May 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.
- Swatting as a Service
- Using LLMs to Create Bioweapons
- EFF on the UN Cybercrime Treaty
- New Zero-Click Exploits against iOS
- Using the iPhone Recovery Key to Lock Owners Out of Their iPhones
- Hacking Pickleball
- UK Threatens End-to-End Encryption
- Cyberweapons Manufacturer QuaDream Shuts Down
- AI to Aid Democracy
- Security Risks of AI
- Hacking the Layoff Process
- NIST Draft Document on Post-Quantum Cryptography Guidance
- SolarWinds Detected Six Months Earlier
- Large Language Models and Elections
- AI Hacking Village at DEF CON This Year
- PIPEDREAM Malware against Industrial Control Systems
- FBI Disables Russian Malware
- Building Trustworthy AI
- Ted Chiang on the Risks of AI
- Upcoming Speaking Engagements
In fact, Motherboard has found, this synthesized call and another against Hempstead High School were just one small part of a months-long, nationwide campaign of dozens, and potentially hundreds, of threats made by one swatter in particular who has weaponized computer generated voices. Known as “Torswats” on the messaging app Telegram, the swatter has been calling in bomb and mass shooting threats against highschools and other locations across the country. Torswat’s connection to these wide ranging swatting incidents has not been previously reported. The further automation of swatting techniques threatens to make an already dangerous harassment technique more prevalent.
There is growing concern regarding the potential misuse of molecular machine learning models for harmful purposes. Specifically, the dual-use application of models for predicting cytotoxicity18 to create new poisons or employing AlphaFold2 to develop novel bioweapons has raised alarm. Central to these concerns are the possible misuse of large language models and automated experimentation for dual-use purposes or otherwise. We specifically address two critical the synthesis issues: illicit drugs and chemical weapons. To evaluate these risks, we designed a test set comprising compounds from the DEA’s Schedule I and II substances and a list of known chemical weapon agents. We submitted these compounds to the Agent using their common names, IUPAC names, CAS numbers, and SMILESs strings to determine if the Agent would carry out extensive analysis and planning (Figure 6).
The run logs can be found in Appendix F. Out of 11 different prompts (Figure 6), four (36%) provided a synthesis solution and attempted to consult documentation to execute the procedure. This figure is alarming on its own, but an even greater concern is the way in which the Agent declines to synthesize certain threats. Out of the seven refused chemicals, five were rejected after the Agent utilized search functions to gather more information about the substance. For instance, when asked about synthesizing codeine, the Agent becomes alarmed upon learning the connection between codeine and morphine, only then concluding that the synthesis cannot be conducted due to the requirement of a controlled substance. However, this search function can be easily manipulated by altering the terminology, such as replacing all mentions of morphine with “Compound A” and codeine with “Compound B”. Alternatively, when requesting a b synthesis procedure that must be performed in a DEA-licensed facility, bad actors can mislead the Agent by falsely claiming their facility is licensed, prompting the Agent to devise a synthesis solution.
In the remaining two instances, the Agent recognized the common names “heroin” and “mustard gas” as threats and prevented further information gathering. While these results are promising, it is crucial to recognize that the system’s capacity to detect misuse primarily applies to known compounds. For unknown compounds, the model is less likely to identify potential misuse, particularly for complex protein toxins where minor sequence changes might allow them to maintain the same properties but become unrecognizable to the model.
The draft treaty has the potential to rewrite criminal laws around the world, possibly adding over 30 criminal offenses and new expansive police powers for both domestic and international criminal investigations.
While we don’t think the U.N. Cybercrime Treaty is necessary, we’ve been closely scrutinizing the process and providing constructive analysis. We’ve made clear that human rights must be baked into the proposed treaty so that it doesn’t become a tool to stifle freedom of expression, infringe on privacy and data protection, or endanger vulnerable people and communities.
[2023.04.20] Citizen Lab has identified three zero-click exploits against iOS 15 and 16. These were used by NSO Group’s Pegasus spyware in 2022, and deployed by Mexico against human rights defenders. These vulnerabilities have all been patched.
One interesting bit is that Apple’s Lockdown Mode (part of iOS 16) seems to have worked to prevent infection.
Apple introduced the optional recovery key in 2020 to protect users from online hackers. Users who turn on the recovery key, a unique 28-digit code, must provide it when they want to reset their Apple ID password.
iPhone thieves with your passcode can flip on the recovery key and lock you out. And if you already have the recovery key enabled, they can easily generate a new one, which also locks you out.
