September 15, 2023
by Bruce Schneier
Fellow and Lecturer, Harvard Kennedy School
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- Zoom Can Spy on Your Calls and Use the Conversation to Train AI, But Says That It Won’t
- UK Electoral Commission Hacked
- Detecting “Violations of Social Norms” in Text with AI
- Bots Are Better than Humans at Solving CAPTCHAs
- White House Announces AI Cybersecurity Challenge
- Applying AI to License Plate Surveillance
- December’s Reimagining Democracy Workshop
- Parmesan Anti-Forgery Protection
- Hacking Food Labeling Laws
- Remotely Stopping Polish Trains
- Identity Theft from 1965 Uncovered through Face Recognition
- When Apps Go Rogue
- Own Your Own Government Surveillance Van
- Spyware Vendor Hacked
- Inconsistencies in the Common Vulnerability Scoring System (CVSS)
- Cryptocurrency Startup Loses Encryption Key for Electronic Wallet
- The Hacker Tool to Get Personal Data from Credit Bureaus
- LLMs and Tool Use
- On Robots Killing People
- Cars Have Terrible Data Privacy
- Zero-Click Exploit in iPhones
- Fake Signal and Telegram Apps in the Google Play Store
- Upcoming Speaking Engagements
Zoom updated its Terms of Service in March, spelling out that the company reserves the right to train AI on user data with no mention of a way to opt out. On Monday, the company said in a blog post that there’s no need to worry about that. Zoom execs swear the company won’t actually train its AI on your video calls without permission, even though the Terms of Service still say it can.
Of course, these are Terms of Service. They can change at any time. Zoom can renege on its promise at any time. There are no rules, only the whims of the company as it tries to maximize its profits.
It’s a stupid way to run a technological revolution. We should not have to rely on the benevolence of for-profit corporations to protect our rights. It’s not their job, and it shouldn’t be.
We worked with external security experts and the National Cyber Security Centre to investigate and secure our systems.
If the hack was by a major government, the odds are really low that it has resecured its systems—unless it burned the network to the ground and rebuilt it from scratch (which seems unlikely).
[2023.08.17] Researchers are trying to use AI to detect “social norms violations.” Feels a little sketchy right now, but this is the sort of thing that AIs will get better at. (Like all of these systems, anything but a very low false positive rate makes the detection useless in practice.)
[2023.08.18] Interesting research: “An Empirical Study & Evaluation of Modern CAPTCHAs“:
Abstract: For nearly two decades, CAPTCHAS have been widely used as a means of protection against bots. Throughout the years, as their use grew, techniques to defeat or bypass CAPTCHAS have continued to improve. Meanwhile, CAPTCHAS have also evolved in terms of sophistication and diversity, becoming increasingly difficult to solve for both bots (machines) and humans. Given this long-standing and still-ongoing arms race, it is critical to investigate how long it takes legitimate users to solve modern CAPTCHAS, and how they are perceived by those users.
In this work, we explore CAPTCHAS in the wild by evaluating users’ solving performance and perceptions of unmodified currently-deployed CAPTCHAS. We obtain this data through manual inspection of popular websites and user studies in which 1, 400 participants collectively solved 14, 000 CAPTCHAS. Results show significant differences between the most popular types of CAPTCHAS: surprisingly, solving time and user perception are not always correlated. We performed a comparative study to investigate the effect of experimental context specifically the difference between solving CAPTCHAS directly versus solving them as part of a more natural task, such as account creation. Whilst there were several potential confounding factors, our results show that experimental context could have an impact on this task, and must be taken into account in future CAPTCHA studies. Finally, we investigate CAPTCHA-induced user task abandonment by analyzing participants who start and do not complete the task.
And let’s all rewatch this great ad from 2022.
The new AI cyber challenge (which is being abbreviated “AIxCC”) will have a number of different phases. Interested would-be competitors can now submit their proposals to the Small Business Innovation Research program for evaluation and, eventually, selected teams will participate in a 2024 “qualifying event.” During that event, the top 20 teams will be invited to a semifinal competition at that year’s DEF CON, another large cybersecurity conference, where the field will be further whittled down.
