All posts by Bruce Schneier

Declassified NSA Newsletters

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/04/declassified-nsa-newsletters.html

Through a 2010 FOIA request (yes, it took that long), we have copies of the NSA’s KRYPTOS Society Newsletter, “Tales of the Krypt,” from 1994 to 2003.

There are many interesting things in the 800 pages of newsletter. There are many redactions. And a 1994 review of Applied Cryptography by redacted:

Applied Cryptography, for those who don’t read the internet news, is a book written by Bruce Schneier last year. According to the jacket, Schneier is a data security expert with a master’s degree in computer science. According to his followers, he is a hero who has finally brought together the loose threads of cryptography for the general public to understand. Schneier has gathered academic research, internet gossip, and everything he could find on cryptography into one 600-page jumble.

The book is destined for commercial success because it is the only volume in which everything linked to cryptography is mentioned. It has sections on such-diverse topics as number theory, zero knowledge proofs, complexity, protocols, DES, patent law, and the Computer Professionals for Social Responsibility. Cryptography is a hot topic just now, and Schneier stands alone in having written a book on it which can be browsed: it is not too dry.

Schneier gives prominence to applications with large sections.on protocols and source code. Code is given for IDEA, FEAL, triple-DES, and other algorithms. At first glance, the book has the look of an encyclopedia of cryptography. Unlike an encyclopedia, however, it can’t be trusted for accuracy.

Playing loose with the facts is a serious problem with Schneier. For example in discussing a small-exponent attack on RSA, he says “an attack by Michael Wiener will recover e when e is up to one quarter the size of n.” Actually, Wiener’s attack recovers the secret exponent d when e has less than one quarter as many bits as n, which is a quite different statement. Or: “The quadratic sieve is the fastest known algorithm for factoring numbers less than 150 digits…. The number field sieve is the fastest known factoring algorithm, although the quadratric sieve is still faster for smaller numbers (the break even point is between 110 and 135 digits).” Throughout the book, Schneier leaves the impression of sloppiness, of a quick and dirty exposition. The reader is subjected to the grunge of equations, only to be confused or misled. The large number of errors compounds the problem. A recent version of the errata (Schneier publishes updates on the internet) is fifteen pages and growing, including errors in diagrams, errors in the code, and errors in the bibliography.

Many readers won’t notice that the details are askew. The importance of the book is that it is the first stab at.putting the whole subject in one spot. Schneier aimed to provide a “comprehensive reference work for modern cryptography.” Comprehensive it is. A trusted reference it is not.

Ouch. But I will not argue that some of my math was sloppy, especially in the first edition (with the blue cover, not the red cover).

A few other highlights:

  • 1995 Kryptos Kristmas Kwiz, pages 299–306
  • 1996 Kryptos Kristmas Kwiz, pages 414–420
  • 1998 Kryptos Kristmas Kwiz, pages 659–665
  • 1999 Kryptos Kristmas Kwiz, pages 734–738
  • Dundee Society Introductory Placement Test (from questions posed by Lambros Callimahos in his famous class), pages 771–773
  • R. Dale Shipp’s Principles of Cryptanalytic Diagnosis, pages 776–779
  • Obit of Jacqueline Jenkins-Nye (Bill Nye the Science Guy’s mother), pages 755–756
  • A praise of Pi, pages 694–696
  • A rant about Acronyms, pages 614–615
  • A speech on women in cryptology, pages 593–599

Ross Anderson

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/03/ross-anderson.html

Ross Anderson unexpectedly passed away Thursday night in, I believe, his home in Cambridge.

I can’t remember when I first met Ross. Of course it was before 2008, when we created the Security and Human Behavior workshop. It was well before 2001, when we created the Workshop on Economics and Information Security. (Okay, he created both—I helped.) It was before 1998, when we wrote about the problems with key escrow systems. I was one of the people he brought to the Newton Institute, at Cambridge University, for the six-month cryptography residency program he ran (I mistakenly didn’t stay the whole time)—that was in 1996.

I know I was at the first Fast Software Encryption workshop in December 1993, another conference he created. There I presented the Blowfish encryption algorithm. Pulling an old first-edition of Applied Cryptography (the one with the blue cover) down from the shelf, I see his name in the acknowledgments. Which means that sometime in early 1993—probably at Eurocrypt in Lofthus, Norway—I, as an unpublished book author who had only written a couple of crypto articles for Dr. Dobb’s Journal, asked him to read and comment on my book manuscript. And he said yes. Which means I mailed him a paper copy. And he read it. And mailed his handwritten comments back to me. In an envelope with stamps. Because that’s how we did it back then.

