April 15, 2023
by Bruce Schneier
Fellow and Lecturer, Harvard Kennedy School
A free monthly newsletter providing summaries, analyses, insights, and commentaries on security: computer and otherwise.
For back issues, or to subscribe, visit Crypto-Gram’s web page.
These same essays and news items appear in the Schneier on Security blog, along with a lively and intelligent comment section. An RSS feed is available.
- NetWire Remote Access Trojan Maker Arrested
- How AI Could Write Our Laws
- Upcoming Speaking Engagements
- US Citizen Hacked by Spyware
- ChatGPT Privacy Flaw
- Mass Ransomware Attack
- Exploding USB Sticks
- A Hacker’s Mind News
- Hacks at Pwn2Own Vancouver 2023
- Security Vulnerabilities in Snipping Tools
- The Security Vulnerabilities of Message Interoperability
- Russian Cyberwarfare Documents Leaked
- UK Runs Fake DDoS-for-Hire Sites
- North Korea Hacking Cryptocurrency Sites with 3CX Exploit
- FBI (and Others) Shut Down Genesis Market
- Research on AI in Adversarial Settings
- LLMs and Phishing
- Car Thieves Hacking the CAN Bus
- FBI Advising People to Avoid Public Charging Stations
- Bypassing a Theft Threat Model
- Gaining an Advantage in Roulette
- Hacking Suicide
- Upcoming Speaking Engagements
A Croatian national has been arrested for allegedly operating NetWire, a Remote Access Trojan (RAT) marketed on cybercrime forums since 2012 as a stealthy way to spy on infected systems and siphon passwords. The arrest coincided with a seizure of the NetWire sales website by the U.S. Federal Bureau of Investigation (FBI). While the defendant in this case hasn’t yet been named publicly, the NetWire website has been leaking information about the likely true identity and location of its owner for the past 11 years.
The article details the mistakes that led to the person’s address.
[2023.03.14] Nearly 90% of the multibillion-dollar federal lobbying apparatus in the United States serves corporate interests. In some cases, the objective of that money is obvious. Google pours millions into lobbying on bills related to antitrust regulation. Big energy companies expect action whenever there is a move to end drilling leases for federal lands, in exchange for the tens of millions they contribute to congressional reelection campaigns.
But lobbying strategies are not always so blunt, and the interests involved are not always so obvious. Consider, for example, a 2013 Massachusetts bill that tried to restrict the commercial use of data collected from K-12 students using services accessed via the internet. The bill appealed to many privacy-conscious education advocates, and appropriately so. But behind the justification of protecting students lay a market-altering policy: the bill was introduced at the behest of Microsoft lobbyists, in an effort to exclude Google Docs from classrooms.
What would happen if such legal-but-sneaky strategies for tilting the rules in favor of one group over another become more widespread and effective? We can see hints of an answer in the remarkable pace at which artificial-intelligence tools for everything from writing to graphic design are being developed and improved. And the unavoidable conclusion is that AI will make lobbying more guileful, and perhaps more successful.
It turns out there is a natural opening for this technology: microlegislation.
“Microlegislation” is a term for small pieces of proposed law that cater—sometimes unexpectedly—to narrow interests. Political scientist Amy McKay coined the term. She studied the 564 amendments to the Affordable Care Act (“Obamacare”) considered by the Senate Finance Committee in 2009, as well as the positions of 866 lobbying groups and their campaign contributions. She documented instances where lobbyist comments—on health-care research, vaccine services, and other provisions—were translated directly into microlegislation in the form of amendments. And she found that those groups’ financial contributions to specific senators on the committee increased the amendments’ chances of passing.
Her finding that lobbying works was no surprise. More important, McKay’s work demonstrated that computer models can predict the likely fate of proposed legislative amendments, as well as the paths by which lobbyists can most effectively secure their desired outcomes. And that turns out to be a critical piece of creating an AI lobbyist.
