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      Security and Human Behavior (SHB) 2024

      news.movim.eu / Schneier · 5 days ago - 20:55 · 1 minute

    This week, I hosted the seventeenth Workshop on Security and Human Behavior at the Harvard Kennedy School. This is the first workshop since our co-founder, Ross Anderson, died unexpectedly .

    SHB is a small, annual, invitational workshop of people studying various aspects of the human side of security. The fifty or so attendees include psychologists, economists, computer security researchers, criminologists, sociologists, political scientists, designers, lawyers, philosophers, anthropologists, geographers, neuroscientists, business school professors, and a smattering of others. It’s not just an interdisciplinary event; most of the people here are individually interdisciplinary.

    Our goal is always to maximize discussion and interaction. We do that by putting everyone on panels, and limiting talks to six to eight minutes, with the rest of the time for open discussion. Short talks limit presenters’ ability to get into the boring details of their work, and the interdisciplinary audience discourages jargon.

    Since the beginning, this workshop has been the most intellectually stimulating two days of my professional year. It influences my thinking in different and sometimes surprising ways—and has resulted in some new friendships and unexpected collaborations. This is why some of us have been coming back every year for over a decade.

    This year’s schedule is here . This page lists the participants and includes links to some of their work. Kami Vaniea liveblogged both days .

    Here are my posts on the first , second , third , fourth , fifth , sixth , seventh , eighth , ninth , tenth , eleventh , twelfth , thirteenth , fourteenth , fifteenth and sixteenth SHB workshops. Follow those links to find summaries, papers, and occasionally audio/video recordings of the sessions. Ross maintained a good webpage of psychology and security resources—it’s still up for now.

    Next year we will be in Cambridge, UK, hosted by Frank Stajano .

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      The Justice Department Took Down the 911 S5 Botnet

      news.movim.eu / Schneier · 6 days ago - 16:57 · 1 minute

    The US Justice Department has dismantled an enormous botnet:

    According to an indictment unsealed on May 24, from 2014 through July 2022, Wang and others are alleged to have created and disseminated malware to compromise and amass a network of millions of residential Windows computers worldwide. These devices were associated with more than 19 million unique IP addresses, including 613,841 IP addresses located in the United States. Wang then generated millions of dollars by offering cybercriminals access to these infected IP addresses for a fee.

    […]

    This operation was a coordinated multiagency effort led by law enforcement in the United States, Singapore, Thailand, and Germany. Agents and officers searched residences, seized assets valued at approximately $30 million, and identified additional forfeitable property valued at approximately $30 million. The operation also seized 23 domains and over 70 servers constituting the backbone of Wang’s prior residential proxy service and the recent incarnation of the service. By seizing multiple domains tied to the historical 911 S5, as well as several new domains and services directly linked to an effort to reconstitute the service, the government has successfully terminated Wang’s efforts to further victimize individuals through his newly formed service Clourouter.io and closed the existing malicious backdoors.

    The creator and operator of the botnet, YunHe Wang, was arrested in Singapore.

    Three news articles .

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      Online Privacy and Overfishing

      news.movim.eu / Schneier · 7 days ago - 20:51 · 4 minutes

    Microsoft recently caught state-backed hackers using its generative AI tools to help with their attacks. In the security community, the immediate questions weren’t about how hackers were using the tools (that was utterly predictable), but about how Microsoft figured it out. The natural conclusion was that Microsoft was spying on its AI users, looking for harmful hackers at work.

    Some pushed back at characterizing Microsoft’s actions as “spying.” Of course cloud service providers monitor what users are doing. And because we expect Microsoft to be doing something like this, it’s not fair to call it spying.

    We see this argument as an example of our shifting collective expectations of privacy. To understand what’s happening, we can learn from an unlikely source: fish.

    In the mid-20th century, scientists began noticing that the number of fish in the ocean—so vast as to underlie the phrase “There are plenty of fish in the sea”—had started declining rapidly due to overfishing. They had already seen a similar decline in whale populations, when the post-WWII whaling industry nearly drove many species extinct. In whaling and later in commercial fishing, new technology made it easier to find and catch marine creatures in ever greater numbers. Ecologists, specifically those working in fisheries management, began studying how and when certain fish populations had gone into serious decline.

