waking up to a stiff neck—this helps!

Wyze Solar Cam Pan

These are great for monitoring the outside of your home, but the real killer app comes from using the indoor ones as baby monitors. Seriously, my family has been using them for years in the kiddos’ rooms, and not only is the quality much better than a standard baby monitor, but the recording features make for some really cute videos, and there’s no range restrictions. It’s a win-win-win.

Bambu Lab X1C 3D Printer

3D printing has become very mainstream over the last few years, and this at-home unit with space for multiple filaments makes it incredibly easy to create your favorite designs.

Kindle

Books. You know them, you love them, you should probably read one of my favorites from the last 10 years (The 7 ½ Deaths of Evelyn Hardcastle) and any of the Kindles should help you do just that! I’m a book on tape guy now because I tend to walk a lot (and if you have Audible I highly recommend the Dungeon Crawler Carl series), but if you enjoy holding something in your hands, the paper white Kindles are fantastic.

Classic Diablo (or any “good old game” from GOG”)

Classic games are classic for a reason! The recommendation from our org was for Diablo on Good Old Games, but there are a ton of classics there to choose from. One of my favorite series: Quest for Glory. Version 4 my favorite as it dives into Eastern European folktale lore!

Voice Activated Transformer Robots

Transformers have always been popular, and these voice-activated robots are what I dreamed of when I was a kiddo. Now that I’m no longer a kiddo, I still kind of want one, because watching toys “build themselves” gives me real Toy Story vibes.

K-pop Demon Hunters Wooble kit

K-Pop Demon Hunters took the world by storm, and now you can turn it into a crafting activity for you or your kiddos! Wobbles are little crochet kits, and come with everything you need to make something cute and squishy, right at home!

Retroid Pocket G2

This little gaming handheld allows you to emulate the games of your youth. Whether you liked playing the Sega Game Gear or the Game Boy, you can get all your favorites in one place with these on-the-go systems.

Moondrop Space Travel Headphones

On the go or on the couch, good headphones are paramount. These are relatively shallow and offer noise cancelling technology to keep the outside world out while you’re listening to tunes, audiobooks, or podcasts!

Give the gift of Backblaze

An image of a gift box with the words "Give Backblaze Backup" overlaid

Of course you can always give the gift of Computer Backup. It makes a great gift and helps keep the data of your family and friends safe and sound. Peace of mind is a great gift.

Something you’ve had on your mind didn’t make our list? Tell us in the comments—we love hearing what people are excited about!

The post Holiday Gift Guide 2025 appeared first on Backblaze Blog | Cloud Storage & Cloud Backup

AI and Voter Engagement

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2025/11/ai-and-voter-engagement.html

Social media has been a familiar, even mundane, part of life for nearly two decades. It can be easy to forget it was not always that way.

In 2008, social media was just emerging into the mainstream. Facebook reached 100 million users that summer. And a singular candidate was integrating social media into his political campaign: Barack Obama. His campaign’s use of social media was so bracingly innovative, so impactful, that it was viewed by journalist David Talbot and others as the strategy that enabled the first term Senator to win the White House.

Over the past few years, a new technology has become mainstream: AI. But still, no candidate has unlocked AI’s potential to revolutionize political campaigns. Americans have three more years to wait before casting their ballots in another Presidential election, but we can look at the 2026 midterms and examples from around the globe for signs of how that breakthrough might occur.

How Obama Did It

Rereading the contemporaneous reflections of the New York Times’ late media critic, David Carr, on Obama’s campaign reminds us of just how new social media felt in 2008. Carr positions it within a now-familiar lineage of revolutionary communications technologies from newspapers to radio to television to the internet.

The Obama campaign and administration demonstrated that social media was different from those earlier communications technologies, including the pre-social internet. Yes, increasing numbers of voters were getting their news from the internet, and content about the then-Senator sometimes made a splash by going viral. But those were still broadcast communications: one voice reaching many. Obama found ways to connect voters to each other.

In describing what social media revolutionized in campaigning, Carr quotes campaign vendor Blue State Digital’s Thomas Gensemer: “People will continue to expect a conversation, a two-way relationship that is a give and take.”

The Obama team made some earnest efforts to realize this vision. His transition team launched change.gov, the website where the campaign collected a “Citizen’s Briefing Book” of public comment. Later, his administration built We the People, an online petitioning platform.

But the lasting legacy of Obama’s 2008 campaign, as political scientists Hahrie Han and Elizabeth McKenna chronicled, was pioneering online “relational organizing.” This technique enlisted individuals as organizers to activate their friends in a self-perpetuating web of relationships.

Perhaps because of the Obama campaign’s close association with the method, relational organizing has been touted repeatedly as the linchpin of Democratic campaigns: in 2020, 2024, and today. But research by non-partisan groups like Turnout Nation and right-aligned groups like the Center for Campaign Innovation has also empirically validated the effectiveness of the technique for inspiring voter turnout within connected groups.

The Facebook of 2008 worked well for relational organizing. It gave users tools to connect and promote ideas to the people they know: college classmates, neighbors, friends from work or church. But the nature of social networking has changed since then.

For the past decade, according to Pew Research, Facebook use has stalled and lagged behind YouTube, while Reddit and TikTok have surged. These platforms are less useful for relational organizing, at least in the traditional sense. YouTube is organized more like broadcast television, where content creators produce content disseminated on their own channels in a largely one-way communication to their fans. Reddit gathers users worldwide in forums (subreddits) organized primarily on topical interest. The endless feed of TikTok’s “For You” page disseminates engaging content with little ideological or social commonality. None of these platforms shares the essential feature of Facebook c. 2008: an organizational structure that emphasizes direct connection to people that users have direct social influence over.

AI and Relational Organizing

Ideas and messages might spread virally through modern social channels, but they are not where you convince your friends to show up at a campaign rally. Today’s platforms are spaces for political hobbyism, where you express your political feelings and see others express theirs.

Relational organizing works when one person’s action inspires others to do this same. That’s inherently a chain of human-to-human connection. If my AI assistant inspires your AI assistant, no human notices and one’s vote changes. But key steps in the human chain can be assisted by AI. Tell your phone’s AI assistant to craft a personal message to one friend—or a hundred—and it can do it.

So if a campaign hits you at the right time with the right message, they might persuade you to task your AI assistant to ask your friends to donate or volunteer. The result can be something more than a form letter; it could be automatically drafted based on the entirety of your email or text correspondence with that friend. It could include references to your discussions of recent events, or past campaigns, or shared personal experiences. It could sound as authentic as if you’d written it from the heart, but scaled to everyone in your address book.

Research suggests that AI can generate and perform written political messaging about as well as humans. AI will surely play a tactical role in the 2026 midterm campaigns, and some candidates may even use it for relational organizing in this way.

(Artificial) Identity Politics

For AI to be truly transformative of politics, it must change the way campaigns work. And we are starting to see that in the US.

The earliest uses of AI in American political campaigns are, to be polite, uninspiring. Candidates viewed them as just another tool to optimize an endless stream of email and text message appeals, to ramp up political vitriol, to harvest data on voters and donors, or merely as a stunt.

Of course, we have seen the rampant production and spread of AI-powered deepfakes and misinformation. This is already impacting the key 2026 Senate races, which are likely to attract hundreds of millions of dollars in financing. Roy Cooper, Democratic candidate for US Senate from North Carolina, and Abdul El-Sayed, Democratic candidate for Senate from Michigan, were both targeted by viral deepfake attacks in recent months. This may reflect a growing trend in Donald Trump’s Republican party in the use of AI-generated imagery to build up GOP candidates and assail the opposition.

And yet, in the global elections of 2024, AI was used more memetically than deceptively. So far, conservative and far right parties seem to have adopted this most aggressively. The ongoing rise of Germany’s far-right populist AfD party has been credited to its use of AI to generate nostalgic and evocative (and, to many, offensive) campaign images, videos, and music and, seemingly as a result, they have dominated TikTok. Because most social platforms’ algorithms are tuned to reward media that generates an emotional response, this counts as a double use of AI: to generate content and to manipulate its distribution.

AI can also be used to generate politically useful, though artificial, identities. These identities can fulfill different roles than humans in campaigning and governance because they have differentiated traits. They can’t be imprisoned for speaking out against the state, can be positioned (legitimately or not) as unsusceptible to bribery, and can be forced to show up when humans will not.

In Venezuela, journalists have turned to AI avatars—artificial newsreaders—to report anonymously on issues that would otherwise elicit government retaliation. Albania recently “appointed” an AI to a ministerial post responsible for procurement, claiming that it would be less vulnerable to bribery than a human. In Virginia, both in 2024 and again this year, candidates have used AI avatars as artificial stand-ins for opponents that refused to debate them.

And yet, none of these examples, whether positive or negative, pursue the promise of the Obama campaign: to make voter engagement a “two-way conversation” on a massive scale.

The closest so far to fulfilling that vision anywhere in the world may be Japan’s new political party, Team Mirai. It started in 2024, when an independent Tokyo gubernatorial candidate, Anno Takahiro, used an AI avatar on YouTube to respond to 8,600 constituent questions over a seventeen-day continuous livestream. He collated hundreds of comments on his campaign manifesto into a revised policy platform. While he didn’t win his race, he shot up to a fifth place finish among a record 56 candidates.