To secure the top spot in DARPA’s new competition, participants will have to develop security solutions that do some seriously novel stuff. “To win first-place, and a top prize of $4 million, finalists must build a system that can rapidly defend critical infrastructure code from attack,” said Perri Adams, program manager for DARPA’s Information Innovation Office, during a Zoom call with reporters Tuesday. In other words: the government wants software that is capable of identifying and mitigating risks by itself.
This is a great idea. I was a big fan of DARPA’s AI capture-the-flag event in 2016, and am happy to see that DARPA is again inciting research in this area. (China has been doing this every year since 2017.)
Typically, Automatic License Plate Recognition (ALPR) technology is used to search for plates linked to specific crimes. But in this case it was used to examine the driving patterns of anyone passing one of Westchester County’s 480 cameras over a two-year period. Zayas’ lawyer Ben Gold contested the AI-gathered evidence against his client, decrying it as “dragnet surveillance.”
And he had the data to back it up. A FOIA he filed with the Westchester police revealed that the ALPR system was scanning over 16 million license plates a week, across 480 ALPR cameras. Of those systems, 434 were stationary, attached to poles and signs, while the remaining 46 were mobile, attached to police vehicles. The AI was not just looking at license plates either. It had also been taking notes on vehicles’ make, model and color—useful when a plate number for a suspect vehicle isn’t visible or is unknown.
[2023.08.23] Imagine that we’ve all—all of us, all of society—landed on some alien planet, and we have to form a government: clean slate. We don’t have any legacy systems from the US or any other country. We don’t have any special or unique interests to perturb our thinking.
How would we govern ourselves?
It’s unlikely that we would use the systems we have today. The modern representative democracy was the best form of government that mid-eighteenth-century technology could conceive of. The twenty-first century is a different place scientifically, technically and socially.
For example, the mid-eighteenth-century democracies were designed under the assumption that both travel and communications were hard. Does it still make sense for all of us living in the same place to organize every few years and choose one of us to go to a big room far away and create laws in our name?
Representative districts are organized around geography, because that’s the only way that made sense 200-plus years ago. But we don’t have to do it that way. We can organize representation by age: one representative for the thirty-one-year-olds, another for the thirty-two-year-olds, and so on. We can organize representation randomly: by birthday, perhaps. We can organize any way we want.
US citizens currently elect people for terms ranging from two to six years. Is ten years better? Is ten days better? Again, we have more technology and therefor more options.
Indeed, as a technologist who studies complex systems and their security, I believe the very idea of representative government is a hack to get around the technological limitations of the past. Voting at scale is easier now than it was 200 year ago. Certainly we don’t want to all have to vote on every amendment to every bill, but what’s the optimal balance between votes made in our name and ballot measures that we all vote on?
In December 2022, I organized a workshop to discuss these and other questions. I brought together fifty people from around the world: political scientists, economists, law professors, AI experts, activists, government officials, historians, science fiction writers and more. We spent two days talking about these ideas. Several themes emerged from the event.
Misinformation and propaganda were themes, of course—and the inability to engage in rational policy discussions when people can’t agree on the facts.
Another theme was the harms of creating a political system whose primary goals are economic. Given the ability to start over, would anyone create a system of government that optimizes the near-term financial interest of the wealthiest few? Or whose laws benefit corporations at the expense of people?
Another theme was capitalism, and how it is or isn’t intertwined with democracy. And while the modern market economy made a lot of sense in the industrial age, it’s starting to fray in the information age. What comes after capitalism, and how does it affect how we govern ourselves?
Many participants examined the effects of technology, especially artificial intelligence. We looked at whether—and when—we might be comfortable ceding power to an AI. Sometimes it’s easy. I’m happy for an AI to figure out the optimal timing of traffic lights to ensure the smoothest flow of cars through the city. When will we be able to say the same thing about setting interest rates? Or designing tax policies?
How would we feel about an AI device in our pocket that voted in our name, thousands of times per day, based on preferences that it inferred from our actions? If an AI system could determine optimal policy solutions that balanced every voter’s preferences, would it still make sense to have representatives? Maybe we should vote directly for ideas and goals instead, and leave the details to the computers. On the other hand, technological solutionism regularly fails.