    One scientist, Daniel Pauly , realized that researchers studying fish populations were making a major error when trying to determine acceptable catch size. It wasn’t that scientists didn’t recognize the declining fish populations. It was just that they didn’t realize how significant the decline was. Pauly noted that each generation of scientists had a different baseline to which they compared the current statistics, and that each generation’s baseline was lower than that of the previous one.

    What seems normal to us in the security community is whatever was commonplace at the beginning of our careers .

    Pauly called this “ shifting baseline syndrome ” in a 1995 paper. The baseline most scientists used was the one that was normal when they began their research careers. By that measure, each subsequent decline wasn’t significant, but the cumulative decline was devastating. Each generation of researchers came of age in a new ecological and technological environment, inadvertently masking an exponential decline.

    Pauly’s insights came too late to help those managing some fisheries. The ocean suffered catastrophes such as the complete collapse of the Northwest Atlantic cod population in the 1990s.

    Internet surveillance, and the resultant loss of privacy, is following the same trajectory. Just as certain fish populations in the world’s oceans have fallen 80 percent, from previously having fallen 80 percent, from previously having fallen 80 percent (ad infinitum), our expectations of privacy have similarly fallen precipitously. The pervasive nature of modern technology makes surveillance easier than ever before, while each successive generation of the public is accustomed to the privacy status quo of their youth. What seems normal to us in the security community is whatever was commonplace at the beginning of our careers.

    Historically, people controlled their computers, and software was standalone. The always-connected cloud-deployment model of software and services flipped the script. Most apps and services are designed to be always-online, feeding usage information back to the company. A consequence of this modern deployment model is that everyone—cynical tech folks and even ordinary users—expects that what you do with modern tech isn’t private. But that’s because the baseline has shifted.

    AI chatbots are the latest incarnation of this phenomenon: They produce output in response to your input, but behind the scenes there’s a complex cloud-based system keeping track of that input—both to improve the service and to sell you ads .

    Shifting baselines are at the heart of our collective loss of privacy. The U.S. Supreme Court has long held that our right to privacy depends on whether we have a reasonable expectation of privacy . But expectation is a slippery thing: It’s subject to shifting baselines.

    The question remains: What now? Fisheries scientists, armed with knowledge of shifting-baseline syndrome, now look at the big picture. They no longer consider relative measures, such as comparing this decade with the last decade. Instead, they take a holistic, ecosystem-wide perspective to see what a healthy marine ecosystem and thus sustainable catch should look like. They then turn these scientifically derived sustainable-catch figures into limits to be codified by regulators.

    In privacy and security, we need to do the same. Instead of comparing to a shifting baseline, we need to step back and look at what a healthy technological ecosystem would look like: one that respects people’s privacy rights while also allowing companies to recoup costs for services they provide. Ultimately, as with fisheries, we need to take a big-picture perspective and be aware of shifting baselines. A scientifically informed and democratic regulatory process is required to preserve a heritage—whether it be the ocean or the Internet—for the next generation.

    This essay was written with Barath Raghavan, and previously appeared in IEEE Spectrum .

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      Breaking a Password Manager

      news.movim.eu / Schneier · 7 days ago - 05:54 · 1 minute

    Interesting story of breaking the security of the RoboForm password manager in order to recover a cryptocurrency wallet password.

    Grand and Bruno spent months reverse engineering the version of the RoboForm program that they thought Michael had used in 2013 and found that the pseudo-random number generator used to generate passwords in that version—­and subsequent versions until 2015­—did indeed have a significant flaw that made the random number generator not so random. The RoboForm program unwisely tied the random passwords it generated to the date and time on the user’s computer­—it determined the computer’s date and time, and then generated passwords that were predictable. If you knew the date and time and other parameters, you could compute any password that would have been generated on a certain date and time in the past.

    If Michael knew the day or general time frame in 2013 when he generated it, as well as the parameters he used to generate the password (for example, the number of characters in the password, including lower- and upper-case letters, figures, and special characters), this would narrow the possible password guesses to a manageable number. Then they could hijack the RoboForm function responsible for checking the date and time on a computer and get it to travel back in time, believing the current date was a day in the 2013 time frame when Michael generated his password. RoboForm would then spit out the same passwords it generated on the days in 2013.