Anno was RECENTLY elected to the upper house of the federal legislature as the founder of a new party with a 100 day plan to bring his vision of a “public listening AI” to the whole country. In the early stages of that plan, they’ve invested their share of Japan’s 32 billion yen in party grants—public subsidies for political parties—to hire engineers building digital civic infrastructure for Japan. They’ve already created platforms to provide transparency for party expenditures, and to use AI to make legislation in the Diet easy, and are meeting with engineers from US-based Jigsaw Labs (a Google company) to learn from international examples of how AI can be used to power participatory democracy.

Team Mirai has yet to prove that it can get a second member elected to the Japanese Diet, let alone to win substantial power, but they’re innovating and demonstrating new ways of using AI to give people a way to participate in politics that we believe is likely to spread.

Organizing with AI

AI could be used in the US in similar ways. Following American federalism’s longstanding model of “laboratories of democracy,” we expect the most aggressive campaign innovation to happen at the state and local level.

D.C. Mayor Muriel Bowser is partnering with MIT and Stanford labs to use the AI-based tool deliberation.io to capture wide scale public feedback in city policymaking about AI. Her administration said that using AI in this process allows “the District to better solicit public input to ensure a broad range of perspectives, identify common ground, and cultivate solutions that align with the public interest.”

It remains to be seen how central this will become to Bowser’s expected re-election campaign in 2026, but the technology has legitimate potential to be a prominent part of a broader program to rebuild trust in government. This is a trail blazed by Taiwan a decade ago. The vTaiwan initiative showed how digital tools like Pol.is, which uses machine learning to make sense of real time constituent feedback, can scale participation in democratic processes and radically improve trust in government. Similar AI listening processes have been used in Kentucky, France, and Germany.

Even if campaigns like Bowser’s don’t adopt this kind of AI-facilitated listening and dialog, expect it to be an increasingly prominent part of American public debate. Through a partnership with Jigsaw, Scott Rasmussen’s Napolitan Institute will use AI to elicit and synthesize the views of at least five Americans from every Congressional district in a project called “We the People.” Timed to coincide with the country’s 250th anniversary in 2026, expect the results to be promoted during the heat of the midterm campaign and to stoke interest in this kind of AI-assisted political sensemaking.

In the year where we celebrate the American republic’s semiquincentennial and continue a decade-long debate about whether or not Donald Trump and the Republican party remade in his image is fighting for the interests of the working class, representation will be on the ballot in 2026. Midterm election candidates will look for any way they can get an edge. For all the risks it poses to democracy, AI presents a real opportunity, too, for politicians to engage voters en masse while factoring their input into their platform and message. Technology isn’t going to turn an uninspiring candidate into Barack Obama, but it gives any aspirant to office the capability to try to realize the promise that swept him into office.

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

Friday Squid Blogging: Pilot Whales Eat a Lot of Squid

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2025/11/friday-squid-blogging-pilot-whales-eat-a-lot-of-squid.html

Short-finned pilot wales (Globicephala macrorhynchus) eat at lot of squid:

To figure out a short-finned pilot whale’s caloric intake, Gough says, the team had to combine data from a variety of sources, including movement data from short-lasting tags, daily feeding rates from satellite tags, body measurements collected via aerial drones, and sifting through the stomachs of unfortunate whales that ended up stranded on land.

Once the team pulled all this data together, they estimated that a typical whale will eat between 82 and 202 squid a day. To meet their energy needs, a whale will have to consume an average of 140 squid a day. Annually, that’s about 74,000 squid per whale. For all the whales in the area, that amounts to about 88,000 tons of squid eaten every year.

Research paper.

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.

Upcoming Speaking Engagements

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2025/11/upcoming-speaking-engagements-50.html

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

  • My coauthor Nathan E. Sanders and I are speaking at the Rayburn House Office Building in Washington, DC at noon ET on November 17, 2025. The event is hosted by the POPVOX Foundation and the topic is “AI and Congress: Practical Steps to Govern and Prepare.”
  • I’m speaking on “Integrity and Trustworthy AI” at North Hennepin Community College in Brooklyn Park, Minnesota, USA, on Friday, November 21, 2025, at 2:00 PM CT. The event is cohosted by the college and The Twin Cities IEEE Computer Society.
  • Nathan E. Sanders and I will be speaking at the MIT Museum in Cambridge, Massachusetts, USA, on December 1, 2025, at 6:00 pm ET.
  • Nathan E. Sanders and I will be speaking at a virtual event hosted by City Lights on the Zoom platform, on December 3, 2025, at 6:00 PM PT.
  • I’m speaking and signing books at the Chicago Public Library in Chicago, Illinois, USA, on February 5, 2026. Details to come.

The list is maintained on this page.

The Role of Humans in an AI-Powered World

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2025/11/the-role-of-humans-in-an-ai-powered-world.html

As AI capabilities grow, we must delineate the roles that should remain exclusively human. The line seems to be between fact-based decisions and judgment-based decisions.

For example, in a medical context, if an AI was demonstrably better at reading a test result and diagnosing cancer than a human, you would take the AI in a second. You want the more accurate tool. But justice is harder because justice is inherently a human quality in a way that “Is this tumor cancerous?” is not. That’s a fact-based question. “What’s the right thing to do here?” is a human-based question.

Chess provides a useful analogy for this evolution. For most of history, humans were best. Then, in the 1990s, Deep Blue beat the best human. For a while after that, a good human paired with a good computer could beat either one alone. But a few years ago, that changed again, and now the best computer simply wins. There will be an intermediate period for many applications where the human-AI combination is optimal, but eventually, for fact-based tasks, the best AI will likely surpass both.

The enduring role for humans lies in making judgments, especially when values come into conflict. What is the proper immigration policy? There is no single “right” answer; it’s a matter of feelings, values, and what we as a society hold dear. A lot of societal governance is about resolving conflicts between people’s rights—my right to play my music versus your right to have quiet. There’s no factual answer there. We can imagine machines will help; perhaps once we humans figure out the rules, the machines can do the implementing and kick the hard cases back to us. But the fundamental value judgments will likely remain our domain.

This essay originally appeared in IVY.

Book Review: The Business of Secrets

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2025/11/book-review-the-business-of-secrets.html

The Business of Secrets: Adventures in Selling Encryption Around the World by Fred Kinch (May 24, 2024)

From the vantage point of today, it’s surreal reading about the commercial cryptography business in the 1970s. Nobody knew anything. The manufacturers didn’t know whether the cryptography they sold was any good. The customers didn’t know whether the crypto they bought was any good. Everyone pretended to know, thought they knew, or knew better than to even try to know.

The Business of Secrets is the self-published memoirs of Fred Kinch. He was founder and vice president of—mostly sales—at a US cryptographic hardware company called Datotek, from company’s founding in 1969 until 1982. It’s mostly a disjointed collection of stories about the difficulties of selling to governments worldwide, along with descriptions of the highs and (mostly) lows of foreign airlines, foreign hotels, and foreign travel in general. But it’s also about encryption.

Datotek sold cryptographic equipment in the era after rotor machines and before modern academic cryptography. The company initially marketed computer-file encryption, but pivoted to link encryption—low-speed data, voice, fax—because that’s what the market wanted.

These were the years where the NSA hired anyone promising in the field, and routinely classified—and thereby blocked—publication of academic mathematics papers of those they didn’t hire. They controlled the fielding of strong cryptography by aggressively using the International Traffic in Arms regulation. Kinch talks about the difficulties in getting an expert license for Datotek’s products; he didn’t know that the only reason he ever got that license was because the NSA was able to break his company’s stuff. He had no idea that his largest competitor, the Swiss company Crypto AG, was owned and controlled by the CIA and its West German equivalent. “Wouldn’t that have made our life easier if we had known that back in the 1970s?” Yes, it would. But no one knew.

Glimmers of the clandestine world peek out of the book. Countries like France ask detailed tech questions, borrow or buy a couple of units for “evaluation,” and then disappear again. Did they break the encryption? Did they just want to see what their adversaries were using? No one at Datotek knew.

Kinch “carried the key generator logic diagrams and schematics” with him—even today, it’s good practice not to rely on their secrecy for security—but the details seem laughably insecure: four linear shift registers of 29, 23, 13, and 7 bits, variable stepping, and a small nonlinear final transformation. The NSA probably used this as a challenge to its new hires. But Datotek didn’t know that, at the time.

Kinch writes: “The strength of the cryptography had to be accepted on trust and only on trust.” Yes, but it’s so, so weird to read about it in practice. Kinch demonstrated the security of his telephone encryptors by hooking a pair of them up and having people listen to the encrypted voice. It’s rather like demonstrating the safety of a food additive by showing that someone doesn’t immediately fall over dead after eating it. (In one absolutely bizarre anecdote, an Argentine sergeant with a “hearing defect” could understand the scrambled analog voice. Datotek fixed its security, but only offered the upgrade to the Argentines, because no one else complained. As I said, no one knew anything.)

In his postscript, he writes that even if the NSA could break Datotek’s products, they were “vastly superior to what [his customers] had used previously.” Given that the previous devices were electromechanical rotor machines, and that his primary competition was a CIA-run operation, he’s probably right. But even today, we know nothing about any other country’s cryptanalytic capabilities during those decades.