Lobbying has long been part of the give-and-take among human policymakers and advocates working to balance their competing interests. The danger of microlegislation—a danger greatly exacerbated by AI—is that it can be used in a way that makes it difficult to figure out who the legislation truly benefits.
Another word for a strategy like this is a “hack.” Hacks follow the rules of a system but subvert their intent. Hacking is often associated with computer systems, but the concept is also applicable to social systems like financial markets, tax codes, and legislative processes.
While the idea of monied interests incorporating AI assistive technologies into their lobbying remains hypothetical, specific machine-learning technologies exist today that would enable them to do so. We should expect these techniques to get better and their utilization to grow, just as we’ve seen in so many other domains.
Here’s how it might work.
Crafting an AI microlegislator
To make microlegislation, machine-learning systems must be able to uncover the smallest modification that could be made to a bill or existing law that would make the biggest impact on a narrow interest.
There are three basic challenges involved. First, you must create a policy proposal—small suggested changes to legal text—and anticipate whether or not a human reader would recognize the alteration as substantive. This is important; a change that isn’t detectable is more likely to pass without controversy. Second, you need to do an impact assessment to project the implications of that change for the short- or long-range financial interests of companies. Third, you need a lobbying strategizer to identify what levers of power to pull to get the best proposal into law.
Existing AI tools can tackle all three of these.
The first step, the policy proposal, leverages the core function of generative AI. Large language models, the sort that have been used for general-purpose chatbots such as ChatGPT, can easily be adapted to write like a native in different specialized domains after seeing a relatively small number of examples. This process is called fine-tuning. For example, a model “pre-trained” on a large library of generic text samples from books and the internet can be “fine-tuned” to work effectively on medical literature, computer science papers, and product reviews.
Given this flexibility and capacity for adaptation, a large language model could be fine-tuned to produce draft legislative texts, given a data set of previously offered amendments and the bills they were associated with. Training data is available. At the federal level, it’s provided by the US Government Publishing Office, and there are already tools for downloading and interacting with it. Most other jurisdictions provide similar data feeds, and there are even convenient assemblages of that data.
Meanwhile, large language models like the one underlying ChatGPT are routinely used for summarizing long, complex documents (even laws and computer code) to capture the essential points, and they are optimized to match human expectations. This capability could allow an AI assistant to automatically predict how detectable the true effect of a policy insertion may be to a human reader.
Today, it can take a highly paid team of human lobbyists days or weeks to generate and analyze alternative pieces of microlegislation on behalf of a client. With AI assistance, that could be done instantaneously and cheaply. This opens the door to dramatic increases in the scope of this kind of microlegislating, with a potential to scale across any number of bills in any jurisdiction.
Teaching machines to assess impact
Impact assessment is more complicated. There is a rich series of methods for quantifying the predicted outcome of a decision or policy, and then also optimizing the return under that model. This kind of approach goes by different names in different circles—mathematical programming in management science, utility maximization in economics, and rational design in the life sciences.
To train an AI to do this, we would need to specify some way to calculate the benefit to different parties as a result of a policy choice. That could mean estimating the financial return to different companies under a few different scenarios of taxation or regulation. Economists are skilled at building risk models like this, and companies are already required to formulate and disclose regulatory compliance risk factors to investors. Such a mathematical model could translate directly into a reward function, a grading system that could provide feedback for the model used to create policy proposals and direct the process of training it.
The real challenge in impact assessment for generative AI models would be to parse the textual output of a model like ChatGPT in terms that an economic model could readily use. Automating this would require extracting structured financial information from the draft amendment or any legalese surrounding it. This kind of information extraction, too, is an area where AI has a long history; for example, AI systems have been trained to recognize clinical details in doctors’ notes. Early indications are that large language models are fairly good at recognizing financial information in texts such as investor call transcripts. While it remains an open challenge in the field, they may even be capable of writing out multi-step plans based on descriptions in free text.
Machines as strategists
The last piece of the puzzle is a lobbying strategizer to figure out what actions to take to convince lawmakers to adopt the amendment.