I have known Ross for over thirty years, as both a colleague and a friend. He was enthusiastic, brilliant, opinionated, articulate, curmudgeonly, and kind. Pick up any of his academic papers—there are many—and odds are that you will find a least one unexpected insight. He was a cryptographer and security engineer, but also very much a generalist. He published on block cipher cryptanalysis in the 1990s, and the security of large-language models last year. He started conferences like nobody’s business. His masterwork book, Security Engineering—now in its third edition—is as comprehensive a tome on cybersecurity and related topics as you could imagine. (Also note his fifteen-lecture video series on that same page. If you have never heard Ross lecture, you’re in for a treat.) He was the first person to understand that security problems are often actually economic problems. He was the first person to make a lot of those sorts of connections. He fought against surveillance and backdoors, and for academic freedom. He didn’t suffer fools in either government or the corporate world.

He’s listed in the acknowledgments as a reader of every one of my books from Beyond Fear on. Recently, we’d see each other a couple of times a year: at this or that workshop or event. The last time I saw him was last June, at SHB 2023, in Pittsburgh. We were having dinner on Alessandro Acquisti‘s rooftop patio, celebrating another successful workshop. He was going to attend my Workshop on Reimagining Democracy in December, but he had to cancel at the last minute. (He sent me the talk he was going to give. I will see about posting it.) The day before he died, we were discussing how to accommodate everyone who registered for this year’s SHB workshop. I learned something from him every single time we talked. And I am not the only one.

My heart goes out to his wife Shireen and his family. We lost him much too soon.

EDITED TO ADD (4/10): I wrote a longer version for Communications of the ACM.

Friday Squid Blogging: The Geopolitics of Eating Squid

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/03/68676.html

New York Times op-ed on the Chinese dominance of the squid industry:

China’s domination in seafood has raised deep concerns among American fishermen, policymakers and human rights activists. They warn that China is expanding its maritime reach in ways that are putting domestic fishermen around the world at a competitive disadvantage, eroding international law governing sea borders and undermining food security, especially in poorer countries that rely heavily on fish for protein. In some parts of the world, frequent illegal incursions by Chinese ships into other nations’ waters are heightening military tensions. American lawmakers are concerned because the United States, locked in a trade war with China, is the world’s largest importer of seafood.

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

Read my blog posting guidelines here.

Hardware Vulnerability in Apple’s M-Series Chips

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/03/hardware-vulnerability-in-apples-m-series-chips.html

It’s yet another hardware side-channel attack:

The threat resides in the chips’ data memory-dependent prefetcher, a hardware optimization that predicts the memory addresses of data that running code is likely to access in the near future. By loading the contents into the CPU cache before it’s actually needed, the DMP, as the feature is abbreviated, reduces latency between the main memory and the CPU, a common bottleneck in modern computing. DMPs are a relatively new phenomenon found only in M-series chips and Intel’s 13th-generation Raptor Lake microarchitecture, although older forms of prefetchers have been common for years.

[…]

The breakthrough of the new research is that it exposes a previously overlooked behavior of DMPs in Apple silicon: Sometimes they confuse memory content, such as key material, with the pointer value that is used to load other data. As a result, the DMP often reads the data and attempts to treat it as an address to perform memory access. This “dereferencing” of “pointers”—meaning the reading of data and leaking it through a side channel—­is a flagrant violation of the constant-time paradigm.

[…]

The attack, which the researchers have named GoFetch, uses an application that doesn’t require root access, only the same user privileges needed by most third-party applications installed on a macOS system. M-series chips are divided into what are known as clusters. The M1, for example, has two clusters: one containing four efficiency cores and the other four performance cores. As long as the GoFetch app and the targeted cryptography app are running on the same performance cluster—­even when on separate cores within that cluster­—GoFetch can mine enough secrets to leak a secret key.

The attack works against both classical encryption algorithms and a newer generation of encryption that has been hardened to withstand anticipated attacks from quantum computers. The GoFetch app requires less than an hour to extract a 2048-bit RSA key and a little over two hours to extract a 2048-bit Diffie-Hellman key. The attack takes 54 minutes to extract the material required to assemble a Kyber-512 key and about 10 hours for a Dilithium-2 key, not counting offline time needed to process the raw data.