A lot of this book has a “you had to be there” vibe. And it’s mostly tone-deaf. There is no real acknowledgment of the human-rights-abusing countries on Datotek’s customer list, and how their products might have assisted those governments. But it’s a fascinating artifact of an era before commercial cryptography went mainstream, before academic cryptography became approved for US classified data, before those of us outside the triple fences of the NSA understood the mathematics of cryptography.

This book review originally appeared in AFIO.

On Hacking Back

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2025/11/on-hacking-back.html

Former DoJ attorney John Carlin writes about hackback, which he defines thus: “A hack back is a type of cyber response that incorporates a counterattack designed to proactively engage with, disable, or collect evidence about an attacker. Although hack backs can take on various forms, they are—­by definition­—not passive defensive measures.”

His conclusion:

As the law currently stands, specific forms of purely defense measures are authorized so long as they affect only the victim’s system or data.

At the other end of the spectrum, offensive measures that involve accessing or otherwise causing damage or loss to the hacker’s systems are likely prohibited, absent government oversight or authorization. And even then parties should proceed with caution in light of the heightened risks of misattribution, collateral damage, and retaliation.

As for the broad range of other hack back tactics that fall in the middle of active defense and offensive measures, private parties should continue to engage in these tactics only with government oversight or authorization. These measures exist within a legal gray area and would likely benefit from amendments to the CFAA and CISA that clarify and carve out the parameters of authorization for specific self-defense measures. But in the absence of amendments or clarification on the scope of those laws, private actors can seek governmental authorization through an array of channels, whether they be partnering with law enforcement or seeking authorization to engage in more offensive tactics from the courts in connection with private litigation.

И пак за руската хибридна война срещу демокрациите

Post Syndicated from Григор original http://www.gatchev.info/blog/?p=2672

Ето ви сериозно съкратен разказ за една от многото руски групи за пропагандни лъжи, кръстена от изследователите на Майкрософт “Storm-1516”. (Числото е пореден номер.)

—-

Започнала е като клон на Агенцията за Интернет проучвания – руска пропагандна група, създадена от Евгений Пригожин и регистрирана като „частна фирма“, за да се прикрие фактът, че е управлявана от отдела за активни мероприятия в чужбина на КГБ. Отделното съществуване на Storm-1516 е забелязано за пръв път от група изследователи на дезинформацията в медиите в университета Клемсън.

Основният им канал за разпространение са създавани в YouTube и X акаунти, представящи се за разследващи журналисти или уисълблоуъри. След това разпространява чрез тях пропагандни лъжи на английски, руски, финландски, арабски и френски езици. Често една и съща лъжа се пуска през няколко акаунта, за да оставя впечатление за достоверност.

Основният ѝ метод е заснемане на фалшиви видеота с участие на актьори или генерирането им с AI (по-рано – с дийпфейкване). Вероятната локация на екипа, който заснема видеотата с актьори, е Санкт Петербург. Те често се позовават на източници, които всъщност не съществуват. Пуснатите от тях видеота, както и другите пропагандни лъжи, след това биват споделяни от контролирани от ГРУ ботмрежи за ехо-акаунти.

В командването на групата вероятно взимат участие, или се намират под общо командване с нея, антилибералният „Центр геополитических экспертиз“ и друга пропагандна група, известна като „Фондация для борьбы с несправедливости“. Тясно свързани с нея са:

– групата Storm-1099, специализирана в създаване на фалшиви или имитационни пропагандни уебсайтове
– групата Storm-1679, специализирана в създаване на фалшиви документи и новинарски рапорти от името на достоверно изглеждащи, но фалшиви организации
– Ruza Flood, Volga Flood и Rybar – организации, които също разпространяват фалшива информация, често под името на реална личност (напр. Rybar – Михаил Звинчук) или фалшива персона, изпълнявана от актьор (напр. „Саня от Флорида“).

Свързана с нея изглежда и руската медийна компания Tenet Media, затворена през 2024 г. Основната ѝ дейност е била да плаща на десни блогъри в САЩ да снимат видеота с поръчано от нея съдържание.

Някои от основните теми, по които Storm-1516 генерират фалшиво съдържание, са:

– Намаляване на подкрепата за западна помощ за Украйна срещу руската агресия

През май 2024 г. те пускат фалшиво видео, което показва как украински войници горят чучело на Доналд Тръмп.

Много от фалшивките им лъжат, че украинските лидери злоупотребяват със западната помощ – купуват си яхти, имения, луксозни коли, наркотици и т.н. Имало е случаи, когато американски политици са се хващали на тези лъжи.

– Подкрепа за Доналд Тръмп

Честа тяхна теза е че украински тролски ферми работят срещу избирането на Доналд Тръмп. Целта ѝ е да прикрие отлично доказания факт, че всички руски тролски ферми бяха ангажирани в подкрепа на Доналд Тръмп. Едно от фалшивите видеота показва „служител на украинска тролска ферма“, който „си признава“, че ЦРУ изпълнява заговор, за да попречи на Тръмп да стане президент.

– Свързване на Украйна с Хамас

През октомври 2023 г. те пускат фалшиво видео, в което „ръководител на Хамас“ (лицето му е скрито) „благодари на Зеленски за доставките на оръжие и муниции“. В реалността между Украйна и Хамас няма връзки. Същият актьор в друго видео, пуснато преди Олимпийските игри в Париж, предупреждава, че „Хамас ще атакуват Олимпийските игри“. Видеото е широко разпространено чрез руските тролски мрежи. Френските и израелските служби потвърждават, че е фалшиво.

– Подкрепа за Доналд Тръмп

От началото на лятото на 2024 г. до самите избори за президент в САЩ групата, както и всички други тролски ресурси на ГРУ, е съсредоточена върху подкрепа за Доналд Тръмп. Storm-1516 основно произвеждат фалшиви видеота и новини, обвиняващи кандидатите на демократите в престъпления и разпространяващи конспиративни теории спрямо тях. През това време групата оперира над 100 фалшиви уебсайта, които очернят демократите и хвалят Тръмп и Русия. Частта от групата, която се грижи за тези сайтове, е ръководена от Джон Марк Дугън – бивш полицай от Флорида, конспиративен теорист, уличен в платена проруска пропаганда и потърсил политическо убежище в Русия.

Един от разпространяваните от групата фалшификати е, че Сикрет Сървис са открили в имението на Тръмп подслушвателни устройства, монтирани там от ФБР. Тръмп и Ванс също споделят тази лъжа в социалните мрежи.

Друг гласи, че при посещение в Замбия Камала Харис е застреляла нелегално Касуба – популярен женски черен носорог (видът е застрашен от изчезване). Фалшификатът е споделен от над милион фалшиви акаунта в Х, както и от работещи за Русия западни пропагандисти (Chay Bowes и т.н.)

Друго тяхно фалшиво видео, разпространено от Джон Дугън, „показва“ как поддръжници на Камала Харис атакуват сбирка на поддръжници на Тръмп и пребиват един от посетителите. Към това са прикрепени обичайните опорки за „агресивните леви екстремисти“, и коментари, целящи да засилят междурасови напрежения в САЩ.

На друго тяхно фалшиво видео „нелегален имигрант от Хаити“ разказва „как му е било платено да гласува за Харис многократно с различни шофьорски книжки“.

На друго тяхно фалшиво видео, пуснато в деня на изборите, „американски гласоподавател“ разказва как двама поддръжници на Харис нападнали и пребили поддръжник на Тръмп, за да му попречат да гласува.

В много агресивно разпространявано тяхно видео, видяно от почти 3 милиона души, жена твърди че е била блъсната като тинейджърка през 2011 г. от Камала Харис с кола и оставена да умре. Била цялата изпотрошена, претърпяла 11 операции преди да проходи отново, била и до момента в постоянни болки и т.н… Жената, посочена в различни източници като „Алисия Браун“, „Алиша Браун“ и т.н., е платена актриса. Рентгеновата снимка, показана във видеото за нейна, е взета от медицинско списание. Снимката на катастрофата от видеото е от катастрофа в Гуам през 2018 г. Новинарската агенция, посочена във видеото като източник на материала, не съществува (с изключение на фалшив уебсайт, създаден и поддържан от Storm-1516). Частта от видеото, на която Камала Харис напуска мястото на катастрофата, е дийпфейк… Видеото е споделено от много конгресмени-републиканци, включително Дж. Д. Ванс.

През октомври 2024 г., точно преди изборите, Storm-1516 прави съвместна кампания с QAnon за изкарване на Тим Валц (кандидат за вицепрезидент на демократите) педофил. Близко до QAnon радио излъчва „интервюта“ с „родител“ (в ролята – Джон Дугън) на „ученик“ на Валц, и със „студент на Валц от Казахстан“. (Проверката показва, че в училището, където преподава Валц, за последните 20 години не е имало студенти от Казахстан.) Части от излъчването са споделени масово от руски тролски ботмрежи и са видени от над 800 000 души в САЩ.

Един от акаунтите, споделили излъчването, е “BlackInsurrectionist” – създаден и управляван от Storm-1516. Има го във всички големи социални мрежи, следван е от водещи републикански политици (вкл. Тръмп-младши и Роджър Стоун). С тяхна помощ излъчването стига до около 33 милиона души.