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      AI Will Increase the Quantity—and Quality—of Phishing Scams

      news.movim.eu / Schneier · Saturday, 1 June - 11:22

    A piece I coauthored with Fredrik Heiding and Arun Vishwanath in the Harvard Business Review :

    Summary. Gen AI tools are rapidly making these emails more advanced, harder to spot, and significantly more dangerous. Recent research showed that 60% of participants fell victim to artificial intelligence (AI)-automated phishing, which is comparable to the success rates of non-AI-phishing messages created by human experts. Companies need to: 1) understand the asymmetrical capabilities of AI-enhanced phishing, 2) determine the company or division’s phishing threat severity level, and 3) confirm their current phishing awareness routines.

    Here’s the full text .

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      Friday Squid Blogging: Baby Colossal Squid

      news.movim.eu / Schneier · Friday, 31 May - 16:04

    This video might be a juvenile colossal squid.

    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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      How AI Will Change Democracy

      news.movim.eu / Schneier · Thursday, 30 May - 12:48 · 21 minutes

    I don’t think it’s an exaggeration to predict that artificial intelligence will affect every aspect of our society. Not by doing new things. But mostly by doing things that are already being done by humans, perfectly competently.

    Replacing humans with AIs isn’t necessarily interesting. But when an AI takes over a human task, the task changes.

    In particular, there are potential changes over four dimensions: Speed, scale, scope and sophistication. The problem with AIs trading stocks isn’t that they’re better than humans—it’s that they’re faster. But computers are better at chess and Go because they use more sophisticated strategies than humans. We’re worried about AI-controlled social media accounts because they operate on a superhuman scale.

    It gets interesting when changes in degree can become changes in kind. High-speed trading is fundamentally different than regular human trading. AIs have invented fundamentally new strategies in the game of Go. Millions of AI-controlled social media accounts could fundamentally change the nature of propaganda.

    It’s these sorts of changes and how AI will affect democracy that I want to talk about.

    To start, I want to list some of AI’s core competences. First, it is really good as a summarizer. Second, AI is good at explaining things, teaching with infinite patience. Third, and related, AI can persuade. Propaganda is an offshoot of this. Fourth, AI is fundamentally a prediction technology. Predictions about whether turning left or right will get you to your destination faster. Predictions about whether a tumor is cancerous might improve medical diagnoses. Predictions about which word is likely to come next can help compose an email. Fifth, AI can assess. Assessing requires outside context and criteria. AI is less good at assessing, but it’s getting better. Sixth, AI can decide. A decision is a prediction plus an assessment. We are already using AI to make all sorts of decisions.

    How these competences translate to actual useful AI systems depends a lot on the details. We don’t know how far AI will go in replicating or replacing human cognitive functions. Or how soon that will happen. In constrained environments it can be easy. AIs already play chess and Go better than humans. Unconstrained environments are harder. There are still significant challenges to fully AI-piloted automobiles. The technologist Jaron Lanier has a nice quote , that AI does best when “human activities have been done many times before, but not in exactly the same way.”

    In this talk, I am going to be largely optimistic about the technology. I’m not going to dwell on the details of how the AI systems might work. Much of what I am talking about is still in the future. Science fiction, but not unrealistic science fiction.

    Where I am going to be less optimistic—and more realistic—is about the social implications of the technology. Again, I am less interested in how AI will substitute for humans. I’m looking more at the second-order effects of those substitutions: How the underlying systems will change because of changes in speed, scale, scope and sophistication. My goal is to imagine the possibilities. So that we might be prepared for their eventuality.

    And as I go through the possibilities, keep in mind a few questions: Will the change distribute or consolidate power? Will it make people more or less personally involved in democracy? What needs to happen before people will trust AI in this context? What could go wrong if a bad actor subverted the AI in this context? And what can we do, as security technologists, to help?

    I am thinking about democracy very broadly. Not just representations, or elections. Democracy as a system for distributing decisions evenly across a population. It’s a way of converting individual preferences into group decisions. And that includes bureaucratic decisions.

