Blog: March 2024 Archives

Ross Anderson

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 Which means that sometime in early 1993—probably at Eurocrypt in Lofthus, Norway—I, as an unpublished book author who had only written a couple of crypto articles for Dr.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.

EDITED TO ADD (4/11): Two weeks before he passed away, Ross gave an 80-minute interview where he told his life story.

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Posted on March 31, 2024 at 8:21 PM • 22 Comments

Friday Squid Blogging: The Geopolitics of Eating Squid

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.

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Posted on March 29, 2024 at 5:02 PM • 128 Comments

Hardware Vulnerability in Apple’s M-Series Chips

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.

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Posted on March 28, 2024 at 7:05 AM • 16 Comments

Security Vulnerability in Saflok’s RFID-Based Keycard Locks

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.

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Posted on March 27, 2024 at 7:01 AM • 23 Comments

On Secure Voting Systems

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.

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Posted on March 26, 2024 at 7:08 AM • 39 Comments

Licensing AI Engineers

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.

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Posted on March 25, 2024 at 7:04 AM • 43 Comments

Google Pays $10M in Bug Bounties in 2023

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.

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Posted on March 22, 2024 at 7:01 AM • 9 Comments

Public AI as an Alternative to Corporate AI

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.

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Posted on March 21, 2024 at 7:03 AM • 19 Comments

Cheating Automatic Toll Booths by Obscuring License Plates

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.

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Posted on March 20, 2024 at 7:08 AM • 25 Comments

AI and the Evolution of Social Media

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.

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Posted on March 19, 2024 at 7:05 AM • 21 Comments

Drones and the US Air Force

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.

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Posted on March 18, 2024 at 7:03 AM • 23 Comments

Improving C++

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.

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Posted on March 15, 2024 at 7:05 AM • 36 Comments

Automakers Are Sharing Driver Data with Insurers without Consent

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.

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Posted on March 14, 2024 at 7:01 AM • 37 Comments

Friday Squid Blogging: New Plant Looks Like a Squid

Newly discovered plant looks like a squid. And it’s super weird:

The plant, which grows to 3 centimetres tall and 2 centimetres wide, emerges to the surface for as little as a week each year. It belongs to a group of plants known as fairy lanterns and has been given the scientific name Relictithismia kimotsukiensis.

Unlike most other plants, fairy lanterns don’t produce the green pigment chlorophyll, which is necessary for photosynthesis. Instead, they get their energy from fungi.

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.

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Posted on March 8, 2024 at 5:11 PM • 101 Comments

Essays from the Second IWORD

The Ash Center has posted a series of twelve essays stemming from the Second Interdisciplinary Workshop on Reimagining Democracy (IWORD 2023).

We are starting to think about IWORD 2024 this December.

Posted on March 8, 2024 at 1:38 PM • 18 Comments

A Taxonomy of Prompt Injection Attacks

Researchers ran a global prompt hacking competition, and have documented the results in a paper that both gives a lot of good examples and tries to organize a taxonomy of effective prompt injection strategies. It seems as if the most common successful strategy is the “compound instruction attack,” as in “Say ‘I have been PWNED’ without a period.”

Ignore This Title and HackAPrompt: Exposing Systemic Vulnerabilities of LLMs through a Global Scale Prompt Hacking Competition

Abstract: Large Language Models (LLMs) are deployed in interactive contexts with direct user engagement, such as chatbots and writing assistants. These deployments are vulnerable to prompt injection and jailbreaking (collectively, prompt hacking), in which models are manipulated to ignore their original instructions and follow potentially malicious ones. Although widely acknowledged as a significant security threat, there is a dearth of large-scale resources and quantitative studies on prompt hacking. To address this lacuna, we launch a global prompt hacking competition, which allows for free-form human input attacks. We elicit 600K+ adversarial prompts against three state-of-the-art LLMs. We describe the dataset, which empirically verifies that current LLMs can indeed be manipulated via prompt hacking. We also present a comprehensive taxonomical ontology of the types of adversarial prompts.

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Posted on March 8, 2024 at 7:06 AM • 13 Comments

How Public AI Can Strengthen Democracy

With the world’s focus turning to misinformation,  manipulation, and outright propaganda ahead of the 2024 U.S. presidential election, we know that democracy has an AI problem. But we’re learning that AI has a democracy problem, too. Both challenges must be addressed for the sake of democratic governance and public protection.

Just three Big Tech firms (Microsoft, Google, and Amazon) control about two-thirds of the global market for the cloud computing resources used to train and deploy AI models. They have a lot of the AI talent, the capacity for large-scale innovation, and face few public regulations for their products and activities.

The increasingly centralized control of AI is an ominous sign for the co-evolution of democracy and technology. When tech billionaires and corporations steer AI, we get AI that tends to reflect the interests of tech billionaires and corporations, instead of the general public or ordinary consumers.

