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      LLMs Acting Deceptively

      news.movim.eu / Schneier · 5 days ago - 03:12 · 1 minute

    New research: “ Deception abilities emerged in large language models “:

    Abstract: Large language models (LLMs) are currently at the forefront of intertwining AI systems with human communication and everyday life. Thus, aligning them with human values is of great importance. However, given the steady increase in reasoning abilities, future LLMs are under suspicion of becoming able to deceive human operators and utilizing this ability to bypass monitoring efforts. As a prerequisite to this, LLMs need to possess a conceptual understanding of deception strategies. This study reveals that such strategies emerged in state-of-the-art LLMs, but were nonexistent in earlier LLMs. We conduct a series of experiments showing that state-of-the-art LLMs are able to understand and induce false beliefs in other agents, that their performance in complex deception scenarios can be amplified utilizing chain-of-thought reasoning, and that eliciting Machiavellianism in LLMs can trigger misaligned deceptive behavior. GPT-4, for instance, exhibits deceptive behavior in simple test scenarios 99.16% of the time (P < 0.001). In complex second-order deception test scenarios where the aim is to mislead someone who expects to be deceived, GPT-4 resorts to deceptive behavior 71.46% of the time (P < 0.001) when augmented with chain-of-thought reasoning. In sum, revealing hitherto unknown machine behavior in LLMs, our study contributes to the nascent field of machine psychology.

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      Security and Human Behavior (SHB) 2024

      news.movim.eu / Schneier · 5 days ago - 03:11 · 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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      Online Privacy and Overfishing

      news.movim.eu / Schneier · 5 days ago - 03:06 · 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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      Exploiting Mistyped URLs

      news.movim.eu / Schneier · 6 days ago - 20:04 · 1 minute

    Interesting research: “ Hyperlink Hijacking: Exploiting Erroneous URL Links to Phantom Domains “:

    Abstract: Web users often follow hyperlinks hastily, expecting them to be correctly programmed. However, it is possible those links contain typos or other mistakes. By discovering active but erroneous hyperlinks, a malicious actor can spoof a website or service, impersonating the expected content and phishing private information. In “typosquatting,” misspellings of common domains are registered to exploit errors when users mistype a web address. Yet, no prior research has been dedicated to situations where the linking errors of web publishers (i.e. developers and content contributors) propagate to users. We hypothesize that these “hijackable hyperlinks” exist in large quantities with the potential to generate substantial traffic. Analyzing large-scale crawls of the web using high-performance computing, we show the web currently contains active links to more than 572,000 dot-com domains that have never been registered, what we term ‘phantom domains.’ Registering 51 of these, we see 88% of phantom domains exceeding the traffic of a control domain, with up to 10 times more visits. Our analysis shows that these links exist due to 17 common publisher error modes, with the phantom domains they point to free for anyone to purchase and exploit for under $20, representing a low barrier to entry for potential attackers.

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      AI and the Indian Election

      news.movim.eu / Schneier · Tuesday, 11 June - 05:44 · 5 minutes

    As India concluded the world’s largest election on June 5, 2024, with over 640 million votes counted, observers could assess how the various parties and factions used artificial intelligence technologies—and what lessons that holds for the rest of the world.

    The campaigns made extensive use of AI, including deepfake impersonations of candidates, celebrities and dead politicians. By some estimates, millions of Indian voters viewed deepfakes.

    But, despite fears of widespread disinformation, for the most part the campaigns, candidates and activists used AI constructively in the election. They used AI for typical political activities, including mudslinging, but primarily to better connect with voters.

    Deepfakes without the deception

    Political parties in India spent an estimated US$50 million on authorized AI-generated content for targeted communication with their constituencies this election cycle. And it was largely successful.

    Indian political strategists have long recognized the influence of personality and emotion on their constituents, and they started using AI to bolster their messaging. Young and upcoming AI companies like The Indian Deepfaker , which started out serving the entertainment industry, quickly responded to this growing demand for AI-generated campaign material.

    In January, Muthuvel Karunanidhi, former chief minister of the southern state of Tamil Nadu for two decades, appeared via video at his party’s youth wing conference. He wore his signature yellow scarf, white shirt, dark glasses and had his familiar stance—head slightly bent sideways. But Karunanidhi died in 2018. His party authorized the deepfake.