В средата на октомври 2024 г. този акаунт пуска видео с е-майл, за който твърди, че е от малолетен, сексуално преследван от Валц. Многобройни признаци, че видеото е фалшификат, са моментално посочени от много негови зрители, но търсенията в Гугъл за „Tim Waltz pedophile“ скачат стократно. Няколко дни по-късно друг акаунт за конспиративни теории, също управляван от Storm-1516, пуска друго видео с подобни твърдения. Трето подобно видео, очевидно дело на същата група, е пуснато от акаунт на QAnon. Анализ на експерти от Wired показва, че всичките тези видеота са дийпфейкове.

На 19 октомври почти всички сайтове, управлявани от Джон Дугън, пускат голям репортаж, който цитира тези видеота (и ги представя за истински). Репортажът е цитиран от десни инфлуенсъри, между тях Кендис Оуенс и Джак Пособиец. На 21 октомври експерти на Wired успяват да докажат, че видеотата са дело на Storm-1516.

Друго фалшиво видео, създадено от същата група и пуснато през акаунт на QAnon 2 дни преди изборите, показва как член на изборна комисия проверява пратени по пощата бюлетини и къса тези, които са за Доналд Тръмп. Експертите веднага забелязват, че материалите и бюлетините, показани във видеото, не са истински – очевидно реквизиторът на групата се е изложил.

… Мога да напиша още няколко пъти по толкова за тях. А като погледнете поредния им номер, се досещайте колко още са като тях.

Правете си изводите.

Prompt Injection in AI Browsers

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2025/11/prompt-injection-in-ai-browsers.html

This is why AIs are not ready to be personal assistants:

A new attack called ‘CometJacking’ exploits URL parameters to pass to Perplexity’s Comet AI browser hidden instructions that allow access to sensitive data from connected services, like email and calendar.

In a realistic scenario, no credentials or user interaction are required and a threat actor can leverage the attack by simply exposing a maliciously crafted URL to targeted users.

[…]

CometJacking is a prompt-injection attack where the query string processed by the Comet AI browser contains malicious instructions added using the ‘collection’ parameter of the URL.

LayerX researchers say that the prompt tells the agent to consult its memory and connected services instead of searching the web. As the AI tool is connected to various services, an attacker leveraging the CometJacking method could exfiltrate available data.

In their tests, the connected services and accessible data include Google Calendar invites and Gmail messages and the malicious prompt included instructions to encode the sensitive data in base64 and then exfiltrate them to an external endpoint.

According to the researchers, Comet followed the instructions and delivered the information to an external system controlled by the attacker, evading Perplexity’s checks.

I wrote previously:

Prompt injection isn’t just a minor security problem we need to deal with. It’s a fundamental property of current LLM technology. The systems have no ability to separate trusted commands from untrusted data, and there are an infinite number of prompt injection attacks with no way to block them as a class. We need some new fundamental science of LLMs before we can solve this.

Secure EKS clusters with the new support for Amazon EKS in AWS Backup

Post Syndicated from Veliswa Boya original https://aws.amazon.com/blogs/aws/secure-eks-clusters-with-the-new-support-for-amazon-eks-in-aws-backup/

Today, we’re announcing support for Amazon EKS in AWS Backup to provide the capability to secure Kubernetes applications using the same centralized platform you trust for your other Amazon Web Services (AWS) services. This integration eliminates the complexity of protecting containerized applications while providing enterprise-grade backup capabilities for both cluster configurations and application data. AWS Backup is a fully managed service to centralize and automate data protection across AWS and on-premises workloads. Amazon Elastic Kubernetes Service (Amazon EKS) is a fully managed Kubernetes service to manage availability and scalability of the Kubernetes clusters. With this new capability, you can centrally manage and automate data protection across your Amazon EKS environments alongside other AWS services.

Until now, for backups, customers relied on custom solutions or third-party tools to back up their EKS clusters, requiring complex scripting and maintenance for each cluster. The support for Amazon EKS in AWS Backup eliminates this overhead by providing a single, centralized, and policy-driven solution that protects both EKS clusters (Kubernetes deployments and resources) and stateful data (stored in Amazon Elastic Block Store (Amazon EBS), Amazon Elastic File System (Amazon EFS), and Amazon Simple Storage Service (Amazon S3) only) without the need to manage custom scripts across clusters. For restores, customers were previously required to restore their EKS backups to a target EKS cluster which was either the source EKS cluster, or a new EKS cluster, requiring that an EKS cluster infrastructure is provisioned ahead of time prior to the restore. With this new capability, during a restore of EKS cluster backups, customers also have the option to create a new EKS cluster based on previous EKS cluster configuration settings and restore to this new EKS cluster, with AWS Backup managing the provisioning of the EKS cluster on the customer’s behalf.

This support includes policy-based automation for protecting single or multiple EKS clusters. This single data protection policy provides a consistent experience across all services AWS Backup supports. It allows creation of immutable backups to prevent malicious or inadvertent changes, helping customers meet their regulatory compliance needs. In case there is a customer data loss or cluster downtime event, customers can easily recover their EKS cluster data from encrypted, immutable backups using an easy-to-use interface and maintain business continuity of running their EKS clusters at scale.

How it works
Here’s how I set up support for on-demand backup of my EKS cluster in AWS Backup. First, I’ll show a walkthrough of the backup process, then demonstrate a restore of the EKS cluster.

Backup
In the AWS Backup console, in the left navigation pane, I choose Settings and then Configure resources to opt in to enable protection of EKS clusters in AWS Backup.

Now that I’ve enabled Amazon EKS, in Protected resources I choose Create on-demand backup to create a backup for my already existing EKS cluster floral-electro-unicorn.

Enabling EKS in Settings ensures that it shows up as a Resource type when I create on-demand backup for the EKS cluster. I proceed to select the EKS resource type and the cluster.

I leave the rest of the information as default, then select Choose an IAM role to select a role (test-eks-backup) that I’ve created and customized with the necessary permissions for AWS Backup to assume when creating and managing backups on my behalf. I choose Create on-demand backup to finalize the process.


The job is initiated, and it will start running to back up both the EKS cluster state and the persistent volumes. If Amazon S3 buckets are attached to the backup, you’ll need to add the additional Amazon S3 backup permissions AWSBackupServiceRolePolicyForS3Backup to your role. This policy contains the permissions necessary for AWS Backup to back up any Amazon S3 bucket, including access to all objects in a bucket and any associated AWS KMS key.


The job is completed successfully and now EKS clusterfloral-electro-unicorn is backed up by AWS Backup.


Restore
Using the AWS Backup Console, I choose the EKS backup composite recovery point to start the process of restoring the EKS cluster backups, then choose Restore.


I choose Restore full EKS cluster to restore the full EKS backup. To restore to an existing cluster, I Choose an existing cluster then select the cluster from the drop-down list. I choose the Default order as the order in which individual Kubernetes resources will be restored.

I then configure the restore for the persistent storage resources, that will be restored alongside my EKS clusters.


Next, I Choose an IAM role to execute the restore action. The Protected resource tags checkbox is selected by default and I’ll leave it as is, then choose Next.

I review all the information before I finalize the process by choosing Restore, to start the job.


Selecting the drop-down arrow gives details of the restore status for both the EKS cluster state and persistent volumes attached. In this walkthrough, all the individual recovery points are restored successfully. If portions of the backup fail, it’s possible to restore the successfully backed up persistent stores (for example, Amazon EBS volumes) and cluster configuration settings individually. However, it’s not possible to restore full EKS backup. The successfully backed up resources will be available for restore, listed as nested recovery points under the EKS cluster recovery point. If there’s a partial failure, there will be a notification of the portion(s) that failed.


Benefits
Here are some of the benefits provided by the support for Amazon EKS in AWS Backup:

  • A fully managed multi-cluster backup experience, removing the overhead associated with managing custom scripts and third-party solutions.
  • Centralized, policy-based backup management that simplifies backup lifecycle management and makes it seamless to back up and recover your application data across AWS services, including EKS.
  • The ability to store and organize your backups with backup vaults. You assign policies to the backup vaults to grant access to users to create backup plans and on-demand backups but limit their ability to delete recovery points after they’re created.

Good to know
The following are some helpful facts to know:

  • Use either the AWS Backup Console, API, or AWS Command Line Interface (AWS CLI) to protect EKS clusters using AWS Backup. Alternatively, you can create an on-demand backup of the cluster after it has been created.
  • You can create secondary copies of your EKS backups across different accounts and AWS Regions to minimize risk of accidental deletion.
  • Restoration of EKS backups is available using the AWS Backup Console, API, or AWS CLI.
  • Restoring to an existing cluster will not override the Kubernetes versions, or any data as restores are non-destructive. Instead, there will be a restore of the delta between the backup and source resource.
  • Namespaces can only be restored to an existing cluster to ensure a successful restore as Kubernetes resources may be scoped at the cluster level.

Voice of the customer

Srikanth Rajan, Sr. Director of Engineering at Salesforce said “Losing a Kubernetes control plane because of software bugs or unintended cluster deletion can be catastrophic without a solid backup and restore plan. That’s why it’s exciting to see AWS rolling out the new EKS Backup and Restore feature, it’s a big step forward in closing a critical resiliency gap for Kubernetes platforms.”

Now available
Support for Amazon EKS in AWS Backup is available today in all AWS commercial Regions (except China) and in the AWS GovCloud (US) where AWS Backup and Amazon EKS are available. Check the full Region list for future updates.