Scale was another theme. The size of modern governments reflects the technology at the time of their founding. European countries and the early American states are a particular size because that’s what was governable in the 18th and 19th centuries. Larger governments—the US as a whole, the European Union—reflect a world in which travel and communications are easier. The problems we have today are primarily either local, at the scale of cities and towns, or global—even if they are currently regulated at state, regional or national levels. This mismatch is especially acute when we try to tackle global problems. In the future, do we really have a need for political units the size of France or Virginia? Or is it a mixture of scales that we really need, one that moves effectively between the local and the global?
As to other forms of democracy, we discussed one from history and another made possible by today’s technology.
Sortition is a system of choosing political officials randomly to deliberate on a particular issue. We use it today when we pick juries, but both the ancient Greeks and some cities in Renaissance Italy used it to select major political officials. Today, several countries—largely in Europe—are using sortition for some policy decisions. We might randomly choose a few hundred people, representative of the population, to spend a few weeks being briefed by experts and debating the problem—and then decide on environmental regulations, or a budget, or pretty much anything.
Liquid democracy does away with elections altogether. Everyone has a vote, and they can keep the power to cast it themselves or assign it to another person as a proxy. There are no set elections; anyone can reassign their proxy at any time. And there’s no reason to make this assignment all or nothing. Perhaps proxies could specialize: one set of people focused on economic issues, another group on health and a third bunch on national defense. Then regular people could assign their votes to whichever of the proxies most closely matched their views on each individual matter—or step forward with their own views and begin collecting proxy support from other people.
This all brings up another question: Who gets to participate? And, more generally, whose interests are taken into account? Early democracies were really nothing of the sort: They limited participation by gender, race and land ownership.
We should debate lowering the voting age, but even without voting we recognize that children too young to vote have rights—and, in some cases, so do other species. Should future generations get a “voice,” whatever that means? What about nonhumans or whole ecosystems?
Should everyone get the same voice? Right now in the US, the outsize effect of money in politics gives the wealthy disproportionate influence. Should we encode that explicitly? Maybe younger people should get a more powerful vote than everyone else. Or maybe older people should.
Those questions lead to ones about the limits of democracy. All democracies have boundaries limiting what the majority can decide. We all have rights: the things that cannot be taken away from us. We cannot vote to put someone in jail, for example.
But while we can’t vote a particular publication out of existence, we can to some degree regulate speech. In this hypothetical community, what are our rights as individuals? What are the rights of society that supersede those of individuals?
Personally, I was most interested in how these systems fail. As a security technologist, I study how complex systems are subverted—hacked, in my parlance—for the benefit of a few at the expense of the many. Think tax loopholes, or tricks to avoid government regulation. I want any government system to be resilient in the face of that kind of trickery.
Or, to put it another way, I want the interests of each individual to align with the interests of the group at every level. We’ve never had a system of government with that property before—even equal protection guarantees and First Amendment rights exist in a competitive framework that puts individuals’ interests in opposition to one another. But—in the age of such existential risks as climate and biotechnology and maybe AI—aligning interests is more important than ever.
Our workshop didn’t produce any answers; that wasn’t the point. Our current discourse is filled with suggestions on how to patch our political system. People regularly debate changes to the Electoral College, or the process of creating voting districts, or term limits. But those are incremental changes.
It’s hard to find people who are thinking more radically: looking beyond the horizon for what’s possible eventually. And while true innovation in politics is a lot harder than innovation in technology, especially without a violent revolution forcing change, it’s something that we as a species are going to have to get good at—one way or another.
This essay previously appeared in The Conversation.
[2023.08.25] This article talks about new Mexican laws about food labeling, and the lengths to which food manufacturers are going to ensure that they are not effective. There are the typical high-pressure lobbying tactics and lawsuits. But there’s also examples of companies hacking the laws:
Companies like Coca-Cola and Kraft Heinz have begun designing their products so that their packages don’t have a true front or back, but rather two nearly identical labels—except for the fact that only one side has the required warning. As a result, supermarket clerks often place the products with the warning facing inward, effectively hiding it.
Other companies have gotten creative in finding ways to keep their mascots, even without reformulating their foods, as is required by law. Bimbo, the international bread company that owns brands in the United States such as Entenmann’s and Takis, for example, technically removed its mascot from its packaging. It instead printed the mascot on the actual food product—a ready to eat pancake—and made the packaging clear, so the mascot is still visible to consumers.