The GoFetch app connects to the targeted app and feeds it inputs that it signs or decrypts. As its doing this, it extracts the app secret key that it uses to perform these cryptographic operations. This mechanism means the targeted app need not perform any cryptographic operations on its own during the collection period.

Note that exploiting the vulnerability requires running a malicious app on the target computer. So it could be worse. On the other hand, like many of these hardware side-channel attacks, it’s not possible to patch.

Slashdot thread.

Security Vulnerability in Saflok’s RFID-Based Keycard Locks

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/03/security-vulnerability-in-safloks-rfid-based-keycard-locks.html

It’s pretty devastating:

Today, Ian Carroll, Lennert Wouters, and a team of other security researchers are revealing a hotel keycard hacking technique they call Unsaflok. The technique is a collection of security vulnerabilities that would allow a hacker to almost instantly open several models of Saflok-brand RFID-based keycard locks sold by the Swiss lock maker Dormakaba. The Saflok systems are installed on 3 million doors worldwide, inside 13,000 properties in 131 countries. By exploiting weaknesses in both Dormakaba’s encryption and the underlying RFID system Dormakaba uses, known as MIFARE Classic, Carroll and Wouters have demonstrated just how easily they can open a Saflok keycard lock. Their technique starts with obtaining any keycard from a target hotel—say, by booking a room there or grabbing a keycard out of a box of used ones—then reading a certain code from that card with a $300 RFID read-write device, and finally writing two keycards of their own. When they merely tap those two cards on a lock, the first rewrites a certain piece of the lock’s data, and the second opens it.

Dormakaba says that it’s been working since early last year to make hotels that use Saflok aware of their security flaws and to help them fix or replace the vulnerable locks. For many of the Saflok systems sold in the last eight years, there’s no hardware replacement necessary for each individual lock. Instead, hotels will only need to update or replace the front desk management system and have a technician carry out a relatively quick reprogramming of each lock, door by door. Wouters and Carroll say they were nonetheless told by Dormakaba that, as of this month, only 36 percent of installed Safloks have been updated. Given that the locks aren’t connected to the internet and some older locks will still need a hardware upgrade, they say the full fix will still likely take months longer to roll out, at the very least. Some older installations may take years.

If ever. My guess is that for many locks, this is a permanent vulnerability.

On Secure Voting Systems

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/03/on-secure-voting-systems.html

Andrew Appel shepherded a public comment—signed by twenty election cybersecurity experts, including myself—on best practices for ballot marking devices and vote tabulation. It was written for the Pennsylvania legislature, but it’s general in nature.

From the executive summary:

We believe that no system is perfect, with each having trade-offs. Hand-marked and hand-counted ballots remove the uncertainty introduced by use of electronic machinery and the ability of bad actors to exploit electronic vulnerabilities to remotely alter the results. However, some portion of voters mistakenly mark paper ballots in a manner that will not be counted in the way the voter intended, or which even voids the ballot. Hand-counts delay timely reporting of results, and introduce the possibility for human error, bias, or misinterpretation.

Technology introduces the means of efficient tabulation, but also introduces a manifold increase in complexity and sophistication of the process. This places the understanding of the process beyond the average person’s understanding, which can foster distrust. It also opens the door to human or machine error, as well as exploitation by sophisticated and malicious actors.

Rather than assert that each component of the process can be made perfectly secure on its own, we believe the goal of each component of the elections process is to validate every other component.

Consequently, we believe that the hallmarks of a reliable and optimal election process are hand-marked paper ballots, which are optically scanned, separately and securely stored, and rigorously audited after the election but before certification. We recommend state legislators adopt policies consistent with these guiding principles, which are further developed below.

Licensing AI Engineers

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/03/licensing-ai-engineers.html

The debate over professionalizing software engineers is decades old. (The basic idea is that, like lawyers and architects, there should be some professional licensing requirement for software engineers.) Here’s a law journal article recommending the same idea for AI engineers.