Passing legislation requires a keen understanding of the complex interrelated networks of legislative offices, outside groups, executive agencies, and other stakeholders vying to serve their own interests. Each actor in this network has a baseline perspective and different factors that influence that point of view. For example, a legislator may be moved by seeing an allied stakeholder take a firm position, or by a negative news story, or by a campaign contribution.
It turns out that AI developers are very experienced at modeling these kinds of networks. Machine-learning models for network graphs have been built, refined, improved, and iterated by hundreds of researchers working on incredibly diverse problems: lidar scans used to guide self-driving cars, the chemical functions of molecular structures, the capture of motion in actors’ joints for computer graphics, behaviors in social networks, and more.
In the context of AI-assisted lobbying, political actors like legislators and lobbyists are nodes on a graph, just like users in a social network. Relations between them are graph edges, like social connections. Information can be passed along those edges, like messages sent to a friend or campaign contributions made to a member. AI models can use past examples to learn to estimate how that information changes the network. Calculating the likelihood that a campaign contribution of a given size will flip a legislator’s vote on an amendment is one application.
McKay’s work has already shown us that there are significant, predictable relationships between these actions and the outcomes of legislation, and that the work of discovering those can be automated. Others have shown that graphs of neural network models like those described above can be applied to political systems. The full-scale use of these technologies to guide lobbying strategy is theoretical, but plausible.
Put together, these three components could create an automatic system for generating profitable microlegislation. The policy proposal system would create millions, even billions, of possible amendments. The impact assessor would identify the few that promise to be most profitable to the client. And the lobbying strategy tool would produce a blueprint for getting them passed.
What remains is for human lobbyists to walk the floors of the Capitol or state house, and perhaps supply some cash to grease the wheels. These final two aspects of lobbying—access and financing—cannot be supplied by the AI tools we envision. This suggests that lobbying will continue to primarily benefit those who are already influential and wealthy, and AI assistance will amplify their existing advantages.
The transformative benefit that AI offers to lobbyists and their clients is scale. While individual lobbyists tend to focus on the federal level or a single state, with AI assistance they could more easily infiltrate a large number of state-level (or even local-level) law-making bodies and elections. At that level, where the average cost of a seat is measured in the tens of thousands of dollars instead of millions, a single donor can wield a lot of influence—if automation makes it possible to coordinate lobbying across districts.
How to stop them
When it comes to combating the potentially adverse effects of assistive AI, the first response always seems to be to try to detect whether or not content was AI-generated. We could imagine a defensive AI that detects anomalous lobbyist spending associated with amendments that benefit the contributing group. But by then, the damage might already be done.
In general, methods for detecting the work of AI tend not to keep pace with its ability to generate convincing content. And these strategies won’t be implemented by AIs alone. The lobbyists will still be humans who take the results of an AI microlegislator and further refine the computer’s strategies. These hybrid human-AI systems will not be detectable from their output.
But the good news is: the same strategies that have long been used to combat misbehavior by human lobbyists can still be effective when those lobbyists get an AI assist. We don’t need to reinvent our democracy to stave off the worst risks of AI; we just need to more fully implement long-standing ideals.
First, we should reduce the dependence of legislatures on monolithic, multi-thousand-page omnibus bills voted on under deadline. This style of legislating exploded in the 1980s and 1990s and continues through to the most recent federal budget bill. Notwithstanding their legitimate benefits to the political system, omnibus bills present an obvious and proven vehicle for inserting unnoticed provisions that may later surprise the same legislators who approved them.
The issue is not that individual legislators need more time to read and understand each bill (that isn’t realistic or even necessary). It’s that omnibus bills must pass. There is an imperative to pass a federal budget bill, and so the capacity to push back on individual provisions that may seem deleterious (or just impertinent) to any particular group is small. Bills that are too big to fail are ripe for hacking by microlegislation.
Moreover, the incentive for legislators to introduce microlegislation catering to a narrow interest is greater if the threat of exposure is lower. To strengthen the threat of exposure for misbehaving legislative sponsors, bills should focus more tightly on individual substantive areas and, after the introduction of amendments, allow more time before the committee and floor votes. During this time, we should encourage public review and testimony to provide greater oversight.