    To that end, I want to discuss five different areas where AI will affect democracy: Politics, lawmaking, administration, the legal system and, finally, citizens themselves.

    I: AI-assisted politicians

    I’ve already said that AIs are good at persuasion. Politicians will make use of that. Pretty much everyone talks about AI propaganda. Politicians will make use of that, too. But let’s talk about how this might go well.

    In the past, candidates would write books and give speeches to connect with voters. In the future, candidates will also use personalized chatbots to directly engage with voters on a variety of issues. AI can also help fundraise. I don’t have to explain the persuasive power of individually crafted appeals. AI can conduct polls. There’s some really interesting work into having large language models assume different personas and answer questions from their points of view. Unlike people, AIs are always available, will answer thousands of questions without getting tired or bored and are more reliable. This won’t replace polls, but it can augment them. AI can assist human campaign managers by coordinating campaign workers, creating talking points, doing media outreach and assisting get-out-the-vote efforts. These are all things that humans already do. So there’s no real news there.

    The changes are largely in scale. AIs can engage with voters, conduct polls and fundraise at a scale that humans cannot—for all sizes of elections. They can also assist in lobbying strategies. AIs could also potentially develop more sophisticated campaign and political strategies than humans can. I expect an arms race as politicians start using these sorts of tools. And we don’t know if the tools will favor one political ideology over another.

    More interestingly, future politicians will largely be AI-driven. I don’t mean that AI will replace humans as politicians. Absent a major cultural shift—and some serious changes in the law—that won’t happen. But as AI starts to look and feel more human, our human politicians will start to look and feel more like AI. I think we will be OK with it, because it’s a path we’ve been walking down for a long time. Any major politician today is just the public face of a complex socio-technical system. When the president makes a speech, we all know that they didn’t write it. When a legislator sends out a campaign email, we know that they didn’t write that either—even if they signed it. And when we get a holiday card from any of these people, we know that it was signed by an autopen. Those things are so much a part of politics today that we don’t even think about it. In the future, we’ll accept that almost all communications from our leaders will be written by AI. We’ll accept that they use AI tools for making political and policy decisions. And for planning their campaigns. And for everything else they do. None of this is necessarily bad. But it does change the nature of politics and politicians—just like television and the internet did.

    II: AI-assisted legislators

    AIs are already good at summarization. This can be applied to listening to constituents:  summarizing letters, comments and making sense of constituent inputs. Public meetings might be summarized. Here the scale of the problem is already overwhelming, and AI can make a big difference. Beyond summarizing, AI can highlight interesting arguments or detect bulk letter-writing campaigns. They can aid in political negotiating.

    AIs can also write laws. In November 2023, Porto Alegre, Brazil became the first city to enact a law that was entirely written by AI. It had to do with water meters. One of the councilmen prompted ChatGPT, and it produced a complete bill. He submitted it to the legislature without telling anyone who wrote it. And the humans passed it without any changes.

    A law is just a piece of generated text that a government agrees to adopt. And as with every other profession, policymakers will turn to AI to help them draft and revise text. Also, AI can take human-written laws and figure out what they actually mean. Lots of laws are recursive, referencing paragraphs and words of other laws. AIs are already good at making sense of all that.

    This means that AI will be good at finding legal loopholes—or at creating legal loopholes. I wrote about this in my latest book, A Hacker’s Mind . Finding loopholes is similar to finding vulnerabilities in software. There’s also a concept called “micro-legislation.” That’s the smallest unit of law that makes a difference to someone. It could be a word or a punctuation mark. AIs will be good at inserting micro-legislation into larger bills. More positively, AI can help figure out unintended consequences of a policy change—by simulating how the change interacts with all the other laws and with human behavior.

    AI can also write more complex law than humans can. Right now, laws tend to be general. With details to be worked out by a government agency. AI can allow legislators to propose, and then vote on, all of those details. That will change the balance of power between the legislative and the executive branches of government. This is less of an issue when the same party controls the executive and the legislative branches. It is a big deal when those branches of government are in the hands of different parties. The worry is that AI will give the most powerful groups more tools for propagating their interests.