This Article proposes another way: professionalizing AI engineering. Require AI engineers to obtain licenses to build commercial AI products, push them to collaborate on scientifically-supported, domain-specific technical standards, and charge them with policing themselves. This Article’s proposal addresses AI harms at their inception, influencing the very engineering decisions that give rise to them in the first place. By wresting control over information and system design away from companies and handing it to AI engineers, professionalization engenders trustworthy AI by design. Beyond recommending the specific policy solution of professionalization, this Article seeks to shift the discourse on AI away from an emphasis on light-touch, ex post solutions that address already-created products to a greater focus on ex ante controls that precede AI development. We’ve used this playbook before in fields requiring a high level of expertise where a duty to the public welfare must trump business motivations. What if, like doctors, AI engineers also vowed to do no harm?

I have mixed feelings about the idea. I can see the appeal, but it never seemed feasible. I’m not sure it’s feasible today.

Google Pays $10M in Bug Bounties in 2023

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/03/google-pays-10m-in-bug-bounties-in-2023.html

BleepingComputer has the details. It’s $2M less than in 2022, but it’s still a lot.

The highest reward for a vulnerability report in 2023 was $113,337, while the total tally since the program’s launch in 2010 has reached $59 million.

For Android, the world’s most popular and widely used mobile operating system, the program awarded over $3.4 million.

Google also increased the maximum reward amount for critical vulnerabilities concerning Android to $15,000, driving increased community reports.

During security conferences like ESCAL8 and hardwea.io, Google awarded $70,000 for 20 critical discoveries in Wear OS and Android Automotive OS and another $116,000 for 50 reports concerning issues in Nest, Fitbit, and Wearables.

Google’s other big software project, the Chrome browser, was the subject of 359 security bug reports that paid out a total of $2.1 million.

Slashdot thread.

Public AI as an Alternative to Corporate AI

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/03/public-ai-as-an-alternative-to-corporate-ai.html

This mini-essay was my contribution to a round table on Power and Governance in the Age of AI.  It’s nothing I haven’t said here before, but for anyone who hasn’t read my longer essays on the topic, it’s a shorter introduction.

 

The increasingly centralized control of AI is an ominous sign. When tech billionaires and corporations steer AI, we get AI that tends to reflect the interests of tech billionaires and corporations, instead of the public. Given how transformative this technology will be for the world, this is a problem.

To benefit society as a whole we need an AI public option—not to replace corporate AI but to serve as a counterbalance—as well as stronger democratic institutions to govern all of AI. Like public roads and the federal postal system, a public AI option could guarantee universal access to this transformative technology and set an implicit standard that private services must surpass to compete.

Widely available public models and computing infrastructure would yield numerous benefits to the United States and to broader society. They would provide a mechanism for public input and oversight on the critical ethical questions facing AI development, such as whether and how to incorporate copyrighted works in model training, how to distribute access to private users when demand could outstrip cloud computing capacity, and how to license access for sensitive applications ranging from policing to medical use. This would serve as an open platform for innovation, on top of which researchers and small businesses—as well as mega-corporations—could build applications and experiment. Administered by a transparent and accountable agency, a public AI would offer greater guarantees about the availability, equitability, and sustainability of AI technology for all of society than would exclusively private AI development.

Federally funded foundation AI models would be provided as a public service, similar to a health care public option. They would not eliminate opportunities for private foundation models, but they could offer a baseline of price, quality, and ethical development practices that corporate players would have to match or exceed to compete.

The key piece of the ecosystem the government would dictate when creating an AI public option would be the design decisions involved in training and deploying AI foundation models. This is the area where transparency, political oversight, and public participation can, in principle, guarantee more democratically-aligned outcomes than an unregulated private market.

The need for such competent and faithful administration is not unique to AI, and it is not a problem we can look to AI to solve. Serious policymakers from both sides of the aisle should recognize the imperative for public-interested leaders to wrest control of the future of AI from unaccountable corporate titans. We do not need to reinvent our democracy for AI, but we do need to renovate and reinvigorate it to offer an effective alternative to corporate control that could erode our democracy.

Cheating Automatic Toll Booths by Obscuring License Plates

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/03/cheating-automatic-toll-booths-by-obscuring-license-plates.html

The Wall Street Journal is reporting on a variety of techniques drivers are using to obscure their license plates so that automatic readers can’t identify them and charge tolls properly.

Some drivers have power-washed paint off their plates or covered them with a range of household items such as leaf-shaped magnets, Bramwell-Stewart said. The Port Authority says officers in 2023 roughly doubled the number of summonses issued for obstructed, missing or fictitious license plates compared with the prior year.