    In February, the All-India Anna Dravidian Progressive Federation party’s official X account posted an audio clip of Jayaram Jayalalithaa, the iconic superstar of Tamil politics colloquially called “Amma” or “Mother.” Jayalalithaa died in 2016.

    Meanwhile, voters received calls from their local representatives to discuss local issues—except the leader on the other end of the phone was an AI impersonation. Bhartiya Janta Party (BJP) workers like Shakti Singh Rathore have been frequenting AI startups to send personalized videos to specific voters about the government benefits they received and asking for their vote over WhatsApp.

    Multilingual boost

    Deepfakes were not the only manifestation of AI in the Indian elections. Long before the election began, Indian Prime Minister Narendra Modi addressed a tightly packed crowd celebrating links between the state of Tamil Nadu in the south of India and the city of Varanasi in the northern state of Uttar Pradesh. Instructing his audience to put on earphones, Modi proudly announced the launch of his “new AI technology” as his Hindi speech was translated to Tamil in real time.

    In a country with 22 official languages and almost 780 unofficial recorded languages , the BJP adopted AI tools to make Modi’s personality accessible to voters in regions where Hindi is not easily understood. Since 2022, Modi and his BJP have been using the AI-powered tool Bhashini , embedded in the NaMo mobile app , to translate Modi’s speeches with voiceovers in Telugu, Tamil, Malayalam, Kannada, Odia, Bengali, Marathi and Punjabi.

    As part of their demos, some AI companies circulated their own viral versions of Modi’s famous monthly radio show “Mann Ki Baat,” which loosely translates to “From the Heart,” which they voice cloned to regional languages.

    Adversarial uses

    Indian political parties doubled down on online trolling, using AI to augment their ongoing meme wars. Early in the election season, the Indian National Congress released a short clip to its 6 million followers on Instagram, taking the title track from a new Hindi music album named “Chor” (thief). The video grafted Modi’s digital likeness onto the lead singer and cloned his voice with reworked lyrics critiquing his close ties to Indian business tycoons.

    The BJP retaliated with its own video , on its 7-million-follower Instagram account, featuring a supercut of Modi campaigning on the streets, mixed with clips of his supporters but set to unique music. It was an old patriotic Hindi song sung by famous singer Mahendra Kapoor , who passed away in 2008 but was resurrected with AI voice cloning.

    Modi himself quote-tweeted an AI-created video of him dancing—a common meme that alters footage of rapper Lil Yachty on stage—commenting “such creativity in peak poll season is truly a delight.”

    In some cases, the violent rhetoric in Modi’s campaign that put Muslims at risk and incited violence was conveyed using generative AI tools, but the harm can be traced back to the hateful rhetoric itself and not necessarily the AI tools used to spread it.

    The Indian experience

    India is an early adopter, and the country’s experiments with AI serve as an illustration of what the rest of the world can expect in future elections. The technology’s ability to produce nonconsensual deepfakes of anyone can make it harder to tell truth from fiction, but its consensual uses are likely to make democracy more accessible.

    The Indian election’s embrace of AI that began with entertainment, political meme wars, emotional appeals to people, resurrected politicians and persuasion through personalized phone calls to voters has opened a pathway for the role of AI in participatory democracy.

    The surprise outcome of the election, with the BJP’s failure to win its predicted parliamentary majority, and India’s return to a deeply competitive political system especially highlights the possibility for AI to have a positive role in deliberative democracy and representative governance.

    Lessons for the world’s democracies

    It’s a goal of any political party or candidate in a democracy to have more targeted touch points with their constituents. The Indian elections have shown a unique attempt at using AI for more individualized communication across linguistically and ethnically diverse constituencies, and making their messages more accessible, especially to rural, low-income populations.

    AI and the future of participatory democracy could make constituent communication not just personalized but also a dialogue, so voters can share their demands and experiences directly with their representatives—at speed and scale.

    India can be an example of taking its recent fluency in AI-assisted party-to-people communications and moving it beyond politics. The government is already using these platforms to provide government services to citizens in their native languages.

    If used safely and ethically, this technology could be an opportunity for a new era in representative governance, especially for the needs and experiences of people in rural areas to reach Parliament.

    This essay was written with Vandinika Shukla and previously appeared in The Conversation .