Second, we should strengthen disclosure requirements on lobbyists, whether they’re entirely human or AI-assisted. State laws regarding lobbying disclosure are a hodgepodge. North Dakota, for example, only requires lobbying reports to be filed annually, so that by the time a disclosure is made, the policy is likely already decided. A lobbying disclosure scorecard created by Open Secrets, a group researching the influence of money in US politics, tracks nine states that do not even require lobbyists to report their compensation.
Ideally, it would be great for the public to see all communication between lobbyists and legislators, whether it takes the form of a proposed amendment or not. Absent that, let’s give the public the benefit of reviewing what lobbyists are lobbying for—and why. Lobbying is traditionally an activity that happens behind closed doors. Right now, many states reinforce that: they actually exempt testimony delivered publicly to a legislature from being reported as lobbying.
In those jurisdictions, if you reveal your position to the public, you’re no longer lobbying. Let’s do the inverse: require lobbyists to reveal their positions on issues. Some jurisdictions already require a statement of position (a ‘yea’ or ‘nay’) from registered lobbyists. And in most (but not all) states, you could make a public records request regarding meetings held with a state legislator and hope to get something substantive back. But we can expect more—lobbyists could be required to proactively publish, within a few days, a brief summary of what they demanded of policymakers during meetings and why they believe it’s in the general interest.
We can’t rely on corporations to be forthcoming and wholly honest about the reasons behind their lobbying positions. But having them on the record about their intentions would at least provide a baseline for accountability.
Finally, consider the role AI assistive technologies may have on lobbying firms themselves and the labor market for lobbyists. Many observers are rightfully concerned about the possibility of AI replacing or devaluing the human labor it automates. If the automating potential of AI ends up commodifying the work of political strategizing and message development, it may indeed put some professionals on K Street out of work.
But don’t expect that to disrupt the careers of the most astronomically compensated lobbyists: former members Congress and other insiders who have passed through the revolving door. There is no shortage of reform ideas for limiting the ability of government officials turned lobbyists to sell access to their colleagues still in government, and they should be adopted and—equally important—maintained and enforced in successive Congresses and administrations.
None of these solutions are really original, specific to the threats posed by AI, or even predominantly focused on microlegislation—and that’s the point. Good governance should and can be robust to threats from a variety of techniques and actors.
But what makes the risks posed by AI especially pressing now is how fast the field is developing. We expect the scale, strategies, and effectiveness of humans engaged in lobbying to evolve over years and decades. Advancements in AI, meanwhile, seem to be making impressive breakthroughs at a much faster pace—and it’s still accelerating.
The legislative process is a constant struggle between parties trying to control the rules of our society as they are updated, rewritten, and expanded at the federal, state, and local levels. Lobbying is an important tool for balancing various interests through our system. If it’s well-regulated, perhaps lobbying can support policymakers in making equitable decisions on behalf of us all.
This article was co-written with Nathan E. Sanders and originally appeared in MIT Technology Review.
[2023.03.14] This is a current list of where and when I am scheduled to speak:
- I’m speaking on “How to Reclaim Power in the Digital World” at EPFL in Lausanne, Switzerland, on Thursday, March 16, 2023, at 5:30 PM CET.
- I’ll be discussing my new book A Hacker’s Mind: How the Powerful Bend Society’s Rules at Harvard Science Center in Cambridge, Massachusetts, USA, on Friday, March 31, 2023, at 6:00 PM EDT.
- I’ll be discussing my book A Hacker’s Mind with Julia Angwin at the Ford Foundation Center for Social Justice in New York City, on Thursday, April 6, 2023, at 6:30 PM EDT. Register here
- I’m speaking at IT-S Now 2023 in Vienna, Austria, on June 2, 2023, at 8:30 AM CEST.
The list is maintained on this page.