Bramwell-Stewart said one driver from New Jersey repeatedly used what’s known in the streets as a flipper, which lets you remotely swap out a car’s real plate for a bogus one ahead of a toll area. In this instance, the bogus plate corresponded to an actual one registered to a woman who was mystified to receive the tolls. “Why do you keep billing me?” Bramwell-Stewart recalled her asking.

[…]

Cathy Sheridan, president of MTA Bridges and Tunnels in New York City, showed video of a flipper in action at a recent public meeting, after the car was stopped by police. One minute it had New York plates, the next it sported Texas tags. She also showed a clip of a second car with a device that lowered a cover over the plate like a curtain.

Boing Boing post.

AI and the Evolution of Social Media

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/03/ai-and-the-evolution-of-social-media.html

Oh, how the mighty have fallen. A decade ago, social media was celebrated for sparking democratic uprisings in the Arab world and beyond. Now front pages are splashed with stories of social platforms’ role in misinformation, business conspiracy, malfeasance, and risks to mental health. In a 2022 survey, Americans blamed social media for the coarsening of our political discourse, the spread of misinformation, and the increase in partisan polarization.

Today, tech’s darling is artificial intelligence. Like social media, it has the potential to change the world in many ways, some favorable to democracy. But at the same time, it has the potential to do incredible damage to society.

There is a lot we can learn about social media’s unregulated evolution over the past decade that directly applies to AI companies and technologies. These lessons can help us avoid making the same mistakes with AI that we did with social media.

In particular, five fundamental attributes of social media have harmed society. AI also has those attributes. Note that they are not intrinsically evil. They are all double-edged swords, with the potential to do either good or ill. The danger comes from who wields the sword, and in what direction it is swung. This has been true for social media, and it will similarly hold true for AI. In both cases, the solution lies in limits on the technology’s use.

#1: Advertising

The role advertising plays in the internet arose more by accident than anything else. When commercialization first came to the internet, there was no easy way for users to make micropayments to do things like viewing a web page. Moreover, users were accustomed to free access and wouldn’t accept subscription models for services. Advertising was the obvious business model, if never the best one. And it’s the model that social media also relies on, which leads it to prioritize engagement over anything else.

Both Google and Facebook believe that AI will help them keep their stranglehold on an 11-figure online ad market (yep, 11 figures), and the tech giants that are traditionally less dependent on advertising, like Microsoft and Amazon, believe that AI will help them seize a bigger piece of that market.

Big Tech needs something to persuade advertisers to keep spending on their platforms. Despite bombastic claims about the effectiveness of targeted marketing, researchers have long struggled to demonstrate where and when online ads really have an impact. When major brands like Uber and Procter & Gamble recently slashed their digital ad spending by the hundreds of millions, they proclaimed that it made no dent at all in their sales.

AI-powered ads, industry leaders say, will be much better. Google assures you that AI can tweak your ad copy in response to what users search for, and that its AI algorithms will configure your campaigns to maximize success. Amazon wants you to use its image generation AI to make your toaster product pages look cooler. And IBM is confident its Watson AI will make your ads better.

These techniques border on the manipulative, but the biggest risk to users comes from advertising within AI chatbots. Just as Google and Meta embed ads in your search results and feeds, AI companies will be pressured to embed ads in conversations. And because those conversations will be relational and human-like, they could be more damaging. While many of us have gotten pretty good at scrolling past the ads in Amazon and Google results pages, it will be much harder to determine whether an AI chatbot is mentioning a product because it’s a good answer to your question or because the AI developer got a kickback from the manufacturer.

#2: Surveillance

Social media’s reliance on advertising as the primary way to monetize websites led to personalization, which led to ever-increasing surveillance. To convince advertisers that social platforms can tweak ads to be maximally appealing to individual people, the platforms must demonstrate that they can collect as much information about those people as possible.

It’s hard to exaggerate how much spying is going on. A recent analysis by Consumer Reports about Facebook—just Facebook—showed that every user has more than 2,200 different companies spying on their web activities on its behalf.

AI-powered platforms that are supported by advertisers will face all the same perverse and powerful market incentives that social platforms do. It’s easy to imagine that a chatbot operator could charge a premium if it were able to claim that its chatbot could target users on the basis of their location, preference data, or past chat history and persuade them to buy products.