To learn more, check out the AWS Backup product page and the AWS Backup pricing page.

Try out this capability for protecting your EKS clusters in AWS Backup and let us know what you think by sending feedback to AWS re:Post for AWS Backup or through your usual AWS Support contacts.

– Veliswa.

Network Stats for Q3 2025: The Magnitude of AI Workflows

Post Syndicated from Brent Nowak original https://www.backblaze.com/blog/network-stats-for-q3-2025-the-magnitude-of-ai-workflows/

A textured background with the words Network Stats overlaid.

The way data moves is changing in the age of AI. As AI training, model tuning, and inferencing accelerate massive, unpredictable flows of data across clouds, our network telemetry here at Backblaze offers a real-world view into the AI data gravity shift: where data lives, how it moves, and what it takes to keep it accessible and affordable.

Over the past couple of years, we’ve shared Network Stats snapshots that shed light on how data moves across Backblaze’s storage cloud. This quarter, we’re taking that foundation further, and evolving this series into a full-fledged transparency report that stands alongside Drive Stats with regular quarterly reporting and stats you can analyze for yourself.

This report isn’t just about traffic patterns. It’s a look at how data movement is changing in the age of AI and what those shifts reveal about performance, cost, and resilience at scale. 

Tune in live for The Stats Lab webinar

Drive Stats was the beginning. Want to see the evolution? Check out the Backblaze Stats Lab webinar, bringing together content from all of our Stats articles. We’re going to chat about all things Backblaze and beyond—by the numbers.

Save My Seat

In this first report, we’re going to outline the fundamentals of our dataset, highlight standout examples for AI related traffic, and lay the foundation to start sharing our quarter-over-quarter metrics.

Dataset details 

Our internal tools allow us to capture network flow data, meaning transmission control protocol (TCP) conversations between parties on our network. Along with basic information such as who is talking and how many bits are being exchanged, we have the ability to record additional pieces of anonymized information like what country, what ISP, or if we’ve seen a particular IP address before. And for each of these metrics, we have numbers for the average, 95th percentile, and maximum values.

Let’s talk about the three elements that make up our dataset: time, values, and metadata.

1. Monthly time slices

For every month, for every region, and for each direction (egress and ingress), we are data warehousing the following metrics. We plan on either using month-by-month numbers, or rolling up into a quarterly value for Network Stats reports going forward.

Item Detail
Date range Every month
Location scope Every region (eg US-West, EU-Central)
Network traffic direction Ingress, egress

2. Metric values

For each monthly snapshot, we’re recording the following details in our data warehouse. Capturing the average, 95th weighted value, and the maximum allows us enough information to profile our traffic. 

The 95th value (discarding the highest 5% bursts) gives us a good profile for daily operations and the maximum helps profile bust traffic. 

The most interesting metrics that I’m excited to explore are the “bits per IP” values. This combination of “amount of traffic” transferred with “how many actors are involved” per network is a good proxy for what I’m calling the “magnitude” of the network flow. We’re exploring the first insight into this metric below in our chart section.

Defining the Network Stats Quarterly Data

Item Field(s) Detail
Name name
asn
Common name and BGP ASN of the network
Bits bits_avg
bits_95th
bits_max
Number of bits/second
Packets packets_avg
packets_95th
packets_max
Number of packets/second
Flows flow_avg
flow_95th
flow_max
Number of TCP flows
IPs ip_unique_avg
ip_unique_95th
ip_unique_max
Number of unique IP addresses
Bits per IP bits_ip_avg
bits_ip_95th
bits_ip_max
Number of bits per IP address
Protocol v4
v6
Amount of traffic using IPv4 vs IPv6

3. Additional metadata

One of the first custom additions to our dataset is a category field. This helps us define the BGP ASNs (Autonomous System Number), basically the organizations common name associated with a range of IP addresses, that we talk to and group them into categories such as neocloud, hyperscaler, CDN, or general ISP.

Additional Network Stats Metadata

Item Field Detail
category group The class of network carrying the traffic (Cloud, PNI, traditional Internet Transit, or Internal Backblaze-Backblaze)
category type The type of network receiving the traffic (Neocloud, Hosting/Compute provider, Hyperscaler, CDN, Regional ISP, more localised ISP, etc)

The global picture

We started capturing this dataset in August of 2025, so we don’t yet have a good amount of data to pull out quarter over quarter trends. But what we can do for now is take a look into some standout metrics for the month of August that we’re interested in tracking over time.

First let’s take a look at where all our traffic goes from a global perspective.

When we look at the data, one pattern stands out immediately: traffic associated with neocloud networks—cloud providers offering compute, GPU, or other AI-related services—already represents nearly a quarter of total ingress and egress across Backblaze’s network. That’s a meaningful signal. Historically CDN traffic has been the majority of our traffic as our B2 Object Storage has been growing. Now, we’re seeing clear evidence of a new class of workload emerging, and it’s AI-shaped.

Neocloud network behavior

Let’s look at the magnitude of our network traffic based on the category of the traffic destination. To help quantify our data set, we interact with around 123,000 unique IP addresses every month. 

CDN, hyperscaler, isp-regional, and isp-tier one traffic cluster in the same general range of bits per IP, but neoclouds have a couple outliers—the two purple data points in the upper right corner of the log scale graph. 

If we change the scale to linear (chart below), now we can see how much of an outlier the AI related traffic is in our sample range.

The “magnitude” (as we’re calling it) of the transfers we’re servicing for AI related flows to neoclouds is an order of magnitude greater than all our traffic patterns. This means that there are only a few unique IP addresses that we’re interacting with transferring large amounts of data in their flows.

The rise of AI-driven data movement

Over the past year, AI training and inference have transformed global data flows. Where traditional workloads move steadily, AI workloads move in bursts—rapid retrievals of massive datasets, short high-volume transfers for model training or tuning, and sustained outbound throughput for inferencing pipelines. The magnitude metric we’re introducing (bits per IP address) captures this shift. 

As shown in the charts above, AI-related traffic to neoclouds isn’t just heavier, it’s denser. Those purple data points represent a small number of IPs exchanging a disproportionate amount of data. That concentration of flow is a hallmark of AI compute pipelines, where a few high-bandwidth endpoints (often GPU clusters) interact with object storage to repeatedly feed and retrieve training data. 

In other words:

  • Fewer talkers, bigger flows. AI systems operate in fewer, more intense network sessions than traditional applications.
  • Shorter duration, higher peaks. Transfer patterns spike sharply, often corresponding to dataset replication or model checkpointing cycles.
  • Cross-cloud mobility. Much of this traffic routes between Backblaze and external compute platforms (classified as neoclouds) showing the rise of multi-cloud AI architectures.

The macro trend: The AI data gravity shift

This pattern reflects a broader macro trend in the cloud ecosystem: AI data gravity is pulling more storage and compute closer together. As AI models grow larger and datasets become more complex, organizations are rethinking where data “lives.” Instead of centralizing everything in one hyperscaler like AWS or Google Cloud Platform, they’re increasingly using cost-efficient, high-throughput storage clouds like Backblaze connected to specialized GPU clouds for compute (case in point: Why CoreWeave’s Object Storage Launch is Good for AI—and Everyone Building It).

This architectural shift explains the outlier traffic patterns we’re seeing on our network. Data isn’t just moving more—it’s moving smarter, following cost, performance, and regional availability cues. 

Why it matters

Tracking this kind of data movement and magnitude helps us, and more importantly our customers, understand a few key things:

  • Operational readiness for AI workloads: How our network scales under bursty, compute-linked demands. (For more on this check out Making the Backblaze Network AI Ready)
  • Cost predictability: Where and when ingress or egress volume spikes may occur.
  • Industry evolution: How AI is reshaping the underlying patterns of internet traffic.

What’s next?

This is just the first glimpse of that industry evolution. As our dataset matures, we’ll be able to watch these AI-linked flows change quarter over quarter, offering not just transparency, but a longitudinal view of how the data backbone of the AI economy takes shape. 

We’re planning to look at quarter over quarter number tracking for network types, IPv4 traffic vs IPv6 traffic, AI related workflows, cross-cloud connectivity trends, and more. We’re also planning to release the raw data quarterly going forward.

Anything specific you want to see? Let us know in the comments or reach out to our Evangelism team. 

We’re excited to share these insights from our network telemetry, the patterns we’re seeing, and what they mean for the broader data economy. This is the stuff we stay up at night studying, and sharing it publicly means we can all better understand the forces shaping digital infrastructure and build with greater confidence and foresight. 

The post Network Stats for Q3 2025: The Magnitude of AI Workflows appeared first on Backblaze Blog | Cloud Storage & Cloud Backup

New Attacks Against Secure Enclaves

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2025/11/new-attacks-against-secure-enclaves.html

Encryption can protect data at rest and data in transit, but does nothing for data in use. What we have are secure enclaves. I’ve written about this before:

Almost all cloud services have to perform some computation on our data. Even the simplest storage provider has code to copy bytes from an internal storage system and deliver them to the user. End-to-end encryption is sufficient in such a narrow context. But often we want our cloud providers to be able to perform computation on our raw data: search, analysis, AI model training or fine-tuning, and more. Without expensive, esoteric techniques, such as secure multiparty computation protocols or homomorphic encryption techniques that can perform calculations on encrypted data, cloud servers require access to the unencrypted data to do anything useful.