A U.S. and Greek national who worked on Meta’s security and trust team while based in Greece was placed under a yearlong wiretap by the Greek national intelligence service and hacked with a powerful cyberespionage tool, according to documents obtained by The New York Times and officials with knowledge of the case.
The disclosure is the first known case of an American citizen being targeted in a European Union country by the advanced snooping technology, the use of which has been the subject of a widening scandal in Greece. It demonstrates that the illicit use of spyware is spreading beyond use by authoritarian governments against opposition figures and journalists, and has begun to creep into European democracies, even ensnaring a foreign national working for a major global corporation.
The simultaneous tapping of the target’s phone by the national intelligence service and the way she was hacked indicate that the spy service and whoever implanted the spyware, known as Predator, were working hand in hand.
TechCrunch has learned of dozens of organizations that used the affected GoAnywhere file transfer software at the time of the ransomware attack, suggesting more victims are likely to come forward.
However, while the number of victims of the mass-hack is widening, the known impact is murky at best.
Since the attack in late January or early February—the exact date is not known—Clop has disclosed less than half of the 130 organizations it claimed to have compromised via GoAnywhere, a system that can be hosted in the cloud or on an organization’s network that allows companies to securely transfer huge sets of data and other large files.
In the port city of Guayaquil, journalist Lenin Artieda of the Ecuavisa private TV station received an envelope containing a pen drive which exploded when he inserted it into a computer, his employer said.
Artieda sustained slight injuries to one hand and his face, said police official Xavier Chango. No one else was hurt.
Chango said the USB drive sent to Artieda could have been loaded with RDX, a military-type explosive.
According to police official Xavier Chango, the flash drive that went off had a 5-volt explosive charge and is thought to have used RDX. Also known as T4, according to the Environmental Protection Agency (PDF), militaries, including the US’s, use RDX, which “can be used alone as a base charge for detonators or mixed with other explosives, such as TNT.” Chango said it comes in capsules measuring about 1 cm, but only half of it was activated in the drive that Artieda plugged in, which likely saved him some harm.
Reminds me of assassination by cell phone.
Reviews are consistently good. I have been enjoying giving podcast interviews. It all feels pretty good right now.
You can order a signed book from me here.
On the first day of Pwn2Own Vancouver 2023, security researchers successfully demoed Tesla Model 3, Windows 11, and macOS zero-day exploits and exploit chains to win $375,000 and a Tesla Model 3.
The first to fall was Adobe Reader in the enterprise applications category after Haboob SA’s Abdul Aziz Hariri (@abdhariri) used an exploit chain targeting a 6-bug logic chain abusing multiple failed patches which escaped the sandbox and bypassed a banned API list on macOS to earn $50,000.
The STAR Labs team (@starlabs_sg) demoed a zero-day exploit chain targeting Microsoft’s SharePoint team collaboration platform that brought them a $100,000 reward and successfully hacked Ubuntu Desktop with a previously known exploit for $15,000.
Synacktiv (@Synacktiv) took home $100,000 and a Tesla Model 3 after successfully executing a TOCTOU (time-of-check to time-of-use) attack against the Tesla-Gateway in the Automotive category. They also used a TOCTOU zero-day vulnerability to escalate privileges on Apple macOS and earned $40,000.
Oracle VirtualBox was hacked using an OOB Read and a stacked-based buffer overflow exploit chain (worth $40,000) by Qrious Security’s Bien Pham (@bienpnn).
Last but not least, Marcin Wiązowski elevated privileges on Windows 11 using an improper input validation zero-day that came with a $30,000 prize.
EDITED TO ADD (4/14): Steven Murdoch has a good explanation as to why this happened—and to two very different snipping tools.
The Digital Markets Act ruled that users on different platforms should be able to exchange messages with each other. This opens up a real Pandora’s box. How will the networks manage keys, authenticate users, and moderate content? How much metadata will have to be shared, and how?
In our latest paper, One Protocol to Rule Them All? On Securing Interoperable Messaging, we explore the security tensions, the conflicts of interest, the usability traps, and the likely consequences for individual and institutional behaviour.