    AI can write laws that are impossible for humans to understand. There are two kinds of laws: specific laws, like speed limits, and laws that require judgment, like those that address reckless driving. Imagine that we train an AI on lots of street camera footage to recognize reckless driving and that it gets better than humans at identifying the sort of behavior that tends to result in accidents. And because it has real-time access to cameras everywhere, it can spot it … everywhere. The AI won’t be able to explain its criteria: It would be a black-box neural net. But we could pass a law defining reckless driving by what that AI says. It would be a law that no human could ever understand. This could happen in all sorts of areas where judgment is part of defining what is illegal. We could delegate many things to the AI because of speed and scale. Market manipulation. Medical malpractice. False advertising. I don’t know if humans will accept this.

    III: AI-assisted bureaucracy

    Generative AI is already good at a whole lot of administrative paperwork tasks. It will only get better. I want to focus on a few places where it will make a big difference. It could aid in benefits administration—figuring out who is eligible for what. Humans do this today, but there is often a backlog because there aren’t enough humans. It could audit contracts. It could operate at scale, auditing all human-negotiated government contracts. It could aid in contracts negotiation. The government buys a lot of things and has all sorts of complicated rules. AI could help government contractors navigate those rules.

    More generally, it could aid in negotiations of all kinds. Think of it as a strategic adviser. This is no different than a human but could result in more complex negotiations. Human negotiations generally center around only a few issues. Mostly because that’s what humans can keep in mind. AI versus AI negotiations could potentially involve thousands of variables simultaneously. Imagine we are using an AI to aid in some international trade negotiation and it suggests a complex strategy that is beyond human understanding. Will we blindly follow the AI? Will we be more willing to do so once we have some history with its accuracy?

    And one last bureaucratic possibility: Could AI come up with better institutional designs than we have today? And would we implement them?

    IV: AI-assisted legal system

    When referring to an AI-assisted legal system, I mean this very broadly—both lawyering and judging and all the things surrounding those activities.

    AIs can be lawyers. Early attempts at having AIs write legal briefs didn’t go well . But this is already changing as the systems get more accurate. Chatbots are now able to properly cite their sources and minimize errors. Future AIs will be much better at writing legalese, drastically reducing the cost of legal counsel. And there’s every indication that it will be able to do much of the routine work that lawyers do. So let’s talk about what this means.

    Most obviously, it reduces the cost of legal advice and representation, giving it to people who currently can’t afford it. An AI public defender is going to be a lot better than an overworked not very good human public defender. But if we assume that human-plus-AI beats AI-only, then the rich get the combination, and the poor are stuck with just the AI.

    It also will result in more sophisticated legal arguments. AI’s ability to search all of the law for precedents to bolster a case will be transformative.

    AI will also change the meaning of a lawsuit. Right now, suing someone acts as a strong social signal because of the cost. If the cost drops to free, that signal will be lost. And orders of magnitude more lawsuits will be filed, which will overwhelm the court system.

    Another effect could be gutting the profession. Lawyering is based on apprenticeship. But if most of the apprentice slots are filled by AIs, where do newly minted attorneys go to get training? And then where do the top human lawyers come from? This might not happen. AI-assisted lawyers might result in more human lawyering. We don’t know yet.

    AI can help enforce the law. In a sense, this is nothing new. Automated systems already act as law enforcement—think speed trap cameras and Breathalyzers. But AI can take this kind of thing much further, like automatically identifying people who cheat on tax returns, identifying fraud on government service applications and watching all of the traffic cameras and issuing citations.

    Again, the AI is performing a task for which we don’t have enough humans. And doing it faster, and at scale. This has the obvious problem of false positives. Which could be hard to contest if the courts believe that the computer is always right. This is a thing today: If a Breathalyzer says you’re drunk, it can be hard to contest the software in court. And also the problem of bias, of course: AI law enforcers may be more and less equitable than their human predecessors.

    But most importantly, AI changes our relationship with the law. Everyone commits driving violations all the time. If we had a system of automatic enforcement, the way we all drive would change—significantly. Not everyone wants this future. Lots of people don’t want to fund the IRS, even though catching tax cheats is incredibly profitable for the government. And there are legitimate concerns as to whether this would be applied equitably.