…the saboteurs appear to have sent simple so-called “radio-stop” commands via radio frequency to the trains they targeted. Because the trains use a radio system that lacks encryption or authentication for those commands, Olejnik says, anyone with as little as $30 of off-the-shelf radio equipment can broadcast the command to a Polish train—sending a series of three acoustic tones at a 150.100 megahertz frequency—and trigger their emergency stop function.
“It is three tonal messages sent consecutively. Once the radio equipment receives it, the locomotive goes to a halt,” Olejnik says, pointing to a document outlining trains’ different technical standards in the European Union that describes the “radio-stop” command used in the Polish system. In fact, Olejnik says that the ability to send the command has been described in Polish radio and train forums and on YouTube for years. “Everybody could do this. Even teenagers trolling. The frequencies are known. The tones are known. The equipment is cheap.”
Even so, this is being described as a cyberattack.
Napoleon Gonzalez, of Etna, assumed the identity of his brother in 1965, a quarter century after his sibling’s death as an infant, and used the stolen identity to obtain Social Security benefits under both identities, multiple passports and state identification cards, law enforcement officials said.
A new investigation was launched in 2020 after facial identification software indicated Gonzalez’s face was on two state identification cards.
The facial recognition technology is used by the Maine Bureau of Motor Vehicles to ensure no one obtains multiple credentials or credentials under someone else’s name, said Emily Cook, spokesperson for the secretary of state’s office.
With more official macOS features added in 2021 that enabled the “Night Shift” dark mode, the NightOwl app was left forlorn and forgotten on many older Macs. Few of those supposed tens of thousands of users likely noticed when the app they ran in the background of their older Macs was bought by another company, nor when earlier this year that company silently updated the dark mode app so that it hijacked their machines in order to send their IP data through a server network of affected computers, AKA a botnet.
This is not an unusual story. Sometimes the apps are sold. Sometimes they’re orphaned, and then taken over by someone else.
So how was this van turned into a mobile spying center? Well, let’s start with how it has more LCD monitors than a Counterstrike LAN party. They can be used to monitor any of six different video inputs including a videoscope camera. A videoscope and a borescope are very similar as they’re both cameras on the ends of optical fibers, so the same tech you’d use to inspect cylinder walls is also useful for surveillance. Kind of cool, right? Multiple Sony DVD-based video recorders store footage captured by cameras, audio recorders by high-end equipment brand Marantz capture sounds, and time and date generators sync gathered media up for accurate analysis. Circling back around to audio, this van features seven different audio inputs including a body wire channel.
Only $26,795, but you can probably negotiate them down.
In an undated note seen by TechCrunch, the unnamed hackers described how they found and exploited several security vulnerabilities that allowed them to compromise WebDetetive’s servers and access its user databases. By exploiting other flaws in the spyware maker’s web dashboard—used by abusers to access the stolen phone data of their victims—the hackers said they enumerated and downloaded every dashboard record, including every customer’s email address.
The hackers said that dashboard access also allowed them to delete victim devices from the spyware network altogether, effectively severing the connection at the server level to prevent the device from uploading new data. “Which we definitely did. Because we could. Because #fuckstalkerware,” the hackers wrote in the note.
The note was included in a cache containing more than 1.5 gigabytes of data scraped from the spyware’s web dashboard. That data included information about each customer, such as the IP address they logged in from and their purchase history. The data also listed every device that each customer had compromised, which version of the spyware the phone was running, and the types of data that the spyware was collecting from the victim’s phone.