Interoperability will vastly increase the attack surface at every level in the stack from the cryptography up through usability to commercial incentives and the opportunities for government interference.
It’s a good idea in theory, but will likely result in the overall security being the worst of each platform’s security.
Thousands of pages of secret documents reveal how Vulkan’s engineers have worked for Russian military and intelligence agencies to support hacking operations, train operatives before attacks on national infrastructure, spread disinformation and control sections of the internet.
The company’s work is linked to the federal security service or FSB, the domestic spy agency; the operational and intelligence divisions of the armed forces, known as the GOU and GRU; and the SVR, Russia’s foreign intelligence organisation.
Lots more at the link.
The documents are in Russian, so it will be a while before we get translations.
EDITED TO ADD (4/1): More information.
The NCA says all of its fake so-called “booter” or “stresser” sites – which have so far been accessed by several thousand people—have been created to look like they offer the tools and services that enable cyber criminals to execute these attacks.
“However, after users register, rather than being given access to cyber crime tools, their data is collated by investigators,” reads an NCA advisory on the program. “Users based in the UK will be contacted by the National Crime Agency or police and warned about engaging in cyber crime. Information relating to those based overseas is being passed to international law enforcement.”
The NCA declined to say how many phony booter sites it had set up, or for how long they have been running. The NCA says hiring or launching attacks designed to knock websites or users offline is punishable in the UK under the Computer Misuse Act 1990.
“Going forward, people who wish to use these services can’t be sure who is actually behind them, so why take the risk?” the NCA announcement continues.
Researchers at Russian cybersecurity firm Kaspersky today revealed that they identified a small number of cryptocurrency-focused firms as at least some of the victims of the 3CX software supply-chain attack that’s unfolded over the past week. Kaspersky declined to name any of those victim companies, but it notes that they’re based in “western Asia.”
Security firms CrowdStrike and SentinelOne last week pinned the operation on North Korean hackers, who compromised 3CX installer software that’s used by 600,000 organizations worldwide, according to the vendor. Despite the potentially massive breadth of that attack, which SentinelOne dubbed “Smooth Operator,” Kaspersky has now found that the hackers combed through the victims infected with its corrupted software to ultimately target fewer than 10 machines—at least as far as Kaspersky could observe so far—and that they seemed to be focusing on cryptocurrency firms with “surgical precision.”
Active since 2018, Genesis Market’s slogan was, “Our store sells bots with logs, cookies, and their real fingerprints.” Customers could search for infected systems with a variety of options, including by Internet address or by specific domain names associated with stolen credentials.
But earlier today, multiple domains associated with Genesis had their homepages replaced with a seizure notice from the FBI, which said the domains were seized pursuant to a warrant issued by the U.S. District Court for the Eastern District of Wisconsin.
The U.S. Attorney’s Office for the Eastern District of Wisconsin did not respond to requests for comment. The FBI declined to comment.
But sources close to the investigation tell KrebsOnSecurity that law enforcement agencies in the United States, Canada and across Europe are currently serving arrest warrants on dozens of individuals thought to support Genesis, either by maintaining the site or selling the service bot logs from infected systems.
The seizure notice includes the seals of law enforcement entities from several countries, including Australia, Canada, Denmark, Germany, the Netherlands, Spain, Sweden and the United Kingdom.
As progress in AI continues to advance, it is important to know how advanced systems will make choices and in what ways they may fail. Machines can already outsmart humans in some domains, and understanding how to safely build ones which may have capabilities at or above the human level is of particular concern. One might suspect that artificially generally intelligent (AGI) and artificially superintelligent (ASI) will be systems that humans cannot reliably outsmart. As a challenge to this assumption, this paper presents the Achilles Heel hypothesis which states that even a potentially superintelligent system may nonetheless have stable decision-theoretic delusions which cause them to make irrational decisions in adversarial settings. In a survey of key dilemmas and paradoxes from the decision theory literature, a number of these potential Achilles Heels are discussed in context of this hypothesis. Several novel contributions are made toward understanding the ways in which these weaknesses might be implanted into a system.