    AI can help enforce regulations. We have no shortage of rules and regulations. What we have is a shortage of time, resources and willpower to enforce them, which means that lots of companies know that they can ignore regulations with impunity. AI can change this by decoupling the ability to enforce rules from the resources necessary to do it. This makes enforcement more scalable and efficient. Imagine putting cameras in every slaughterhouse in the country looking for animal welfare violations or fielding an AI in every warehouse camera looking for labor violations. That could create an enormous shift in the balance of power between government and corporations—which means that it will be strongly resisted by corporate power.

    AIs can provide expert opinions in court. Imagine an AI trained on millions of traffic accidents, including video footage, telemetry from cars and previous court cases. The AI could provide the court with a reconstruction of the accident along with an assignment of fault. AI could do this in a lot of cases where there aren’t enough human experts to analyze the data—and would do it better, because it would have more experience.

    AIs can also perform judging tasks, weighing evidence and making decisions, probably not in actual courtrooms, at least not anytime soon, but in other contexts. There are many areas of government where we don’t have enough adjudicators. Automated adjudication has the potential to offer everyone immediate justice. Maybe the AI does the first level of adjudication and humans handle appeals. Probably the first place we’ll see this is in contracts. Instead of the parties agreeing to binding arbitration to resolve disputes, they’ll agree to binding arbitration by AI. This would significantly decrease cost of arbitration. Which would probably significantly increase the number of disputes.

    So, let’s imagine a world where dispute resolution is both cheap and fast. If you and I are business partners, and we have a disagreement, we can get a ruling in minutes. And we can do it as many times as we want—multiple times a day, even. Will we lose the ability to disagree and then resolve our disagreements on our own? Or will this make it easier for us to be in a partnership and trust each other?

    V: AI-assisted citizens

    AI can help people understand political issues by explaining them. We can imagine both partisan and nonpartisan chatbots. AI can also provide political analysis and commentary. And it can do this at every scale. Including for local elections that simply aren’t important enough to attract human journalists. There is a lot of research going on right now on AI as moderator , facilitator , and consensus builder . Human moderators are still better, but we don’t have enough human moderators. And AI will improve over time. AI can moderate at scale, giving the capability to every decision-making group—or chatroom—or local government meeting.

    AI can act as a government watchdog. Right now, much local government effectively happens in secret because there are no local journalists covering public meetings. AI can change that, providing summaries and flagging changes in position.

    AIs can help people navigate bureaucracies by filling out forms, applying for services and contesting bureaucratic actions. This would help people get the services they deserve, especially disadvantaged people who have difficulty navigating these systems. Again, this is a task that we don’t have enough qualified humans to perform. It sounds good, but not everyone wants this. Administrative burdens can be deliberate.

    Finally, AI can eliminate the need for politicians. This one is further out there, but bear with me. Already there is research showing AI can extrapolate our political preferences. An AI personal assistant trained on and continuously attuned to your political preferences could advise you, including what to support and who to vote for. It could possibly even vote on your behalf or, more interestingly, act as your personal representative.

    This is where it gets interesting. Our system of representative democracy empowers elected officials to stand in for our collective preferences. But that has obvious problems. Representatives are necessary because people don’t pay attention to politics. And even if they did, there isn’t enough room in the debate hall for everyone to fit. So we need to pick one of us to pass laws in our name. But that selection process is incredibly inefficient. We have complex policy wants and beliefs and can make complex trade-offs. The space of possible policy outcomes is equally complex. But we can’t directly debate the policies. We can only choose one of two—or maybe a few more—candidates to do that for us. This has been called democracy’s “lossy bottleneck.” AI can change this. We can imagine a personal AI directly participating in policy debates on our behalf along with millions of other personal AIs and coming to a consensus on policy.

    More near term, AIs can result in more ballot initiatives. Instead of five or six, there might be five or six hundred, as long as the AI can reliably advise people on how to vote. It’s hard to know whether this is a good thing. I don’t think we want people to become politically passive because the AI is taking care of it. But it could result in more legislation that the majority actually wants.

    Where will AI take us?