Shedding Light on CVSS Scoring Inconsistencies: A User-Centric Study on Evaluating Widespread Security Vulnerabilities
Abstract: The Common Vulnerability Scoring System (CVSS) is a popular method for evaluating the severity of vulnerabilities in vulnerability management. In the evaluation process, a numeric score between 0 and 10 is calculated, 10 being the most severe (critical) value. The goal of CVSS is to provide comparable scores across different evaluators. However, previous works indicate that CVSS might not reach this goal: If a vulnerability is evaluated by several analysts, their scores often differ. This raises the following questions: Are CVSS evaluations consistent? Which factors influence CVSS assessments? We systematically investigate these questions in an online survey with 196 CVSS users. We show that specific CVSS metrics are inconsistently evaluated for widespread vulnerability types, including Top 3 vulnerabilities from the ”2022 CWE Top 25 Most Dangerous Software Weaknesses” list. In a follow-up survey with 59 participants, we found that for the same vulnerabilities from the main study, 68% of these users gave different severity ratings. Our study reveals that most evaluators are aware of the problematic aspects of CVSS, but they still see CVSS as a useful tool for vulnerability assessment. Finally, we discuss possible reasons for inconsistent evaluations and provide recommendations on improving the consistency of scoring.
Here’s a summary of the research.
I can’t understand why anyone thinks these technologies are a good idea.
This is the result of a secret weapon criminals are selling access to online that appears to tap into an especially powerful set of data: the target’s credit header. This is personal information that the credit bureaus Experian, Equifax, and TransUnion have on most adults in America via their credit cards. Through a complex web of agreements and purchases, that data trickles down from the credit bureaus to other companies who offer it to debt collectors, insurance companies, and law enforcement.
A 404 Media investigation has found that criminals have managed to tap into that data supply chain, in some cases by stealing former law enforcement officer’s identities, and are selling unfettered access to their criminal cohorts online. The tool 404 Media tested has also been used to gather information on high profile targets such as Elon Musk, Joe Rogan, and even President Joe Biden, seemingly without restriction. 404 Media verified that although not always sensitive, at least some of that data is accurate.
[2023.09.08] Last March, just two weeks after GPT-4 was released, researchers at Microsoft quietly announced a plan to compile millions of APIs—tools that can do everything from ordering a pizza to solving physics equations to controlling the TV in your living room—into a compendium that would be made accessible to large language models (LLMs). This was just one milestone in the race across industry and academia to find the best ways to teach LLMs how to manipulate tools, which would supercharge the potential of AI more than any of the impressive advancements we’ve seen to date.
The Microsoft project aims to teach AI how to use any and all digital tools in one fell swoop, a clever and efficient approach. Today, LLMs can do a pretty good job of recommending pizza toppings to you if you describe your dietary preferences and can draft dialog that you could use when you call the restaurant. But most AI tools can’t place the order, not even online. In contrast, Google’s seven-year-old Assistant tool can synthesize a voice on the telephone and fill out an online order form, but it can’t pick a restaurant or guess your order. By combining these capabilities, though, a tool-using AI could do it all. An LLM with access to your past conversations and tools like calorie calculators, a restaurant menu database, and your digital payment wallet could feasibly judge that you are trying to lose weight and want a low-calorie option, find the nearest restaurant with toppings you like, and place the delivery order. If it has access to your payment history, it could even guess at how generously you usually tip. If it has access to the sensors on your smartwatch or fitness tracker, it might be able to sense when your blood sugar is low and order the pie before you even realize you’re hungry.
Perhaps the most compelling potential applications of tool use are those that give AIs the ability to improve themselves. Suppose, for example, you asked a chatbot for help interpreting some facet of ancient Roman law that no one had thought to include examples of in the model’s original training. An LLM empowered to search academic databases and trigger its own training process could fine-tune its understanding of Roman law before answering. Access to specialized tools could even help a model like this better explain itself. While LLMs like GPT-4 already do a fairly good job of explaining their reasoning when asked, these explanations emerge from a “black box” and are vulnerable to errors and hallucinations. But a tool-using LLM could dissect its own internals, offering empirical assessments of its own reasoning and deterministic explanations of why it produced the answer it did.
If given access to tools for soliciting human feedback, a tool-using LLM could even generate specialized knowledge that isn’t yet captured on the web. It could post a question to Reddit or Quora or delegate a task to a human on Amazon’s Mechanical Turk. It could even seek out data about human preferences by doing survey research, either to provide an answer directly to you or to fine-tune its own training to be able to better answer questions in the future. Over time, tool-using AIs might start to look a lot like tool-using humans. An LLM can generate code much faster than any human programmer, so it can manipulate the systems and services of your computer with ease. It could also use your computer’s keyboard and cursor the way a person would, allowing it to use any program you do. And it could improve its own capabilities, using tools to ask questions, conduct research, and write code to incorporate into itself.