[2023.04.10] Here’s an experiment being run by undergraduate computer science students everywhere: Ask ChatGPT to generate phishing emails, and test whether these are better at persuading victims to respond or click on the link than the usual spam. It’s an interesting experiment, and the results are likely to vary wildly based on the details of the experiment.
But while it’s an easy experiment to run, it misses the real risk of large language models (LLMs) writing scam emails. Today’s human-run scams aren’t limited by the number of people who respond to the initial email contact. They’re limited by the labor-intensive process of persuading those people to send the scammer money. LLMs are about to change that. A decade ago, one type of spam email had become a punchline on every late-night show: “I am the son of the late king of Nigeria in need of your assistance….” Nearly everyone had gotten one or a thousand of those emails, to the point that it seemed everyone must have known they were scams.
So why were scammers still sending such obviously dubious emails? In 2012, researcher Cormac Herley offered an answer: It weeded out all but the most gullible. A smart scammer doesn’t want to waste their time with people who reply and then realize it’s a scam when asked to wire money. By using an obvious scam email, the scammer can focus on the most potentially profitable people. It takes time and effort to engage in the back-and-forth communications that nudge marks, step by step, from interlocutor to trusted acquaintance to pauper.
Long-running financial scams are now known as pig butchering, growing the potential mark up until their ultimate and sudden demise. Such scams, which require gaining trust and infiltrating a target’s personal finances, take weeks or even months of personal time and repeated interactions. It’s a high stakes and low probability game that the scammer is playing.
Here is where LLMs will make a difference. Much has been written about the unreliability of OpenAI’s GPT models and those like them: They “hallucinate” frequently, making up things about the world and confidently spouting nonsense. For entertainment, this is fine, but for most practical uses it’s a problem. It is, however, not a bug but a feature when it comes to scams: LLMs’ ability to confidently roll with the punches, no matter what a user throws at them, will prove useful to scammers as they navigate hostile, bemused, and gullible scam targets by the billions. AI chatbot scams can ensnare more people, because the pool of victims who will fall for a more subtle and flexible scammer—one that has been trained on everything ever written online—is much larger than the pool of those who believe the king of Nigeria wants to give them a billion dollars.
Personal computers are powerful enough today that they can run compact LLMs. After Facebook’s new model, LLaMA, was leaked online, developers tuned it to run fast and cheaply on powerful laptops. Numerous other open-source LLMs are under development, with a community of thousands of engineers and scientists.
A single scammer, from their laptop anywhere in the world, can now run hundreds or thousands of scams in parallel, night and day, with marks all over the world, in every language under the sun. The AI chatbots will never sleep and will always be adapting along their path to their objectives. And new mechanisms, from ChatGPT plugins to LangChain, will enable composition of AI with thousands of API-based cloud services and open source tools, allowing LLMs to interact with the internet as humans do. The impersonations in such scams are no longer just princes offering their country’s riches. They are forlorn strangers looking for romance, hot new cryptocurrencies that are soon to skyrocket in value, and seemingly-sound new financial websites offering amazing returns on deposits. And people are already falling in love with LLMs.
This is a change in both scope and scale. LLMs will change the scam pipeline, making them more profitable than ever. We don’t know how to live in a world with a billion, or 10 billion, scammers that never sleep.
There will also be a change in the sophistication of these attacks. This is due not only to AI advances, but to the business model of the internet—surveillance capitalism—which produces troves of data about all of us, available for purchase from data brokers. Targeted attacks against individuals, whether for phishing or data collection or scams, were once only within the reach of nation-states. Combine the digital dossiers that data brokers have on all of us with LLMs, and you have a tool tailor-made for personalized scams.