Fortunately, the last few years have seen the advent of general-purpose, hardware-enabled secure computation. This is powered by special functionality on processors known as trusted execution environments (TEEs) or secure enclaves. TEEs decouple who runs the chip (a cloud provider, such as Microsoft Azure) from who secures the chip (a processor vendor, such as Intel) and from who controls the data being used in the computation (the customer or user). A TEE can keep the cloud provider from seeing what is being computed. The results of a computation are sent via a secure tunnel out of the enclave or encrypted and stored. A TEE can also generate a signed attestation that it actually ran the code that the customer wanted to run.

Secure enclaves are critical in our modern cloud-based computing architectures. And, of course, they have vulnerabilities:

The most recent attack, released Tuesday, is known as TEE.fail. It defeats the latest TEE protections from all three chipmakers. The low-cost, low-complexity attack works by placing a small piece of hardware between a single physical memory chip and the motherboard slot it plugs into. It also requires the attacker to compromise the operating system kernel. Once this three-minute attack is completed, Confidential Compute, SEV-SNP, and TDX/SDX can no longer be trusted. Unlike the Battering RAM and Wiretap attacks from last month—which worked only against CPUs using DDR4 memory—TEE.fail works against DDR5, allowing them to work against the latest TEEs.

Yes, these attacks require physical access. But that’s exactly the threat model secure enclaves are supposed to secure against.

Friday Squid Blogging: Squid Game: The Challenge, Season Two

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2025/11/friday-squid-blogging-squid-game-the-challenge-season-two.html

The second season of the Netflix reality competition show Squid Game: The Challenge has dropped. (Too many links to pick a few—search for it.)

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.

Faking Receipts with AI

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2025/11/faking-receipts-with-ai.html

Over the past few decades, it’s become easier and easier to create fake receipts. Decades ago, it required special paper and printers—I remember a company in the UK advertising its services to people trying to cover up their affairs. Then, receipts became computerized, and faking them required some artistic skills to make the page look realistic.

Now, AI can do it all:

Several receipts shown to the FT by expense management platforms demonstrated the realistic nature of the images, which included wrinkles in paper, detailed itemization that matched real-life menus, and signatures.

[…]

The rise in these more realistic copies has led companies to turn to AI to help detect fake receipts, as most are too convincing to be found by human reviewers.

The software works by scanning receipts to check the metadata of the image to discover whether an AI platform created it. However, this can be easily removed by users taking a photo or a screenshot of the picture.

To combat this, it also considers other contextual information by examining details such as repetition in server names and times and broader information about the employee’s trip.

Yet another AI-powered security arms race.

How AI shapes your feed: An explainable social media simulator for the classroom

Post Syndicated from Diana Kirby original https://www.raspberrypi.org/blog/how-ai-shapes-your-feed-an-explainable-social-media-simulator-for-the-classroom/

Social media can have a powerful impact on the way we see and experience the world. What we see in our feeds is not random: it is determined by AI-driven systems that collect vast amounts of data, build user profiles, analyse engagement, and generate recommendations. But while young people are prolific users of social media, studies show that many have little understanding of what is happening ‘under the hood’

Henriikka Vartiainen and Matti Tedre from the University of Eastern Finland
Researchers Henriikka Vartiainen and Matti Tedre.

In our September research seminar, we welcomed back Henriikka Vartiainen and Matti Tedre from the University of Eastern Finland. They introduced Somekone, a social media simulator that is designed to help learners understand some of the fundamental processes behind social media platforms. Their team has been developing AI education materials and tools since 2019, including GenAI Teachable Machine, which they presented at our May research seminar.

Collaboration and co-design

Henriikka explained that the development of the Somekone tool emerged from the team’s long-term collaboration with teachers and schools in Finland. They co-developed the tool with the aim of making concepts like data collection, engagement, profiling, recommendations, filter bubbles, and polarisation visible and explainable for students aged 11 to 13 years old.

Photo of three school pupils together looking at a mobile phone.

A four-phase learning model

Henriikka described the pedagogical model that the team follows in all of their AI education interventions. Their goal is not only to support students to develop their understanding of AI concepts, but also to foster ethical awareness and a sense of agency.

  • Phase 1: Contextualisation and familiarisation
    Students begin by discussing their experiences with social media and their initial ideas about how platforms such as TikTok, YouTube, and Instagram work. This activates students’ prior knowledge and helps connect the learning to their own interests. It also enables teachers to uncover any misconceptions the students may have.
  • Phase 2: Exploration
    Students explore their initial ideas by experimenting with the Somekone tool. They discover how different types of data are collected and combined for profiling in a way that connects these new concepts to their own everyday lives.
  • Phase 3: Design and inquiry
    Students explore the Somekone tool more deeply. Teachers guide them through activities where the students analyse, interpret, and discuss the data they can see in the tool. Importantly, the data they are using has all been gathered from their activity on the platform. Students can see how the likes, follows, and comments they and their classmates make change the images they are shown, and this is all real time.
  • Phase 4: Ethical and societal reflection
    Students reflect on what they have learnt and consider the broader impacts of social media. Teachers encourage them to think critically, question the way social media platforms currently work, and imagine alternatives. At the end of the project, students write letters to decision-makers with their suggestions for how social media could better serve children’s interests.

Inside the simulator

Matti then gave a live demonstration of Somekone. Nothing compares to seeing the tool in action, so do check out the video of his demo here!

Students log on to the tool and are presented with an Instagram-style feed of images. They scroll through the feed and like, share, or comment on images that catch their attention or match their interests. For many students this is a very familiar type of environment, and they really enjoy playing with the app!

Four young people sitting at their desks, on their mobile phones.

However, the unique value of Somekone is that it provides students with a real-time view of the way data is collected from every single user interaction, and demonstrates what is done with that data. It also allows students to experiment with a social media tool in the classroom without any data protection issues, as all of the data is stored locally.

Learners explore:

  • Data collection in real time. Working in pairs, one student browses the image feed, while the other watches a live view of the data that the simulator is collecting every time their partner interacts with or simply pauses on a post.
  • Profile building. Somekone shows how all this data accumulates to build a profile. Students watch their profiles developing based on the way they and their classmates are interacting with their feeds.
  • Clustering and connections. Students then see how the tool groups profiles to create clusters of users with similar interests. Often friendship groups in the classroom are evident on screen because students sitting next to each other have all chosen to engage with the same things!
The simulator creates clusters of users with similar interests, which update in real time as students interact with posts on their feeds

The simulator creates clusters of users with similar interests, which update in real time as students interact with posts on their feeds

  • Explainable recommendations. A key feature of Somekone is that it provides explanations for why it recommends posts to users. Students learn that recommendations can be based on various things, such as the image’s tag matching the tag on other posts they liked, or the image being popular among other users with similar profiles to theirs. These are the mechanisms that underpin real recommendation systems, but Somekone makes them explicit.
The tool provides an explanation for why each post is recommended

The tool provides an explanation for why each post is recommended

  • Filter bubbles and polarisation. A filter bubble forms when a user only sees social media posts that match their existing interests or beliefs, due to highly personalised recommendation systems. Somekone presents this concept in a visually compelling way through a heatmap showing all the content in the system, with a colour scale indicating which posts are most likely to be shown to a particular user, and which they will never encounter. By comparing different users’ filter bubbles side by side, students start to understand how polarisation can arise. As Matti said: “If our feeds are so different from each other that I never see the pictures that you see and you never see the pictures I see, then […] we don’t even share the same reality”.
Two users’ heatmaps presented side by side, showing their respective filter bubbles

Two users’ heatmaps presented side by side, showing their respective filter bubbles

  • Algorithm settings. A key learning opportunity is that students can adjust the algorithm’s parameters and observe how this changes their feed and their filter bubble. They can choose between personalised or non-personalised recommendations, select how posts are ranked, and decide whether to allow any diversity in the popularity of posts recommended to them. This is key to ‘opening up the box’.

For teachers, the tool has a simple guided interface to make it easy to use in class. There is also a button that teachers can use to pause the app, stopping students from scrolling (much to their dismay!) in order to focus their attention on the teacher when they are explaining concepts.

Evidence of impact

The research team used pre- and post-tests to evaluate what impact the intervention had on students’ understanding of social media mechanisms and on their sense of agency in relation to data. They conducted the post-test a week after the intervention, and then also did a delayed post-test six months later to see whether any changes were sustained. They found:

  • Improved understanding of key concepts. Learners showed statistically significant improvements in identifying different types of data traces and in understanding how data profiling works. They also showed some improvement in grasping recommendation mechanisms.
  • Retention over time. These improvements were generally still evident six months later, particularly in the case of understanding data traces.
  • Stronger sense of agency. The team found that students’ sense of data agency improved after taking part in the intervention. This is really important as students are more likely to want to study a topic further if they have feelings of agency and self-efficacy.

Accessing the tool

The Somekone tool is freely available online — in Finnish, English, German, and French — at somekone.gen-ai.fi. The developer Nick Pope has also made the source code available on GitHub at github.com/knicos/genai-somekone. 

However, the supporting materials and teacher resources are currently only available in Finnish and the underpinning pedagogies relate to the Finnish context.