The possibility of manipulation is only going to get greater as we rely on AI for personal services. One of the promises of generative AI is the prospect of creating a personal digital assistant advanced enough to act as your advocate with others and as a butler to you. This requires more intimacy than you have with your search engine, email provider, cloud storage system, or phone. You’re going to want it with you constantly, and to most effectively work on your behalf, it will need to know everything about you. It will act as a friend, and you are likely to treat it as such, mistakenly trusting its discretion.

Even if you choose not to willingly acquaint an AI assistant with your lifestyle and preferences, AI technology may make it easier for companies to learn about you. Early demonstrations illustrate how chatbots can be used to surreptitiously extract personal data by asking you mundane questions. And with chatbots increasingly being integrated with everything from customer service systems to basic search interfaces on websites, exposure to this kind of inferential data harvesting may become unavoidable.

#3: Virality

Social media allows any user to express any idea with the potential for instantaneous global reach. A great public speaker standing on a soapbox can spread ideas to maybe a few hundred people on a good night. A kid with the right amount of snark on Facebook can reach a few hundred million people within a few minutes.

A decade ago, technologists hoped this sort of virality would bring people together and guarantee access to suppressed truths. But as a structural matter, it is in a social network’s interest to show you the things you are most likely to click on and share, and the things that will keep you on the platform.

As it happens, this often means outrageous, lurid, and triggering content. Researchers have found that content expressing maximal animosity toward political opponents gets the most engagement on Facebook and Twitter. And this incentive for outrage drives and rewards misinformation.

As Jonathan Swift once wrote, “Falsehood flies, and the Truth comes limping after it.” Academics seem to have proved this in the case of social media; people are more likely to share false information—perhaps because it seems more novel and surprising. And unfortunately, this kind of viral misinformation has been pervasive.

AI has the potential to supercharge the problem because it makes content production and propagation easier, faster, and more automatic. Generative AI tools can fabricate unending numbers of falsehoods about any individual or theme, some of which go viral. And those lies could be propelled by social accounts controlled by AI bots, which can share and launder the original misinformation at any scale.

Remarkably powerful AI text generators and autonomous agents are already starting to make their presence felt in social media. In July, researchers at Indiana University revealed a botnet of more than 1,100 Twitter accounts that appeared to be operated using ChatGPT.

AI will help reinforce viral content that emerges from social media. It will be able to create websites and web content, user reviews, and smartphone apps. It will be able to simulate thousands, or even millions, of fake personas to give the mistaken impression that an idea, or a political position, or use of a product, is more common than it really is. What we might perceive to be vibrant political debate could be bots talking to bots. And these capabilities won’t be available just to those with money and power; the AI tools necessary for all of this will be easily available to us all.

#4: Lock-in

Social media companies spend a lot of effort making it hard for you to leave their platforms. It’s not just that you’ll miss out on conversations with your friends. They make it hard for you to take your saved data—connections, posts, photos—and port it to another platform. Every moment you invest in sharing a memory, reaching out to an acquaintance, or curating your follows on a social platform adds a brick to the wall you’d have to climb over to go to another platform.

This concept of lock-in isn’t unique to social media. Microsoft cultivated proprietary document formats for years to keep you using its flagship Office product. Your music service or e-book reader makes it hard for you to take the content you purchased to a rival service or reader. And if you switch from an iPhone to an Android device, your friends might mock you for sending text messages in green bubbles. But social media takes this to a new level. No matter how bad it is, it’s very hard to leave Facebook if all your friends are there. Coordinating everyone to leave for a new platform is impossibly hard, so no one does.

Similarly, companies creating AI-powered personal digital assistants will make it hard for users to transfer that personalization to another AI. If AI personal assistants succeed in becoming massively useful time-savers, it will be because they know the ins and outs of your life as well as a good human assistant; would you want to give that up to make a fresh start on another company’s service? In extreme examples, some people have formed close, perhaps even familial, bonds with AI chatbots. If you think of your AI as a friend or therapist, that can be a powerful form of lock-in.

Lock-in is an important concern because it results in products and services that are less responsive to customer demand. The harder it is for you to switch to a competitor, the more poorly a company can treat you. Absent any way to force interoperability, AI companies have less incentive to innovate in features or compete on price, and fewer qualms about engaging in surveillance or other bad behaviors.

#5: Monopolization

Social platforms often start off as great products, truly useful and revelatory for their consumers, before they eventually start monetizing and exploiting those users for the benefit of their business customers. Then the platforms claw back the value for themselves, turning their products into truly miserable experiences for everyone. This is a cycle that Cory Doctorow has powerfully written about and traced through the history of Facebook, Twitter, and more recently TikTok.