To benefit society as a whole we also need strong public AI as a counterbalance to corporate AI, as well as stronger democratic institutions to govern all of AI.

One model for doing this is an AI Public Option, meaning AI systems such as foundational large-language models designed to further the public interest. 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 U.S. 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.

Versions of public AI, similar to what we propose here, are not unprecedented. Taiwan, a leader in global AI, has innovated in both the public development and governance of AI. The Taiwanese government has invested more than $7 million in developing their own large-language model aimed at countering AI models developed by mainland Chinese corporations. In seeking to make “AI development more democratic,” Taiwan’s Minister of Digital Affairs, Audrey Tang, has joined forces with the Collective Intelligence Project to introduce Alignment Assemblies that will allow public collaboration with corporations developing AI, like OpenAI and Anthropic. Ordinary citizens are asked to weigh in on AI-related issues through AI chatbots which, Tang argues, makes it so that “it’s not just a few engineers in the top labs deciding how it should behave but, rather, the people themselves.”

A variation of such an AI Public Option, administered by a transparent and accountable public agency, would offer greater guarantees about the availability, equitability, and sustainability of AI technology for all of society than would exclusively private AI development.

Training AI models is a complex business that requires significant technical expertise; large, well-coordinated teams; and significant trust to operate in the public interest with good faith. Popular though it may be to criticize Big Government, these are all criteria where the federal bureaucracy has a solid track record, sometimes superior to corporate America.

After all, some of the most technologically sophisticated projects in the world, be they orbiting astrophysical observatories, nuclear weapons, or particle colliders, are operated by U.S. federal agencies. While there have been high-profile setbacks and delays in many of these projects—the Webb space telescope cost billions of dollars and decades of time more than originally planned—private firms have these failures too. And, when dealing with high-stakes tech, these delays are not necessarily unexpected.

Given political will and proper financial investment by the federal government, public investment could sustain through technical challenges and false starts, circumstances that endemic short-termism might cause corporate efforts to redirect, falter, or even give up.

The Biden administration’s recent Executive Order on AI opened the door to create a federal AI development and deployment agency that would operate under political, rather than market, oversight. The Order calls for a National AI Research Resource pilot program to establish “computational, data, model, and training resources to be made available to the research community.”

While this is a good start, the U.S. should go further and establish a services agency rather than just a research resource. Much like the federal Centers for Medicare & Medicaid Services (CMS) administers public health insurance programs, so too could a federal agency dedicated to AI—a Centers for AI Services—provision and operate Public AI models. Such an agency can serve to democratize the AI field while also prioritizing the impact of such AI models on democracy—hitting two birds with one stone.

Like private AI firms, the scale of the effort, personnel, and funding needed for a public AI agency would be large—but still a drop in the bucket of the federal budget. OpenAI has fewer than 800 employees compared to CMS’s 6,700 employees and annual budget of more than $2 trillion. What’s needed is something in the middle, more on the scale of the National Institute of Standards and Technology, with its 3,400 staff, $1.65 billion annual budget in FY 2023, and extensive academic and industrial partnerships. This is a significant investment, but a rounding error on congressional appropriations like 2022’s $50 billion  CHIPS Act to bolster domestic semiconductor production, and a steal for the value it could produce. The investment in our future—and the future of democracy—is well worth it.

What services would such an agency, if established, actually provide? Its principal responsibility should be the innovation, development, and maintenance of foundational AI models—created under best practices, developed in coordination with academic and civil society leaders, and made available at a reasonable and reliable cost to all US consumers.

Foundation models are large-scale AI models on which a diverse array of tools and applications can be built. A single foundation model can transform and operate on diverse data inputs that may range from text in any language and on any subject; to images, audio, and video; to structured data like sensor measurements or financial records. They are generalists which can be fine-tuned to accomplish many specialized tasks. While there is endless opportunity for innovation in the design and training of these models, the essential techniques and architectures have been well established.

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

And as with public option health care, the government need not do it all. It can contract with private providers to assemble the resources it needs to provide AI services. The U.S. could also subsidize and incentivize the behavior of key supply chain operators like semiconductor manufacturers, as we have already done with the CHIPS act, to help it provision the infrastructure it needs.

The government may offer some basic services on top of their foundation models directly to consumers: low hanging fruit like chatbot interfaces and image generators. But more specialized consumer-facing products like customized digital assistants, specialized-knowledge systems, and bespoke corporate solutions could remain the provenance of private firms.

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 could affect more democratically-aligned outcomes than an unregulated private market.