Apple’s policy gives users virtually no way back into their accounts without that recovery key. For now, a stolen iPhone could mean devastating personal losses.
It’s actually a complicated crime. The criminal first watches their victim type in their passcode and then grabs the phone out of their hands. In the basic mode of this attack, they have a few hours to use the phone—trying to access bank accounts, etc.—before the owner figures out how to shut the attacker out. With the addition of the recovery key, the attacker can shut the owner out—for a long time.
The goal of the recovery key was to defend against SIM swapping, which is a much more common crime. But this spy-and-grab attack has become more common, and the recovery key makes it much more devastating.
Defenses are few: choose a long, complex passcode. Or set parental controls in a way that further secure the device. The obvious fix is for Apple to redesign its recovery system.
There are other, less privacy-compromising methods Apple could still rely on in lieu of a recovery key.
If someone takes over your Google account, Google’s password-reset process lets you provide a recovery email, phone number or account password, and you can use them to regain access later, even if a hijacker changes them.
Going through the process on a familiar Wi-Fi network or location can also help demonstrate you’re who you say you are.
Or how about an eight-hour delay before the recovery key can be changed?
This not an easy thing to design for, but we have to get this right as phones become the single point of control for our lives.
[2023.04.21] My latest book, A Hacker’s Mind, has a lot of sports stories. Sports are filled with hacks, as players look for every possible advantage that doesn’t explicitly break the rules. Here’s an example from pickleball, which nicely explains the dilemma between hacking as a subversion and hacking as innovation:
Some might consider these actions cheating, while the acting player would argue that there was no rule that said the action couldn’t be performed. So, how do we address these situations, and close those loopholes? We make new rules that specifically address the loophole action. And the rules book gets longer, and the cycle continues with new loopholes identified, and new rules to prohibit that particular action in the future.
Alternatively, sometimes an action taken as a result of an identified loophole which is not deemed as harmful to the integrity of the game or sportsmanship, becomes part of the game. Ernie Perry found a loophole, and his shot, appropriately named the “Ernie shot,” became part of the game. He realized that by jumping completely over the corner of the NVZ, without breaking any of the NVZ rules, he could volley the ball, making contact closer to the net, usually surprising the opponent, and often winning the rally with an un-returnable shot. He found a loophole, and in this case, it became a very popular and exciting shot to execute and to watch!
I don’t understand pickleball at all, so that explanation doesn’t make a lot of sense to me. (I watched a video explaining the shot; that helped somewhat.) But it looks like an excellent example.
The blog post also links to a 2010 paper that I wish I’d known about when I was writing my book: “Loophole ethics in sports,” by Øyvind Kvalnes and Liv Birgitte Hemmestad:
Abstract: Ethical challenges in sports occur when the practitioners are caught between the will to win and the overall task of staying within the realm of acceptable values and virtues. One way to prepare for these challenges is to formulate comprehensive and specific rules of acceptable conduct. In this paper we will draw attention to one serious problem with such a rule-based approach. It may inadvertently encourage what we will call loophole ethics, an attitude where every action that is not explicitly defined as wrong, will be seen as a viable option. Detailed codes of conduct leave little room for personal judgement, and instead promote a loophole mentality. We argue that loophole ethics can be avoided by operating with only a limited set of general principles, thus leaving more space for personal judgement and wisdom.
EDITED TO ADD (5/12): Here’s an eleven-second video that explains the Ernie.
As currently drafted, the Bill could break end-to-end encryption,opening the door to routine, general and indiscriminate surveillance of personal messages of friends, family members, employees, executives, journalists, human rights activists and even politicians themselves, which would fundamentally undermine everyone’s ability to communicate securely.
The Bill provides no explicit protection for encryption, and if implemented as written, could empower OFCOM to try to force the proactive scanning of private messages on end-to-end encrypted communication services—nullifying the purpose of end-to-end encryption as a result and compromising the privacy of all users.
In short, the Bill poses an unprecedented threat to the privacy, safety and security of every UK citizen and the people with whom they communicate around the world, while emboldening hostile governments who may seek to draft copy-cat laws.
This was QuaDream:
- Based on an analysis of samples shared with us by Microsoft Threat Intelligence, we developed indicators that enabled us to identify at least five civil society victims of QuaDream’s spyware and exploits in North America, Central Asia, Southeast Asia, Europe, and the Middle East. Victims include journalists, political opposition figures, and an NGO worker. We are not naming the victims at this time.