It’s easy to see how this kind of tool use comes with tremendous risks. Imagine an LLM being able to find someone’s phone number, call them and surreptitiously record their voice, guess what bank they use based on the largest providers in their area, impersonate them on a phone call with customer service to reset their password, and liquidate their account to make a donation to a political party. Each of these tasks invokes a simple tool—an Internet search, a voice synthesizer, a bank app—and the LLM scripts the sequence of actions using the tools.
We don’t yet know how successful any of these attempts will be. As remarkably fluent as LLMs are, they weren’t built specifically for the purpose of operating tools, and it remains to be seen how their early successes in tool use will translate to future use cases like the ones described here. As such, giving the current generative AI sudden access to millions of APIs—as Microsoft plans to—could be a little like letting a toddler loose in a weapons depot.
Companies like Microsoft should be particularly careful about granting AIs access to certain combinations of tools. Access to tools to look up information, make specialized calculations, and examine real-world sensors all carry a modicum of risk. The ability to transmit messages beyond the immediate user of the tool or to use APIs that manipulate physical objects like locks or machines carries much larger risks. Combining these categories of tools amplifies the risks of each.
The operators of the most advanced LLMs, such as OpenAI, should continue to proceed cautiously as they begin enabling tool use and should restrict uses of their products in sensitive domains such as politics, health care, banking, and defense. But it seems clear that these industry leaders have already largely lost their moat around LLM technology—open source is catching up. Recognizing this trend, Meta has taken an “If you can’t beat ’em, join ’em” approach and partially embraced the role of providing open source LLM platforms.
On the policy front, national—and regional—AI prescriptions seem futile. Europe is the only significant jurisdiction that has made meaningful progress on regulating the responsible use of AI, but it’s not entirely clear how regulators will enforce it. And the US is playing catch-up and seems destined to be much more permissive in allowing even risks deemed “unacceptable” by the EU. Meanwhile, no government has invested in a “public option” AI model that would offer an alternative to Big Tech that is more responsive and accountable to its citizens.
Regulators should consider what AIs are allowed to do autonomously, like whether they can be assigned property ownership or register a business. Perhaps more sensitive transactions should require a verified human in the loop, even at the cost of some added friction. Our legal system may be imperfect, but we largely know how to hold humans accountable for misdeeds; the trick is not to let them shunt their responsibilities to artificial third parties. We should continue pursuing AI-specific regulatory solutions while also recognizing that they are not sufficient on their own.
We must also prepare for the benign ways that tool-using AI might impact society. In the best-case scenario, such an LLM may rapidly accelerate a field like drug discovery, and the patent office and FDA should prepare for a dramatic increase in the number of legitimate drug candidates. We should reshape how we interact with our governments to take advantage of AI tools that give us all dramatically more potential to have our voices heard. And we should make sure that the economic benefits of superintelligent, labor-saving AI are equitably distributed.
We can debate whether LLMs are truly intelligent or conscious, or have agency, but AIs will become increasingly capable tool users either way. Some things are greater than the sum of their parts. An AI with the ability to manipulate and interact with even simple tools will become vastly more powerful than the tools themselves. Let’s be sure we’re ready for them.
This essay was written with Nathan Sanders, and previously appeared on Wired.com.
[2023.09.11] The robot revolution began long ago, and so did the killing. One day in 1979, a robot at a Ford Motor Company casting plant malfunctioned—human workers determined that it was not going fast enough. And so twenty-five-year-old Robert Williams was asked to climb into a storage rack to help move things along. The one-ton robot continued to work silently, smashing into Williams’s head and instantly killing him. This was reportedly the first incident in which a robot killed a human; many more would follow.
At Kawasaki Heavy Industries in 1981, Kenji Urada died in similar circumstances. A malfunctioning robot he went to inspect killed him when he obstructed its path, according to Gabriel Hallevy in his 2013 book, When Robots Kill: Artificial Intelligence Under Criminal Law. As Hallevy puts it, the robot simply determined that “the most efficient way to eliminate the threat was to push the worker into an adjacent machine.” From 1992 to 2017, workplace robots were responsible for 41 recorded deaths in the United States—and that’s likely an underestimate, especially when you consider knock-on effects from automation, such as job loss. A robotic anti-aircraft cannon killed nine South African soldiers in 2007 when a possible software failure led the machine to swing itself wildly and fire dozens of lethal rounds in less than a second. In a 2018 trial, a medical robot was implicated in killing Stephen Pettitt during a routine operation that had occurred a few years earlier.