Companies like OpenAI attempt to prevent their models from doing bad things. But with the release of each new LLM, social media sites buzz with new AI jailbreaks that evade the new restrictions put in place by the AI’s designers. ChatGPT, and then Bing Chat, and then GPT-4 were all jailbroken within minutes of their release, and in dozens of different ways. Most protections against bad uses and harmful output are only skin-deep, easily evaded by determined users. Once a jailbreak is discovered, it usually can be generalized, and the community of users pulls the LLM open through the chinks in its armor. And the technology is advancing too fast for anyone to fully understand how they work, even the designers.
This is all an old story, though: It reminds us that many of the bad uses of AI are a reflection of humanity more than they are a reflection of AI technology itself. Scams are nothing new—simply intent and then action of one person tricking another for personal gain. And the use of others as minions to accomplish scams is sadly nothing new or uncommon: For example, organized crime in Asia currently kidnaps or indentures thousands in scam sweatshops. Is it better that organized crime will no longer see the need to exploit and physically abuse people to run their scam operations, or worse that they and many others will be able to scale up scams to an unprecedented level?
Defense can and will catch up, but before it does, our signal-to-noise ratio is going to drop dramatically.
This essay was written with Barath Raghavan, and previously appeared on Wired.com.
Avoid using free charging stations in airports, hotels, or shopping centers. Bad actors have figured out ways to use public USB ports to introduce malware and monitoring software onto devices that access these ports. Carry your own charger and USB cord and use an electrical outlet instead.
How much of a risk is this, really? I am unconvinced, although I do carry a USB condom for charging stations I find suspicious.
I wrote about this kind of thing in 2000, in Secrets and Lies (page 318):
My favorite example is a band of California art thieves that would break into people’s houses by cutting a hole in their walls with a chainsaw. The attacker completely bypassed the threat model of the defender. The countermeasures that the homeowner put in place were door and window alarms; they didn’t make a difference to this attack.
The article says they took half a million dollars worth of iPhones. I don’t understand iPhone device security, but don’t they have a system of denying stolen phones access to the network?
EDITED TO ADD (4/13): A commenter says: “Locked idevices will still sell for 40-60% of their value on eBay and co, they will go to Chinese shops to be stripped for parts. A aftermarket ‘oem-quality’ iPhone 14 display is $400+ alone on ifixit.”
On a perfect [roulette] wheel, the ball would always fall in a random way. But over time, wheels develop flaws, which turn into patterns. A wheel that’s even marginally tilted could develop what Barnett called a ‘drop zone.’ When the tilt forces the ball to climb a slope, the ball decelerates and falls from the outer rim at the same spot on almost every spin. A similar thing can happen on equipment worn from repeated use, or if a croupier’s hand lotion has left residue, or for a dizzying number of other reasons. A drop zone is the Achilles’ heel of roulette. That morsel of predictability is enough for software to overcome the random skidding and bouncing that happens after the drop.”
You want to commit suicide, but it’s a mortal sin: your soul goes straight to hell, forever. So what you do is murder someone. That will get you executed, but if you confess your sins to a priest beforehand you avoid hell. Problem solved.
This was actually a problem in the 17th and 18th centuries in Northern Europe, particularly Denmark. And it remained a problem until capital punishment was abolished for murder.
It’s a clever hack. I didn’t learn about it in time to put it in my book, A Hacker’s Mind, but I have several other good hacks of religious rules.
[2023.04.14] This is a current list of where and when I am scheduled to speak:
- I’m speaking on “Cybersecurity Thinking to Reinvent Democracy” at RSA Conference 2023 in San Francisco, California, on Tuesday, April 25, 2023, at 9:40 AM PT.
- I’m speaking at IT-S Now 2023 in Vienna, Austria, on June 2, 2023 at 8:30 AM CEST.
The list is maintained on this page.
Since 1998, CRYPTO-GRAM has been a free monthly newsletter providing summaries, analyses, insights, and commentaries on security technology. To subscribe, or to read back issues, see Crypto-Gram’s web page.
You can also read these articles on my blog, Schneier on Security.
Please feel free to forward CRYPTO-GRAM, in whole or in part, to colleagues and friends who will find it valuable. Permission is also granted to reprint CRYPTO-GRAM, as long as it is reprinted in its entirety.
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.