The reason for these outcomes is structural. The network effects of tech platforms push a few firms to become dominant, and lock-in ensures their continued dominance. The incentives in the tech sector are so spectacularly, blindingly powerful that they have enabled six megacorporations (Amazon, Apple, Google, Facebook parent Meta, Microsoft, and Nvidia) to command a trillion dollars each of market value—or more. These firms use their wealth to block any meaningful legislation that would curtail their power. And they sometimes collude with each other to grow yet fatter.

This cycle is clearly starting to repeat itself in AI. Look no further than the industry poster child OpenAI, whose leading offering, ChatGPT, continues to set marks for uptake and usage. Within a year of the product’s launch, OpenAI’s valuation had skyrocketed to about $90 billion.

OpenAI once seemed like an “open” alternative to the megacorps—a common carrier for AI services with a socially oriented nonprofit mission. But the Sam Altman firing-and-rehiring debacle at the end of 2023, and Microsoft’s central role in restoring Altman to the CEO seat, simply illustrated how venture funding from the familiar ranks of the tech elite pervades and controls corporate AI. In January 2024, OpenAI took a big step toward monetization of this user base by introducing its GPT Store, wherein one OpenAI customer can charge another for the use of its custom versions of OpenAI software; OpenAI, of course, collects revenue from both parties. This sets in motion the very cycle Doctorow warns about.

In the middle of this spiral of exploitation, little or no regard is paid to externalities visited upon the greater public—people who aren’t even using the platforms. Even after society has wrestled with their ill effects for years, the monopolistic social networks have virtually no incentive to control their products’ environmental impact, tendency to spread misinformation, or pernicious effects on mental health. And the government has applied virtually no regulation toward those ends.

Likewise, few or no guardrails are in place to limit the potential negative impact of AI. Facial recognition software that amounts to racial profiling, simulated public opinions supercharged by chatbots, fake videos in political ads—all of it persists in a legal gray area. Even clear violators of campaign advertising law might, some think, be let off the hook if they simply do it with AI.

Mitigating the risks

The risks that AI poses to society are strikingly familiar, but there is one big difference: it’s not too late. This time, we know it’s all coming. Fresh off our experience with the harms wrought by social media, we have all the warning we should need to avoid the same mistakes.

The biggest mistake we made with social media was leaving it as an unregulated space. Even now—after all the studies and revelations of social media’s negative effects on kids and mental health, after Cambridge Analytica, after the exposure of Russian intervention in our politics, after everything else—social media in the US remains largely an unregulated “weapon of mass destruction.” Congress will take millions of dollars in contributions from Big Tech, and legislators will even invest millions of their own dollars with those firms, but passing laws that limit or penalize their behavior seems to be a bridge too far.

We can’t afford to do the same thing with AI, because the stakes are even higher. The harm social media can do stems from how it affects our communication. AI will affect us in the same ways and many more besides. If Big Tech’s trajectory is any signal, AI tools will increasingly be involved in how we learn and how we express our thoughts. But these tools will also influence how we schedule our daily activities, how we design products, how we write laws, and even how we diagnose diseases. The expansive role of these technologies in our daily lives gives for-profit corporations opportunities to exert control over more aspects of society, and that exposes us to the risks arising from their incentives and decisions.

The good news is that we have a whole category of tools to modulate the risk that corporate actions pose for our lives, starting with regulation. Regulations can come in the form of restrictions on activity, such as limitations on what kinds of businesses and products are allowed to incorporate AI tools. They can come in the form of transparency rules, requiring disclosure of what data sets are used to train AI models or what new preproduction-phase models are being trained. And they can come in the form of oversight and accountability requirements, allowing for civil penalties in cases where companies disregard the rules.

The single biggest point of leverage governments have when it comes to tech companies is antitrust law. Despite what many lobbyists want you to think, one of the primary roles of regulation is to preserve competition—not to make life harder for businesses. It is not inevitable for OpenAI to become another Meta, an 800-pound gorilla whose user base and reach are several times those of its competitors. In addition to strengthening and enforcing antitrust law, we can introduce regulation that supports competition-enabling standards specific to the technology sector, such as data portability and device interoperability. This is another core strategy for resisting monopoly and corporate control.