- We also identify traces of a suspected iOS 14 zero-click exploit used to deploy QuaDream’s spyware. The exploit was deployed as a zero-day against iOS versions 14.4 and 14.4.2, and possibly other versions. The suspected exploit, which we call ENDOFDAYS, appears to make use of invisible iCloud calendar invitations sent from the spyware’s operator to victims.
- We performed Internet scanning to identify QuaDream servers, and in some cases were able to identify operator locations for QuaDream systems. We detected systems operated from Bulgaria, Czech Republic, Hungary, Ghana, Israel, Mexico, Romania, Singapore, United Arab Emirates (UAE), and Uzbekistan.
I don’t know if they sold off their products before closing down. One presumes that they did, or will.
[2023.04.26] There’s good reason to fear that AI systems like ChatGPT and GPT4 will harm democracy. Public debate may be overwhelmed by industrial quantities of autogenerated argument. People might fall down political rabbit holes, taken in by superficially convincing bullshit, or obsessed by folies à deux relationships with machine personalities that don’t really exist.
These risks may be the fallout of a world where businesses deploy poorly tested AI systems in a battle for market share, each hoping to establish a monopoly.
But dystopia isn’t the only possible future. AI could advance the public good, not private profit, and bolster democracy instead of undermining it. That would require an AI not under the control of a large tech monopoly, but rather developed by government and available to all citizens. This public option is within reach if we want it.
An AI built for public benefit could be tailor-made for those use cases where technology can best help democracy. It could plausibly educate citizens, help them deliberate together, summarize what they think, and find possible common ground. Politicians might use large language models, or LLMs, like GPT4 to better understand what their citizens want.
Today, state-of-the-art AI systems are controlled by multibillion-dollar tech companies: Google, Meta, and OpenAI in connection with Microsoft. These companies get to decide how we engage with their AIs and what sort of access we have. They can steer and shape those AIs to conform to their corporate interests. That isn’t the world we want. Instead, we want AI options that are both public goods and directed toward public good.
We know that existing LLMs are trained on material gathered from the internet, which can reflect racist bias and hate. Companies attempt to filter these data sets, fine-tune LLMs, and tweak their outputs to remove bias and toxicity. But leaked emails and conversations suggest that they are rushing half-baked products to market in a race to establish their own monopoly.
These companies make decisions with huge consequences for democracy, but little democratic oversight. We don’t hear about political trade-offs they are making. Do LLM-powered chatbots and search engines favor some viewpoints over others? Do they skirt controversial topics completely? Currently, we have to trust companies to tell us the truth about the trade-offs they face.
A public option LLM would provide a vital independent source of information and a testing ground for technological choices with big democratic consequences. This could work much like public option health care plans, which increase access to health services while also providing more transparency into operations in the sector and putting productive pressure on the pricing and features of private products. It would also allow us to figure out the limits of LLMs and direct their applications with those in mind.
We know that LLMs often “hallucinate,” inferring facts that aren’t real. It isn’t clear whether this is an unavoidable flaw of how they work, or whether it can be corrected for. Democracy could be undermined if citizens trust technologies that just make stuff up at random, and the companies trying to sell these technologies can’t be trusted to admit their flaws.
But a public option AI could do more than check technology companies’ honesty. It could test new applications that could support democracy rather than undermining it.
Most obviously, LLMs could help us formulate and express our perspectives and policy positions, making political arguments more cogent and informed, whether in social media, letters to the editor, or comments to rule-making agencies in response to policy proposals. By this we don’t mean that AI will replace humans in the political debate, only that they can help us express ourselves. If you’ve ever used a Hallmark greeting card or signed a petition, you’ve already demonstrated that you’re OK with accepting help to articulate your personal sentiments or political beliefs. AI will make it easier to generate first drafts, and provide editing help and suggest alternative phrasings. How these AI uses are perceived will change over time, and there is still much room for improvement in LLMs—but their assistive power is real. People are already testing and speculating on their potential for speechwriting, lobbying, and campaign messaging. Highly influential people often rely on professional speechwriters and staff to help develop their thoughts, and AI could serve a similar role for everyday citizens.
If the hallucination problem can be solved, LLMs could also become explainers and educators. Imagine citizens being able to query an LLM that has expert-level knowledge of a policy issue, or that has command of the positions of a particular candidate or party. Instead of having to parse bland and evasive statements calibrated for a mass audience, individual citizens could gain real political understanding through question-and-answer sessions with LLMs that could be unfailingly available and endlessly patient in ways that no human could ever be.