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      Using AI for Political Polling

      news.movim.eu / Schneier · Tuesday, 11 June - 05:20 · 8 minutes

    Public polling is a critical function of modern political campaigns and movements, but it isn’t what it once was. Recent US election cycles have produced copious postmortems explaining both the successes and the flaws of public polling. There are two main reasons polling fails.

    First, nonresponse has skyrocketed. It’s radically harder to reach people than it used to be. Few people fill out surveys that come in the mail anymore. Few people answer their phone when a stranger calls. Pew Research reported that 36% of the people they called in 1997 would talk to them, but only 6% by 2018. Pollsters worldwide have faced similar challenges.

    Second, people don’t always tell pollsters what they really think. Some hide their true thoughts because they are embarrassed about them. Others behave as a partisan, telling the pollster what they think their party wants them to say—or what they know the other party doesn’t want to hear.

    Despite these frailties, obsessive interest in polling nonetheless consumes our politics. Headlines more likely tout the latest changes in polling numbers than the policy issues at stake in the campaign. This is a tragedy for a democracy. We should treat elections like choices that have consequences for our lives and well-being, not contests to decide who gets which cushy job.

    Polling Machines?

    AI could change polling. AI can offer the ability to instantaneously survey and summarize the expressed opinions of individuals and groups across the web, understand trends by demographic, and offer extrapolations to new circumstances and policy issues on par with human experts. The politicians of the (near) future won’t anxiously pester their pollsters for information about the results of a survey fielded last week: they’ll just ask a chatbot what people think. This will supercharge our access to realtime, granular information about public opinion, but at the same time it might also exacerbate concerns about the quality of this information.

    I know it sounds impossible, but stick with us.

    Large language models, the AI foundations behind tools like ChatGPT, are built on top of huge corpuses of data culled from the Internet. These are models trained to recapitulate what millions of real people have written in response to endless topics, contexts, and scenarios. For a decade or more, campaigns have trawled social media, looking for hints and glimmers of how people are reacting to the latest political news. This makes asking questions of an AI chatbot similar in spirit to doing analytics on social media, except that they are generative: you can ask them new questions that no one has ever posted about before, you can generate more data from populations too small to measure robustly, and you can immediately ask clarifying questions of your simulated constituents to better understand their reasoning

    Researchers and firms are already using LLMs to simulate polling results. Current techniques are based on the ideas of AI agents. An AI agent is an instance of an AI model that has been conditioned to behave in a certain way. For example, it may be primed to respond as if it is a person with certain demographic characteristics and can access news articles from certain outlets. Researchers have set up populations of thousands of AI agents that respond as if they are individual members of a survey population, like humans on a panel that get called periodically to answer questions.

    The big difference between humans and AI agents is that the AI agents always pick up the phone, so to speak, no matter how many times you contact them. A political candidate or strategist can ask an AI agent whether voters will support them if they take position A versus B, or tweaks of those options, like policy A-1 versus A-2. They can ask that question of male voters versus female voters. They can further limit the query to married male voters of retirement age in rural districts of Illinois without college degrees who lost a job during the last recession; the AI will integrate as much context as you ask.

    What’s so powerful about this system is that it can generalize to new scenarios and survey topics, and spit out a plausible answer, even if its accuracy is not guaranteed. In many cases, it will anticipate those responses at least as well as a human political expert. And if the results don’t make sense, the human can immediately prompt the AI with a dozen follow-up questions.

    Making AI agents better polling subjects

    When we ran our own experiments in this kind of AI use case with the earliest versions of the model behind ChatGPT (GPT-3.5), we found that it did a fairly good job at replicating human survey responses. The ChatGPT agents tended to match the responses of their human counterparts fairly well across a variety of survey questions, such as support for abortion and approval of the US Supreme Court. The AI polling results had average responses, and distributions across demographic properties such as age and gender, similar to real human survey panels.

    Our major systemic failure happened on a question about US intervention in the Ukraine war.  In our experiments, the AI agents conditioned to be liberal were predominantly opposed to US intervention in Ukraine and likened it to the Iraq war. Conservative AI agents gave hawkish responses supportive of US intervention. This is pretty much what most political experts would have expected of the political equilibrium in US foreign policy at the start of the decade but was exactly wrong in the politics of today.