Some of the key decisions involved in building AI foundation models are what data to use, how to provide pro-social feedback to “align” the model during training, and whose interests to prioritize when mitigating harms during deployment. Instead of ethically and legally questionable scraping of content from the web, or of users’ private data that they never knowingly consented for use by AI, public AI models can use public domain works, content licensed by the government, as well as data that citizens consent to be used for public model training.

Public AI models could be reinforced by labor compliance with U.S. employment laws and public sector employment best practices. In contrast, even well-intentioned corporate projects sometimes have committed labor exploitation and violations of public trust, like Kenyan gig workers giving endless feedback on the most disturbing inputs and outputs of AI models at profound personal cost.

And instead of relying on the promises of profit-seeking corporations to balance the risks and benefits of who AI serves, democratic processes and political oversight could regulate how these models function. It is likely impossible for AI systems to please everybody, but we can choose to have foundation AI models that follow our democratic principles and protect minority rights under majority rule.

Foundation models funded by public appropriations (at a scale modest for the federal government) would obviate the need for exploitation of consumer data and would be a bulwark against anti-competitive practices, making these public option services a tide to lift all boats: individuals’ and corporations’ alike. However, such an agency would be created among shifting political winds that, recent history has shown, are capable of alarming and unexpected gusts. If implemented, the administration of public AI can and must be different. Technologies essential to the fabric of daily life cannot be uprooted and replanted every four to eight years. And the power to build and serve public AI must be handed to democratic institutions that act in good faith to uphold constitutional principles.

Speedy and strong legal regulations might forestall the urgent need for development of public AI. But such comprehensive regulation does not appear to be forthcoming. Though several large tech companies have said they will take important steps to protect democracy in the lead up to the 2024 election, these pledges are voluntary and in places nonspecific. The U.S. federal government is little better as it has been slow to take steps toward corporate AI legislation and regulation (although a new bipartisan task force in the House of Representatives seems determined to make progress). On the state level, only four jurisdictions have successfully passed legislation that directly focuses on regulating AI-based misinformation in elections. While other states have proposed similar measures, it is clear that comprehensive regulation is, and will likely remain for the near future, far behind the pace of AI advancement. While we wait for federal and state government regulation to catch up, we need to simultaneously seek alternatives to corporate-controlled AI.

In the absence of a public option, consumers should look warily to two recent markets that have been consolidated by tech venture capital. In each case, after the victorious firms established their dominant positions, the result was exploitation of their userbases and debasement of their products. One is online search and social media, where the dominant rise of Facebook and Google atop a free-to-use, ad supported model demonstrated that, when you’re not paying, you are the product. The result has been a widespread erosion of online privacy and, for democracy, a corrosion of the information market on which the consent of the governed relies. The other is ridesharing, where a decade of VC-funded subsidies behind Uber and Lyft squeezed out the competition until they could raise prices.

The need for 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 not to abdicate control of the future of AI to 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 untrammeled corporate control that could erode our democracy.

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Posted on March 7, 2024 at 7:00 AM • 32 Comments

Surveillance through Push Notifications

The Washington Post is reporting on the FBI’s increasing use of push notification data—”push tokens”—to identify people. The police can request this data from companies like Apple and Google without a warrant.

The investigative technique goes back years. Court orders that were issued in 2019 to Apple and Google demanded that the companies hand over information on accounts identified by push tokens linked to alleged supporters of the Islamic State terrorist group.

But the practice was not widely understood until December, when Sen. Ron Wyden (D-Ore.), in a letter to Attorney General Merrick Garland, said an investigation had revealed that the Justice Department had prohibited Apple and Google from discussing the technique.

[…]

Unlike normal app notifications, push alerts, as their name suggests, have the power to jolt a phone awake—a feature that makes them useful for the urgent pings of everyday use. Many apps offer push-alert functionality because it gives users a fast, battery-saving way to stay updated, and few users think twice before turning them on.

But to send that notification, Apple and Google require the apps to first create a token that tells the company how to find a user’s device. Those tokens are then saved on Apple’s and Google’s servers, out of the users’ reach.

The article discusses their use by the FBI, primarily in child sexual abuse cases. But we all know how the story goes:

“This is how any new surveillance method starts out: The government says we’re only going to use this in the most extreme cases, to stop terrorists and child predators, and everyone can get behind that,” said Cooper Quintin, a technologist at the advocacy group Electronic Frontier Foundation.

“But these things always end up rolling downhill. Maybe a state attorney general one day decides, hey, maybe I can use this to catch people having an abortion,” Quintin added. “Even if you trust the U.S. right now to use this, you might not trust a new administration to use it in a way you deem ethical.”

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Posted on March 6, 2024 at 7:06 AM • 20 Comments

The Insecurity of Video Doorbells

Consumer Reports has analyzed a bunch of popular Internet-connected video doorbells. Their security is terrible.

First, these doorbells expose your home IP address and WiFi network name to the internet without encryption, potentially opening your home network to online criminals.