Additionally, governments can enforce existing regulations on advertising. Just as the US regulates what media can and cannot host advertisements for sensitive products like cigarettes, and just as many other jurisdictions exercise strict control over the time and manner of politically sensitive advertising, so too could the US limit the engagement between AI providers and advertisers.

Lastly, we should recognize that developing and providing AI tools does not have to be the sovereign domain of corporations. We, the people and our government, can do this too. The proliferation of open-source AI development in 2023, successful to an extent that startled corporate players, is proof of this. And we can go further, calling on our government to build public-option AI tools developed with political oversight and accountability under our democratic system, where the dictatorship of the profit motive does not apply.

Which of these solutions is most practical, most important, or most urgently needed is up for debate. We should have a vibrant societal dialogue about whether and how to use each of these tools. There are lots of paths to a good outcome.

The problem is that this isn’t happening now, particularly in the US. And with a looming presidential election, conflict spreading alarmingly across Asia and Europe, and a global climate crisis, it’s easy to imagine that we won’t get our arms around AI any faster than we have (not) with social media. But it’s not too late. These are still the early years for practical consumer AI applications. We must and can do better.

This essay was written with Nathan Sanders, and was originally published in MIT Technology Review.

Drones and the US Air Force

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/03/drones-and-the-us-air-force.html

Fascinating analysis of the use of drones on a modern battlefield—that is, Ukraine—and the inability of the US Air Force to react to this change.

The F-35A certainly remains an important platform for high-intensity conventional warfare. But the Air Force is planning to buy 1,763 of the aircraft, which will remain in service through the year 2070. These jets, which are wholly unsuited for countering proliferated low-cost enemy drones in the air littoral, present enormous opportunity costs for the service as a whole. In a set of comments posted on LinkedIn last month, defense analyst T.X. Hammes estimated the following. The delivered cost of a single F-35A is around $130 million, but buying and operating that plane throughout its lifecycle will cost at least $460 million. He estimated that a single Chinese Sunflower suicide drone costs about $30,000—so you could purchase 16,000 Sunflowers for the cost of one F-35A. And since the full mission capable rate of the F-35A has hovered around 50 percent in recent years, you need two to ensure that all missions can be completed—for an opportunity cost of 32,000 Sunflowers. As Hammes concluded, “Which do you think creates more problems for air defense?”

Ironically, the first service to respond decisively to the new contestation of the air littoral has been the U.S. Army. Its soldiers are directly threatened by lethal drones, as the Tower 22 attack demonstrated all too clearly. Quite unexpectedly, last month the Army cancelled its future reconnaissance helicopter ­ which has already cost the service $2 billion—because fielding a costly manned reconnaissance aircraft no longer makes sense. Today, the same mission can be performed by far less expensive drones—without putting any pilots at risk. The Army also decided to retire its aging Shadow and Raven legacy drones, whose declining survivability and capabilities have rendered them obsolete, and announced a new rapid buy of 600 Coyote counter-drone drones in order to help protect its troops.

Improving C++

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/03/improving-c.html

C++ guru Herb Sutter writes about how we can improve the programming language for better security.

The immediate problem “is” that it’s Too Easy By Default™ to write security and safety vulnerabilities in C++ that would have been caught by stricter enforcement of known rules for type, bounds, initialization, and lifetime language safety.

His conclusion:

We need to improve software security and software safety across the industry, especially by improving programming language safety in C and C++, and in C++ a 98% improvement in the four most common problem areas is achievable in the medium term. But if we focus on programming language safety alone, we may find ourselves fighting yesterday’s war and missing larger past and future security dangers that affect software written in any language.

Automakers Are Sharing Driver Data with Insurers without Consent

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/03/automakers-are-sharing-driver-data-with-insurers-without-consent.html

Kasmir Hill has the story:

Modern cars are internet-enabled, allowing access to services like navigation, roadside assistance and car apps that drivers can connect to their vehicles to locate them or unlock them remotely. In recent years, automakers, including G.M., Honda, Kia and Hyundai, have started offering optional features in their connected-car apps that rate people’s driving. Some drivers may not realize that, if they turn on these features, the car companies then give information about how they drive to data brokers like LexisNexis [who then sell it to insurance companies].

Automakers and data brokers that have partnered to collect detailed driving data from millions of Americans say they have drivers’ permission to do so. But the existence of these partnerships is nearly invisible to drivers, whose consent is obtained in fine print and murky privacy policies that few read.