You get the picture. Robots—”intelligent” and not—have been killing people for decades. And the development of more advanced artificial intelligence has only increased the potential for machines to cause harm. Self-driving cars are already on American streets, and robotic "dogs" are being used by law enforcement. Computerized systems are being given the capabilities to use tools, allowing them to directly affect the physical world. Why worry about the theoretical emergence of an all-powerful, superintelligent program when more immediate problems are at our doorstep? Regulation must push companies toward safe innovation and innovation in safety. We are not there yet.
Historically, major disasters have needed to occur to spur regulation—the types of disasters we would ideally foresee and avoid in today’s AI paradigm. The 1905 Grover Shoe Factory disaster led to regulations governing the safe operation of steam boilers. At the time, companies claimed that large steam-automation machines were too complex to rush safety regulations. This, of course, led to overlooked safety flaws and escalating disasters. It wasn’t until the American Society of Mechanical Engineers demanded risk analysis and transparency that dangers from these huge tanks of boiling water, once considered mystifying, were made easily understandable. The 1911 Triangle Shirtwaist Factory fire led to regulations on sprinkler systems and emergency exits. And the preventable 1912 sinking of the Titanic resulted in new regulations on lifeboats, safety audits, and on-ship radios.
Perhaps the best analogy is the evolution of the Federal Aviation Administration. Fatalities in the first decades of aviation forced regulation, which required new developments in both law and technology. Starting with the Air Commerce Act of 1926, Congress recognized that the integration of aerospace tech into people’s lives and our economy demanded the highest scrutiny. Today, every airline crash is closely examined, motivating new technologies and procedures.
Any regulation of industrial robots stems from existing industrial regulation, which has been evolving for many decades. The Occupational Safety and Health Act of 1970 established safety standards for machinery, and the Robotic Industries Association, now merged into the Association for Advancing Automation, has been instrumental in developing and updating specific robot-safety standards since its founding in 1974. Those standards, with obscure names such as R15.06 and ISO 10218, emphasize inherent safe design, protective measures, and rigorous risk assessments for industrial robots.
But as technology continues to change, the government needs to more clearly regulate how and when robots can be used in society. Laws need to clarify who is responsible, and what the legal consequences are, when a robot’s actions result in harm. Yes, accidents happen. But the lessons of aviation and workplace safety demonstrate that accidents are preventable when they are openly discussed and subjected to proper expert scrutiny.
AI and robotics companies don’t want this to happen. OpenAI, for example, has reportedly fought to “water down” safety regulations and reduce AI-quality requirements. According to an article in Time, it lobbied European Union officials against classifying models like ChatGPT as “high risk” which would have brought “stringent legal requirements including transparency, traceability, and human oversight.” The reasoning was supposedly that OpenAI did not intend to put its products to high-risk use—a logical twist akin to the Titanic owners lobbying that the ship should not be inspected for lifeboats on the principle that it was a “general purpose” vessel that also could sail in warm waters where there were no icebergs and people could float for days. (OpenAI did not comment when asked about its stance on regulation; previously, it has said that “achieving our mission requires that we work to mitigate both current and longer-term risks,” and that it is working toward that goal by “collaborating with policymakers, researchers and users.”)
Large corporations have a tendency to develop computer technologies to self-servingly shift the burdens of their own shortcomings onto society at large, or to claim that safety regulations protecting society impose an unjust cost on corporations themselves, or that security baselines stifle innovation. We’ve heard it all before, and we should be extremely skeptical of such claims. Today’s AI-related robot deaths are no different from the robot accidents of the past. Those industrial robots malfunctioned, and human operators trying to assist were killed in unexpected ways. Since the first-known death resulting from the feature in January 2016, Tesla’s Autopilot has been implicated in more than 40 deaths according to official report estimates. Malfunctioning Teslas on Autopilot have deviated from their advertised capabilities by misreading road markings, suddenly veering into other cars or trees, crashing into well-marked service vehicles, or ignoring red lights, stop signs, and crosswalks. We’re concerned that AI-controlled robots already are moving beyond accidental killing in the name of efficiency and “deciding” to kill someone in order to achieve opaque and remotely controlled objectives.