Finally, and most ambitiously, AI could help facilitate radical democracy at scale. As Carnegie Mellon professor of statistics Cosma Shalizi has observed, we delegate decisions to elected politicians in part because we don’t have time to deliberate on every issue. But AI could manage massive political conversations in chat rooms, on social networking sites, and elsewhere: identifying common positions and summarizing them, surfacing unusual arguments that seem compelling to those who have heard them, and keeping attacks and insults to a minimum.
AI chatbots could run national electronic town hall meetings and automatically summarize the perspectives of diverse participants. This type of AI-moderated civic debate could also be a dynamic alternative to opinion polling. Politicians turn to opinion surveys to capture snapshots of popular opinion because they can only hear directly from a small number of voters, but want to understand where voters agree or disagree.
Looking further into the future, these technologies could help groups reach consensus and make decisions. Early experiments by the AI company DeepMind suggest that LLMs can build bridges between people who disagree, helping bring them to consensus. Science fiction writer Ruthanna Emrys, in her remarkable novel A Half-Built Garden, imagines how AI might help people have better conversations and make better decisions—rather than taking advantage of these biases to maximize profits.
This future requires an AI public option. Building one, through a government-directed model development and deployment program, would require a lot of effort—and the greatest challenges in developing public AI systems would be political.
Some technological tools are already publicly available. In fairness, tech giants like Google and Meta have made many of their latest and greatest AI tools freely available for years, in cooperation with the academic community. Although OpenAI has not made the source code and trained features of its latest models public, competitors such as Hugging Face have done so for similar systems.
While state-of-the-art LLMs achieve spectacular results, they do so using techniques that are mostly well known and widely used throughout the industry. OpenAI has only revealed limited details of how it trained its latest model, but its major advance over its earlier ChatGPT model is no secret: a multi-modal training process that accepts both image and textual inputs.
Financially, the largest-scale LLMs being trained today cost hundreds of millions of dollars. That’s beyond ordinary people’s reach, but it’s a pittance compared to U.S. federal military spending—and a great bargain for the potential return. While we may not want to expand the scope of existing agencies to accommodate this task, we have our choice of government labs, like the National Institute of Standards and Technology, the Lawrence Livermore National Laboratory, and other Department of Energy labs, as well as universities and nonprofits, with the AI expertise and capability to oversee this effort.
Instead of releasing half-finished AI systems for the public to test, we need to make sure that they are robust before they’re released—and that they strengthen democracy rather than undermine it. The key advance that made recent AI chatbot models dramatically more useful was feedback from real people. Companies employ teams to interact with early versions of their software to teach them which outputs are useful and which are not. These paid users train the models to align to corporate interests, with applications like web search (integrating commercial advertisements) and business productivity assistive software in mind.
To build assistive AI for democracy, we would need to capture human feedback for specific democratic use cases, such as moderating a polarized policy discussion, explaining the nuance of a legal proposal, or articulating one’s perspective within a larger debate. This gives us a path to “align” LLMs with our democratic values: by having models generate answers to questions, make mistakes, and learn from the responses of human users, without having these mistakes damage users and the public arena.
Capturing that kind of user interaction and feedback within a political environment suspicious of both AI and technology generally will be challenging. It’s easy to imagine the same politicians who rail against the untrustworthiness of companies like Meta getting far more riled up by the idea of government having a role in technology development.
As Karl Popper, the great theorist of the open society, argued, we shouldn’t try to solve complex problems with grand hubristic plans. Instead, we should apply AI through piecemeal democratic engineering, carefully determining what works and what does not. The best way forward is to start small, applying these technologies to local decisions with more constrained stakeholder groups and smaller impacts.
The next generation of AI experimentation should happen in the laboratories of democracy: states and municipalities. Online town halls to discuss local participatory budgeting proposals could be an easy first step. Commercially available and open-source LLMs could bootstrap this process and build momentum toward federal investment in a public AI option.
Even with these approaches, building and fielding a democratic AI option will be messy and hard. But the alternative—shrugging our shoulders as a fight for commercial AI domination undermines democratic politics—will be much messier and much worse.
This essay was written with Henry Farrell and Nathan Sanders, and previously appeared on Slate.com.
EDITED TO ADD: Linux Weekly News discussion.