Join our next seminar

Join us at our next seminar on Tuesday, 11 November from 17:00 to 18:30 GMT to hear Karl-Emil Bilstrup (Copenhagen University) speak about using the micro:bit to explore machine learning practices. We hope to see you there!

To sign up and take part in our research seminars, click below:

You can also view the schedule of our upcoming seminars, and catch up on past seminars on our previous seminars page.

The post How AI shapes your feed: An explainable social media simulator for the classroom appeared first on Raspberry Pi Foundation.

Rigged Poker Games

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2025/11/rigged-poker-games.html

The Department of Justice has indicted thirty-one people over the high-tech rigging of high-stakes poker games.

In a typical legitimate poker game, a dealer uses a shuffling machine to shuffle the cards randomly before dealing them to all the players in a particular order. As set forth in the indictment, the rigged games used altered shuffling machines that contained hidden technology allowing the machines to read all the cards in the deck. Because the cards were always dealt in a particular order to the players at the table, the machines could determine which player would have the winning hand. This information was transmitted to an off-site member of the conspiracy, who then transmitted that information via cellphone back to a member of the conspiracy who was playing at the table, referred to as the “Quarterback” or “Driver.” The Quarterback then secretly signaled this information (usually by prearranged signals like touching certain chips or other items on the table) to other co-conspirators playing at the table, who were also participants in the scheme. Collectively, the Quarterback and other players in on the scheme (i.e., the cheating team) used this information to win poker games against unwitting victims, who sometimes lost tens or hundreds of thousands of dollars at a time. The defendants used other cheating technology as well, such as a chip tray analyzer (essentially, a poker chip tray that also secretly read all cards using hidden cameras), an x-ray table that could read cards face down on the table, and special contact lenses or eyeglasses that could read pre-marked cards.

News articles.

Cómo Enviar SMS Internacionales con Números Gratuitos de EE.UU. Usando AWS End User Messaging

Post Syndicated from Bruno Giorgini original https://aws.amazon.com/blogs/messaging-and-targeting/como-enviar-sms-internacionales-con-numeros-gratuitos-de-ee-uu-usando-aws-end-user-messaging/

AWS End User Messaging ahora admite capacidades de SMS internacional para Números Gratuitos de EE.UU. (TFN). Esta nueva función permite a las empresas usar un solo TFN de EE.UU. para enviar mensajes SMS a más de 150 países, simplificando el alcance global. Beneficia principalmente a clientes que necesitan enviar alertas transaccionales unidireccionales—como contraseñas de un solo uso (OTP) o notificaciones de envío—y empresas que quieren crear prototipos rápidamente y probar su estrategia de mensajería en nuevos mercados internacionales sin la complejidad de adquirir números específicos por país.

Esta guía te mostrará los pros y contras de esta función y cómo habilitarla y cuándo usarla versus métodos tradicionales de envío específicos por país.

¿Qué Son los Números Gratuitos Internacionales de EE.UU.?

Un número gratuito internacional de EE.UU. es un TFN (toll-free number) estándar de EE.UU. que ha sido habilitado con la capacidad de enviar mensajes SMS a destinos fuera de Estados Unidos. Esta función es compatible con versiones anteriores, lo que significa que puedes habilitarla en cualquier TFN de EE.UU. nuevo o existente en tu cuenta.

Cómo Habilitar el Envío Internacional

Hay tres formas principales de habilitar esta función para tus Números Gratuitos de EE.UU.:

  • Habilitar el envío internacional al registrar un nuevo número en la consola.
  • Habilitar el envío internacional para un número existente en la consola.
  • Habilitar el envío internacional para un número existente a través del AWS CLI.

1. Habilitar Al Registrar un Nuevo Número Gratuito de EE.UU. (Consola)

  • Desde la consola de AWS End User Messaging, navega a Administrar SMS
  • Desde la consola de AWS End User Messaging, navega a Configuraciones > Números de teléfono > y selecciona Originador de la solicitud
  • Paso 1: Seleccione un país, selecciona Estados Unidos (US) como tu país de destino
  • En Paso 2: Defina el caso de uso, configura las diversas opciones listadas para tu Caso de uso de mensajería previsto, y selecciona Sí para habilitar el envío Internacional, antes de hacer clic en Siguiente
  • Para Paso 3: Seleccionar tipo de originador, selecciona Gratuito, valida tus opciones de Política de recursos, selecciona Siguiente
  • En Paso 4: Revisar y solicitar: Verifica que la información que ingresaste sea correcta y selecciona Solicitar. Nota: Las solicitudes de registro de números gratuitos de EE.UU. pueden tomar aproximadamente 15 días hábiles para ser aprobadas.

Para más información, consulta Solicitar un número de teléfono en AWS End User Messaging SMS

2. Habilitar para un Número Gratuito de EE.UU. Existente (Consola o CLI)

Si ya has adquirido un TFN, puedes habilitar la función de envío internacional en cualquier momento.

Usando la Consola de Administración de AWS:

  • Navega a Configuraciones > Números de teléfono > y selecciona un número Gratuito existente
  • Localiza la pestaña Envío internacional y elige Editar configuración
  • Marca la casilla Habilitar envío internacional en los detalles de tu número de teléfono
    • Guardar Cambios

Usando el AWS CLI

El comando update-phone-number te permite modificar las capacidades de un número de teléfono, mientras que el comando describe-phone-numbers te permite verificar su estado.

1. Para Habilitar el Envío Internacional:

Usa el parámetro --international-sending-enabled

aws pinpoint-sms-voice-v2 update-phone-number \
    --phone-number-id "phone-a1b2c3d4e5f67890" \
    --international-sending-enabled \
    --region us-east-1

Nota: Reemplaza "phone-a1b2c3d4e5f67890" con el ID real de tu número de teléfono

2. Para Deshabilitar el Envío Internacional:

Usa el parámetro --no-international-sending-enabled

aws pinpoint-sms-voice-v2 update-phone-number \
    --phone-number-id "phone-a1b2c3d4e5f67890" \
    --no-international-sending-enabled \
    --region us-east-1

Respuesta Esperada (para update-phone-number):

Un comando exitoso devuelve el objeto JSON completo para el número de teléfono. Confirma el cambio verificando que el valor InternationalSendingEnabled sea true

{
    "PhoneNumberArn": "arn:aws:sms-voice:us-east-1:111122223333:phone-number/phone-a1b2c3d4e5f67890",
    "PhoneNumberId": "phone-a1b2c3d4e5f67890",
    "PhoneNumber": "+18005550199",
    "Status": "ACTIVE",
    "IsoCountryCode": "US",
    "MessageType": "TRANSACTIONAL",
    "NumberCapabilities": [
        "SMS"
    ],
    "NumberType": "TOLL_FREE",
    "MonthlyLeasingPrice": "2.00",
    "TwoWayEnabled": true,
    "InternationalSendingEnabled": true,
    "CreatedTimestamp": "2025-08-15T10:30:00.123Z"
}

3. Para Verificar el Estado Actual:

Usa el comando describe-phone-numbers con tu ID de Número de Teléfono para verificar su configuración actual en cualquier momento.

aws pinpoint-sms-voice-v2 describe-phone-numbers \
    --phone-number-ids "phone-a1b2c3d4e5f67890" \
    --region us-east-1

Beneficios y Limitaciones

Esta función ofrece una nueva forma poderosa de llegar a una audiencia global, pero es importante entender dónde destaca y cuáles son sus limitaciones.

Beneficios (Ventajas)

  • Alcance Global con un Solo Número: Envía SMS a más de 150 países usando un solo TFN de EE.UU. existente.
  • Gestión Simplificada: Evita la complejidad operacional y el costo de comprar y gestionar una flota de números de teléfono específicos por país.
  • Prototipado y Pruebas Rápidas: Prueba rápidamente campañas de mensajería en nuevos mercados internacionales antes de comprometerte con el enfoque de mejores prácticas de adquirir números dedicados en el país.
  • Optimización de Costos para Alertas Unidireccionales: Proporciona un método rentable para enviar mensajes transaccionales unidireccionales de alto volumen como OTP, recordatorios de citas y notificaciones de envío globalmente.

Limitaciones y Consideraciones Técnicas

  • SMS Bidireccional Limitado a EE.UU. y Canadá: Las conversaciones SMS bidireccionales confiables solo son compatibles para destinatarios en Estados Unidos y Canadá.
  • Solo Unidireccional para Todos los Otros Países: Para todos los otros destinos, esto es solo unidireccional.
  • Entrega con máximo esfuerzo (no garantizado): El envío fuera de EE.UU. y Canadá es con máximo esfuerzo (no garantizado). El número de teléfono que aparece en el dispositivo del destinatario puede ser reemplazado con un número local o ID de Remitente, por lo que la mensajería bidireccional no funcionará para estos destinos. Para más detalles sobre maximizar la entrega, lee Una Guía para Optimizar la Entrega de SMS y Mejores Prácticas (Inglés).
  • La Exclusión Gestionada No Está Garantizada Internacionalmente: La funcionalidad automática de respuesta STOP no funciona para destinos fuera de EE.UU. y Canadá. Para destinatarios internacionales, debes proporcionar un método alternativo de exclusión.
  • Rendimiento Estándar (3 MPS): Los TFN internacionales tienen un rendimiento predeterminado de 3 Partes de Mensaje Por Segundo (MPS). Para campañas de alto volumen y alto rendimiento, los números específicos por país dedicados (como códigos cortos) son la mejor práctica recomendada.