    This mistake has everything to do with timing. The humans were asked the question after Russia’s full-scale invasion in 2022, whereas the AI model was trained using data that only covered events through September 2021. The AI got it wrong because it didn’t know how the politics had changed. The model lacked sufficient context on crucially relevant recent events.

    We believe AI agents can overcome these shortcomings. While AI models are dependent on  the data they are trained with, and all the limitations inherent in that, what makes AI agents special is that they can automatically source and incorporate new data at the time they are asked a question. AI models can update the context in which they generate opinions by learning from the same sources that humans do. Each AI agent in a simulated panel can be exposed to the same social and media news sources as humans from that same demographic before they respond to a survey question. This works because AI agents can follow multi-step processes, such as reading a question, querying a defined database of information (such as Google, or the New York Times, or Fox News, or Reddit), and then answering a question.

    In this way, AI polling tools can simulate exposing their synthetic survey panel to whatever news is most relevant to a topic and likely to emerge in each AI agent’s own echo chamber. And they can query for other relevant contextual information, such as demographic trends and historical data. Like human pollsters, they can try to refine their expectations on the basis of factors like how expensive homes are in a respondent’s neighborhood, or how many people in that district turned out to vote last cycle.

    Likely use cases for AI polling

    AI polling will be irresistible to campaigns, and to the media. But research is already revealing when and where this tool will fail. While AI polling will always have limitations in accuracy, that makes them similar to, not different from, traditional polling. Today’s pollsters are challenged to reach sample sizes large enough to measure statistically significant differences between similar populations, and the issues of nonresponse and inauthentic response can make them systematically wrong. Yet for all those shortcomings, both traditional and AI-based polls will still be useful. For all the hand-wringing and consternation over the accuracy of US political polling, national issue surveys still tend to be accurate to within a few percentage points. If you’re running for a town council seat or in a neck-and-neck national election, or just trying to make the right policy decision within a local government, you might care a lot about those small and localized differences. But if you’re looking to track directional changes over time, or differences between demographic groups, or to uncover insights about who responds best to what message, then these imperfect signals are sufficient to help campaigns and policymakers.

    Where AI will work best is as an augmentation of more traditional human polls. Over time, AI tools will get better at anticipating human responses, and also at knowing when they will be most wrong or uncertain. They will recognize which issues and human communities are in the most flux, where the model’s training data is liable to steer it in the wrong direction. In those cases, AI models can send up a white flag and indicate that they need to engage human respondents to calibrate to real people’s perspectives. The AI agents can even be programmed to automate this. They can use existing survey tools—with all their limitations and latency—to query for authentic human responses when they need them.

    This kind of human-AI polling chimera lands us, funnily enough, not too distant from where survey research is today. Decades of social science research has led to substantial innovations in statistical methodologies for analyzing survey data. Current polling methods already do substantial modeling and projecting to predictively model properties of a general population based on sparse survey samples. Today, humans fill out the surveys and computers fill in the gaps. In the future, it will be the opposite: AI will fill out the survey and, when the AI isn’t sure what box to check, humans will fill the gaps. So if you’re not comfortable with the idea that political leaders will turn to a machine to get intelligence about which candidates and policies you want, then you should have about as many misgivings about the present as you will the future.

    And while the AI results could improve quickly, they probably won’t be seen as credible for some time. Directly asking people what they think feels more reliable than asking a computer what people think. We expect these AI-assisted polls will be initially used internally by campaigns, with news organizations relying on more traditional techniques. It will take a major election where AI is right and humans are wrong to change that.

    This essay was written with Aaron Berger, Eric Gong, and Nathan Sanders, and previously appeared on the Harvard Kennedy School Ash Center’s website .

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      Ross Anderson

      news.movim.eu / Schneier · Monday, 1 April - 00:21 · 2 minutes

    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 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 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 down from the shelf, I see his name in the acknowledgments. Which means that sometime in early 1993 I, as an unpublished book author who only wrote a couple of crypto articles for Dr. Dobbs 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 an unexpected insight. He was a cryptographer and security engineer, but also very much a generalist. He analyzed block ciphers in the 1990s, and attacks against 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 back doors, 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 other of my books from Beyond Fear on. Recently, we saw each other on only a couple of occasions every year: at this or that workshop or event. Most recently was last June, at SHB 2023 , in Pittsburgh. He was going to attend my Workshop on Reimagining Democracy , 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 had a conversation. And I am not the only one.

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