As we move into a future where robots are becoming integral to our lives, we can’t forget that safety is a crucial part of innovation. True technological progress comes from applying comprehensive safety standards across technologies, even in the realm of the most futuristic and captivating robotic visions. By learning lessons from past fatalities, we can enhance safety protocols, rectify design flaws, and prevent further unnecessary loss of life.
For example, the UK government already sets out statements that safety matters. Lawmakers must reach further back in history to become more future-focused on what we must demand right now: modeling threats, calculating potential scenarios, enabling technical blueprints, and ensuring responsible engineering for building within parameters that protect society at large. Decades of experience have given us the empirical evidence to guide our actions toward a safer future with robots. Now we need the political will to regulate.
This essay was written with Davi Ottenheimer, and previously appeared on Atlantic.com.
All 25 car brands we researched earned our *Privacy Not Included warning label—making cars the official worst category of products for privacy that we have ever reviewed.
There’s a lot of details in the report. They’re all bad.
Citizen Lab says two zero-days fixed by Apple today in emergency security updates were actively abused as part of a zero-click exploit chain (dubbed BLASTPASS) to deploy NSO Group’s Pegasus commercial spyware onto fully patched iPhones.
The two bugs, tracked as CVE-2023-41064 and CVE-2023-41061, allowed the attackers to infect a fully-patched iPhone running iOS 16.6 and belonging to a Washington DC-based civil society organization via PassKit attachments containing malicious images.
“We refer to the exploit chain as BLASTPASS. The exploit chain was capable of compromising iPhones running the latest version of iOS (16.6) without any interaction from the victim,” Citizen Lab said.
“The exploit involved PassKit attachments containing malicious images sent from an attacker iMessage account to the victim.”
An app with the name Signal Plus Messenger was available on Play for nine months and had been downloaded from Play roughly 100 times before Google took it down last April after being tipped off by security firm ESET. It was also available in the Samsung app store and on signalplus[.]org, a dedicated website mimicking the official Signal.org. An app calling itself FlyGram, meanwhile, was created by the same threat actor and was available through the same three channels. Google removed it from Play in 2021. Both apps remain available in the Samsung store.
Both apps were built on open source code available from Signal and Telegram. Interwoven into that code was an espionage tool tracked as BadBazaar. The Trojan has been linked to a China-aligned hacking group tracked as GREF. BadBazaar has been used previously to target Uyghurs and other Turkic ethnic minorities. The FlyGram malware was also shared in a Uyghur Telegram group, further aligning it to previous targeting by the BadBazaar malware family.
Signal Plus could monitor sent and received messages and contacts if people connected their infected device to their legitimate Signal number, as is normal when someone first installs Signal on their device. Doing so caused the malicious app to send a host of private information to the attacker, including the device IMEI number, phone number, MAC address, operator details, location data, Wi-Fi information, emails for Google accounts, contact list, and a PIN used to transfer texts in the event one was set up by the user.
This kind of thing is really scary.
[2023.09.14] This is a current list of where and when I am scheduled to speak:
- I’m speaking at swampUP 2023 in San Jose, California, on September 13, 2023 at 11:35 AM PT.
The list is maintained on this page.
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Bruce Schneier is an internationally renowned security technologist, called a security guru by the Economist. He is the author of over one dozen books—including his latest, A Hacker’s Mind—as well as hundreds of articles, essays, and academic papers. His newsletter and blog are read by over 250,000 people. Schneier is a fellow at the Berkman Klein Center for Internet & Society at Harvard University; a Lecturer in Public Policy at the Harvard Kennedy School; a board member of the Electronic Frontier Foundation, AccessNow, and the Tor Project; and an Advisory Board Member of the Electronic Privacy Information Center and VerifiedVoting.org. He is the Chief of Security Architecture at Inrupt, Inc.
Copyright © 2023 by Bruce Schneier.