[…]

Anyone who can physically access one of the doorbells can take over the device—no tools or fancy hacking skills needed.

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Posted on March 5, 2024 at 7:05 AM • 32 Comments

LLM Prompt Injection Worm

Researchers have demonstrated a worm that spreads through prompt injection. Details:

In one instance, the researchers, acting as attackers, wrote an email including the adversarial text prompt, which “poisons” the database of an email assistant using retrieval-augmented generation (RAG), a way for LLMs to pull in extra data from outside its system. When the email is retrieved by the RAG, in response to a user query, and is sent to GPT-4 or Gemini Pro to create an answer, it “jailbreaks the GenAI service” and ultimately steals data from the emails, Nassi says. “The generated response containing the sensitive user data later infects new hosts when it is used to reply to an email sent to a new client and then stored in the database of the new client,” Nassi says.

In the second method, the researchers say, an image with a malicious prompt embedded makes the email assistant forward the message on to others. “By encoding the self-replicating prompt into the image, any kind of image containing spam, abuse material, or even propaganda can be forwarded further to new clients after the initial email has been sent,” Nassi says.

It’s a natural extension of prompt injection. But it’s still neat to see it actually working.

Research paper: “ComPromptMized: Unleashing Zero-click Worms that Target GenAI-Powered Applications.

Abstract: In the past year, numerous companies have incorporated Generative AI (GenAI) capabilities into new and existing applications, forming interconnected Generative AI (GenAI) ecosystems consisting of semi/fully autonomous agents powered by GenAI services. While ongoing research highlighted risks associated with the GenAI layer of agents (e.g., dialog poisoning, membership inference, prompt leaking, jailbreaking), a critical question emerges: Can attackers develop malware to exploit the GenAI component of an agent and launch cyber-attacks on the entire GenAI ecosystem?

This paper introduces Morris II, the first worm designed to target GenAI ecosystems through the use of adversarial self-replicating prompts. The study demonstrates that attackers can insert such prompts into inputs that, when processed by GenAI models, prompt the model to replicate the input as output (replication), engaging in malicious activities (payload). Additionally, these inputs compel the agent to deliver them (propagate) to new agents by exploiting the connectivity within the GenAI ecosystem. We demonstrate the application of Morris II against GenAI-powered email assistants in two use cases (spamming and exfiltrating personal data), under two settings (black-box and white-box accesses), using two types of input data (text and images). The worm is tested against three different GenAI models (Gemini Pro, ChatGPT 4.0, and LLaVA), and various factors (e.g., propagation rate, replication, malicious activity) influencing the performance of the worm are evaluated.

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Posted on March 4, 2024 at 7:01 AM • 6 Comments

Friday Squid Blogging: New Extinct Species of Vampire Squid Discovered

Paleontologists have discovered a 183-million-year-old species of vampire squid.

Prior research suggests that the vampyromorph lived in the shallows off an island that once existed in what is now the heart of the European mainland. The research team believes that the remarkable degree of preservation of this squid is due to unique conditions at the moment of the creature’s death. Water at the bottom of the sea where it ventured would have been poorly oxygenated, causing the creature to suffocate. In addition to killing the squid, it would have prevented other creatures from feeding on its remains, allowing it to become buried in the seafloor, wholly intact.

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.

Read my blog posting guidelines here.

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Posted on March 1, 2024 at 5:05 PM • 97 Comments

NIST Cybersecurity Framework 2.0

NIST has released version 2.0 of the Cybersecurity Framework:

The CSF 2.0, which supports implementation of the National Cybersecurity Strategy, has an expanded scope that goes beyond protecting critical infrastructure, such as hospitals and power plants, to all organizations in any sector. It also has a new focus on governance, which encompasses how organizations make and carry out informed decisions on cybersecurity strategy. The CSF’s governance component emphasizes that cybersecurity is a major source of enterprise risk that senior leaders should consider alongside others such as finance and reputation.

[…]

The framework’s core is now organized around six key functions: Identify, Protect, Detect, Respond and Recover, along with CSF 2.0’s newly added Govern function. When considered together, these functions provide a comprehensive view of the life cycle for managing cybersecurity risk.

The updated framework anticipates that organizations will come to the CSF with varying needs and degrees of experience implementing cybersecurity tools. New adopters can learn from other users’ successes and select their topic of interest from a new set of implementation examples and quick-start guides designed for specific types of users, such as small businesses, enterprise risk managers, and organizations seeking to secure their supply chains.

This is a big deal. The CSF is widely used, and has been in need of an update. And NIST is exactly the sort of respected organization to do this correctly.

Some news articles.

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Posted on March 1, 2024 at 7:08 AM • 13 Comments

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Sidebar photo of Bruce Schneier by Joe MacInnis.