Entendiendo el Costo

El precio para esta función es directo:

  • Sin Tarifas Mensuales Adicionales: No hay cargo extra por habilitar la capacidad de envío internacional en tu TFN de EE.UU. Solo pagas el arrendamiento mensual estándar por el número mismo.
  • Mensajería de Pago por Uso: Se te factura por cada mensaje SMS saliente a la tarifa estándar por mensaje para el país de destino.

Para una lista completa y actualizada de precios por país, visita la página de Precios de AWS End User Messaging.

Cuándo Usar TFN Internacional vs. Números Específicos por País

Elegir la herramienta correcta depende de tu caso de uso. Aquí hay una comparación simple:

Caso de Uso ¿Usar TFN Internacional? ¿Usar Número Específico por País (Mejor Práctica)?
Probar rápidamente un nuevo mercado Sí. Es la forma más rápida de comenzar. No, este enfoque toma más tiempo para configurar.
Enviar alertas unidireccionales (OTP, notificaciones) Sí. Es una solución simple y rentable. Sí, pero es más complejo si necesitas enviar a muchos países.
Requerir conversaciones bidireccionales Solo para EE.UU. y Canadá. Sí. Este es el enfoque requerido para SMS bidireccional confiable en un país específico.
Garantizar que tu marca/número aparezca consistentemente No, planifica que el ID del Remitente no se preserve, ya que esto no está garantizado internacionalmente. Sí. Esta es la razón principal para usar un número dedicado en el país.
Maximizar la entregabilidad para campañas críticas No, la entrega es “con máximo esfuerzo”. Sí. Un número local proporciona la mayor probabilidad de entrega exitosa.

Consideraciones y Próximos Pasos

Una vez que hayas habilitado tu envío internacional sobre Números Gratuitos de EE.UU., puedes mejorar tu estrategia de mensajería considerando resistencia, monitoreo y escalabilidad. Los siguientes recursos proporcionan mejores prácticas para mejorar tu envío.

Conclusión

El SMS Internacional para Números Gratuitos de EE.UU. es una herramienta estratégica poderosa para empresas que buscan simplificar su mensajería global. Destaca en permitir pruebas rápidas en nuevos mercados y entregar eficientemente alertas transaccionales unidireccionales en todo el mundo desde un solo número.

Sin embargo, no es un reemplazo para la mejor práctica de usar números de teléfono dedicados en el país cuando conversaciones bidireccionales confiables y marca garantizada son críticas para el éxito de tu campaña. Al entender sus beneficios y limitaciones, puedes usar estratégicamente esta función para comenzar rápidamente mientras planificas un movimiento a largo plazo hacia códigos específicos por país para tus mercados más importantes.

Making the Backblaze Network AI Ready

Post Syndicated from Brent Nowak original https://www.backblaze.com/blog/making-the-backblaze-network-ai-ready/

An illustration of a chip with AI written on it.

AI isn’t just reshaping how data is processed—it’s rewriting how data moves. Behind every training run or inference pipeline is a torrent of data, and how efficiently (or not) that data travels through networks (and whether it’s an AI-ready network) can make or break performance. 

Data workloads have massively evolved over the 18 years we’ve been in business from computer backups to exabyte-scale storage to AI data pipelines. And that has implications for not just our storage hardware, but our network. 

What started as a single ISP serving a few racks in the early days has grown into a global, multi-terabit backbone connecting customers, compute, and storage in real time via multiple Tier 1 carriers, Internet Exchanges, and PNI links. 

So why talk about it now? Because AI is testing the limits of every part of the infrastructure stack—and the network is where those limits are most visible. Running an AI-ready network means rethinking how you design, route, and scale traffic to handle not just more data, but faster, more synchronized, and more resilient data movement than ever before.

In this post, I’m talking about how our network has evolved to support AI workflows, including what’s changed under the hood, how we’re adapting our hardware and architecture, and what that means for the way data moves through Backblaze today.

Go with the flow

The Network Engineering (NetEng) group at Backblaze is responsible for the design, implementation, and support of our physical network—everything from the physical copper and fiber cables inside our datacenters to the routers and switches that connect our storage to the world.

When we talk about network traffic, we often refer to a “flow”—a stream of information sent between two or more parties. Downloading a file? That’s a flow between your computer and the server offering the file. Multiple small requests loading a website (text, formatting code, animation code, etc.)? Those are known as “mouse” flows. Massive dataset transfers that sustain hundreds of gigabits per second? Those are “elephant” flows. 

The elephant in the room

AI workloads are the largest “elephant” flows our network has ever sustained. These aren’t just big files, they’re ecosystems of data: multi-petabyte datasets, hundreds of thousands of objects ranging from a single megabyte to hundreds of megabytes per object, and thousands of simultaneous connections working in parallel.

Moving these data sets around is no small task. It means engineering for sustained, lossless throughput. It’s cutting edge, using many machines to perform parallel operations, all at large transfer rates. Let’s say we’re the source of a dataset that is being transferred to a neocloud for processing, the processing layers (often GPUs) want a continuous stream of high bandwidth with no loss. And a single dropped packet in a training pipeline can trigger expensive re-requests, idle GPUs, and cascading slowdowns. 

With that in mind, we’ve evolved our infrastructure from traditional cloud networking—designed for smaller flows—to handle the relentless firehose of AI data.

Traditional cloud vs AI cloud

AI changes everything about traffic behavior. It doesn’t just mean that our total capacity is bigger, but also that our considerations for how we design, support, and scale our infrastructure morphed along with our capacity upgrades.

Here’s a quick overview of the former challenges and the new ones we’re engineering to serve our AI workflows.

Traditional Cloud Network AI Cloud Network
Small to large flow sizes (megabits to, gigabits) Very large flows (multi-gigabit to terabit)
High entropy flows (many sources and destinations) Low entropy flows (consistent source/destination pairs)
Predictable usage patterns Burst traffic patterns
Tolerant to failures Sensitive to faults, buffering, congestion

In short: AI traffic is heavier, stickier, and far less forgiving. So the goal is to design networks that can transfer 100Gbps, 200Gbps, and up to 1,000 Gbps (1 Terabit) a second with a low latency, low jitter, and a zero loss profile. Simple right? 

Hardware network upgrades

To meet these new demands of AI workflows, we’ve upgraded nearly every layer of our physical infrastructure. We needed to increase the density of our networking hardware, deploy denser fiber optic solutions, and upgrade the capacity of our edge network.

What technologies are we deploying?

1. Transitioning from NRZ to PAM4 Optics

The fiber optic modules that are used to connect all our infrastructure hardware (servers, switches, routers) have been transitioned to modules that support a denser encoding method. Both NRZ and PAM4 are technologies used to modulate signals. Think of NRZ as a one-lane highway with one passenger per car. PAM4 adds three more passengers per car, doubling the rate without doubling lanes and with controllable cons such as increased noise sensitivity. By using four voltage levels instead of two, PAM4 transmits twice the information per signal change, effectively doubling bandwidth per fiber strand.

2. MTP-8 and MTP-16 Fiber

MTP is a fiber connector type and the number after denotes the number of fiber optic strands contained within the cable. The higher the number, the more fiber pairs in the cable. We’ve used MTP-8 for years (four pairs of fiber), but to handle AI-scale traffic, we’re now deploying MTP-16 for higher-density connections. That means where we once ran 100G links, we now run 400G—and can scale up to multiple 100G paths as workloads grow (4x100G, 8x100G, etc).

3. Expanding edge and core capacity

We’ve refreshed routers and switches to handle higher port speeds and density—moving from 100G to 400G interfaces across our interconnects. The result: higher aggregate throughput and better fault isolation for massive parallel transfers.

Visualizing an AI workflow

Our monitoring tools track network flows (TCP conversations) in real time, giving us visibility into how large AI workflows move across the infrastructure. We use this type of information to monitor and make sure that large workflows are distributed across our physical infrastructure to allow for traffic balancing.

So, what does a large “AI workflow” look like? It’s not one device talking to one device at a high rate, but rather a collection of actors all working together.

On our side, our API layer speaks to our storage layer, requesting the files. Once the files are retrieved from our storage layer, they flow through our API servers and are then sent to a destination. In order to achieve a high throughput, many API servers talk to many destination servers. 

A typical 200+ Gbps transfer (diagrammed below) might involve four API virtual IPs (VIPs), each hosted on multiple backend servers sending 5–7 Gbps to ten destination nodes for a total output of 52Gbps from each API server. On the receiving side, each destination server might be ingesting 20Gbps across multiple streams.

The key insight: AI data transfer isn’t one big pipe—it’s a distributed mesh of many coordinated streams. Our design scales linearly—add more API servers, add more destination nodes, and the flow grows predictably without congestion or packet loss.

Conclusion 

AI workflows have redefined what “fast” means on the network. At Backblaze, we’ve evolved from a single-ISP startup to an AI-scale infrastructure provider by continuously pushing the boundaries of connectivity, throughput, and reliability.

As our customers push the frontiers of AI, we’ll keep tuning the invisible layer that makes it possible: the AI-ready network.

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