    That’s my list. Again, watch where changes of degree result in changes in kind. The sophistication of AI lawmaking will mean more detailed laws, which will change the balance of power between the executive and the legislative branches. The scale of AI lawyering means that litigation becomes affordable to everyone, which will mean an explosion in the amount of litigation. The speed of AI adjudication means that contract disputes will get resolved much faster, which will change the nature of settlements. The scope of AI enforcement means that some laws will become impossible to evade, which will change how the rich and powerful think about them.

    I think this is all coming. The time frame is hazy, but the technology is moving in these directions.

    All of these applications need security of one form or another. Can we provide confidentiality, integrity and availability where it is needed? AIs are just computers. As such, they have all the security problems regular computers have—plus the new security risks stemming from AI and the way it is trained, deployed and used. Like everything else in security, it depends on the details.

    First, the incentives matter. In some cases, the user of the AI wants it to be both secure and accurate. In some cases, the user of the AI wants to subvert the system. Think about prompt injection attacks. In most cases, the owners of the AIs aren’t the users of the AI. As happened with search engines and social media, surveillance and advertising are likely to become the AI’s business model. And in some cases, what the user of the AI wants is at odds with what society wants.

    Second, the risks matter. The cost of getting things wrong depends a lot on the application. If a candidate’s chatbot suggests a ridiculous policy, that’s easily corrected. If an AI is helping someone fill out their immigration paperwork, a mistake can get them deported. We need to understand the rate of AI mistakes versus the rate of human mistakes—and also realize that AI mistakes are viewed differently than human mistakes. There are also different types of mistakes: false positives versus false negatives. But also, AI systems can make different kinds of mistakes than humans do—and that’s important. In every case, the systems need to be able to correct mistakes, especially in the context of democracy.

    Many of the applications are in adversarial environments. If two countries are using AI to assist in trade negotiations, they are both going to try to hack each other’s AIs. This will include attacks against the AI models but also conventional attacks against the computers and networks that are running the AIs. They’re going to want to subvert, eavesdrop on or disrupt the other’s AI.

    Some AI applications will need to run in secure environments. Large language models work best when they have access to everything, in order to train. That goes against traditional classification rules about compartmentalization.

    Fourth, power matters. AI is a technology that fundamentally magnifies power of the humans who use it, but not equally across users or applications. Can we build systems that reduce power imbalances rather than increase them? Think of the privacy versus surveillance debate in the context of AI.

    And similarly, equity matters. Human agency matters.

    And finally, trust matters. Whether or not to trust an AI is less about the AI and more about the application. Some of these AI applications are individual. Some of these applications are societal. Whether something like “fairness” matters depends on this. And there are many competing definitions of fairness that depend on the details of the system and the application. It’s the same with transparency. The need for it depends on the application and the incentives. Democratic applications are likely to require more transparency than corporate ones and probably AI models that are not owned and run by global tech monopolies.

    All of these security issues are bigger than AI or democracy. Like all of our security experience, applying it to these new systems will require some new thinking.

    AI will be one of humanity’s most important inventions. That’s probably true. What we don’t know is if this is the moment we are inventing it. Or if today’s systems are yet more over-hyped technologies. But these are security conversations we are going to need to have eventually.

    AI is fundamentally a power-enhancing technology. We need to ensure that it distributes power and doesn’t further concentrate it.

    AI is coming for democracy. Whether the changes are a net positive or negative depends on us. Let’s help tilt things to the positive.

    This essay is adapted from a keynote speech delivered at the RSA Conference in San Francisco on May 7, 2024. It originally appeared in Cyberscoop .

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      Supply Chain Attack against Courtroom Software

      news.movim.eu / Schneier · Tuesday, 28 May - 16:06

    No word on how this backdoor was installed:

    A software maker serving more than 10,000 courtrooms throughout the world hosted an application update containing a hidden backdoor that maintained persistent communication with a malicious website, researchers reported Thursday, in the latest episode of a supply-chain attack.

    The software, known as the JAVS Viewer 8, is a component of the JAVS Suite 8, an application package courtrooms use to record, play back, and manage audio and video from proceedings. Its maker, Louisville, Kentucky-based Justice AV Solutions, says its products are used in more than 10,000 courtrooms throughout the US and 11 other countries. The company has been in business for 35 years.

    It’s software used by courts; we can imagine all sort of actors who want to backdoor it.