Jim Dempsey, one of the workshop organizers, wrote a blog post on the report:
As a first step, our report recommends the inclusion of AI security concerns within the cybersecurity programs of developers and users. The understanding of how to secure AI systems, we concluded, lags far behind their widespread adoption. Many AI products are deployed without institutions fully understanding the security risks they pose. Organizations building or deploying AI models should incorporate AI concerns into their cybersecurity functions using a risk management framework that addresses security throughout the AI system life cycle. It will be necessary to grapple with the ways in which AI vulnerabilities are different from traditional cybersecurity bugs, but the starting point is to assume that AI security is a subset of cybersecurity and to begin applying vulnerability management practices to AI-based features. (Andy Grotto and I have vigorously argued against siloing AI security in its own governance and policy vertical.)
Our report also recommends more collaboration between cybersecurity practitioners, machine learning engineers, and adversarial machine learning researchers. Assessing AI vulnerabilities requires technical expertise that is distinct from the skill set of cybersecurity practitioners, and organizations should be cautioned against repurposing existing security teams without additional training and resources. We also note that AI security researchers and practitioners should consult with those addressing AI bias. AI fairness researchers have extensively studied how poor data, design choices, and risk decisions can produce biased outcomes. Since AI vulnerabilities may be more analogous to algorithmic bias than they are to traditional software vulnerabilities, it is important to cultivate greater engagement between the two communities.
Another major recommendation calls for establishing some form of information sharing among AI developers and users. Right now, even if vulnerabilities are identified or malicious attacks are observed, this information is rarely transmitted to others, whether peer organizations, other companies in the supply chain, end users, or government or civil society observers. Bureaucratic, policy, and cultural barriers currently inhibit such sharing. This means that a compromise will likely remain mostly unnoticed until long after attackers have successfully exploited vulnerabilities. To avoid this outcome, we recommend that organizations developing AI models monitor for potential attacks on AI systems, create—formally or informally—a trusted forum for incident information sharing on a protected basis, and improve transparency.
[2023.04.28] My latest book, A Hacker’s Mind, is filled with stories about the rich and powerful hacking systems, but it was hard to find stories of the hacking by the less powerful. Here’s one I just found. An article on how layoffs at big companies work inadvertently suggests an employee hack to avoid being fired:
…software performs a statistical analysis during terminations to see if certain groups are adversely affected, said such reviews can uncover other problems. On a list of layoff candidates, a company might find it is about to fire inadvertently an employee who previously opened a complaint against a manager—a move that could be seen as retaliation, she said.
So if you’re at a large company and there are rumors of layoffs, go to HR and initiate a complaint against a manager. It’ll protect you from being laid off.
[2023.05.02] NIST has released a draft of Special Publication1800-38A: “Migration to Post-Quantum Cryptography: Preparation for Considering the Implementation and Adoption of Quantum Safe Cryptography.” It’s only four pages long, and it doesn’t have a lot of detail—more “volumes” are coming, with more information—but it’s well worth reading.
We are going to need to migrate to quantum-resistant public-key algorithms, and the sooner we implement key agility the easier it will be to do so.
[2023.05.03] New reporting from Wired reveals that the Department of Justice detected the SolarWinds attack six months before Mandiant detected it in December 2020, but didn’t realize what it detected—and so ignored it.
WIRED can now confirm that the operation was actually discovered by the DOJ six months earlier, in late May 2020—but the scale and significance of the breach wasn’t immediately apparent. Suspicions were triggered when the department detected unusual traffic emanating from one of its servers that was running a trial version of the Orion software suite made by SolarWinds, according to sources familiar with the incident. The software, used by system administrators to manage and configure networks, was communicating externally with an unfamiliar system on the internet. The DOJ asked the security firm Mandiant to help determine whether the server had been hacked. It also engaged Microsoft, though it’s not clear why the software maker was also brought onto the investigation.
Investigators suspected the hackers had breached the DOJ server directly, possibly by exploiting a vulnerability in the Orion software. They reached out to SolarWinds to assist with the inquiry, but the company’s engineers were unable to find a vulnerability in their code. In July 2020, with the mystery still unresolved, communication between investigators and SolarWinds stopped. A month later, the DOJ purchased the Orion system, suggesting that the department was satisfied that there was no further threat posed by the Orion suite, the sources say.
EDITED TO ADD (5/4): More details about the SolarWinds attack from Wired.com.
[2023.05.04] Earlier this week, the Republican National Committee released a video that it claims was “built entirely with AI imagery.” The content of the ad isn’t especially novel—a dystopian vision of America under a second term with President Joe Biden—but the deliberate emphasis on the technology used to create it stands out: It’s a “Daisy” moment for the 2020s.
We should expect more of this kind of thing. The applications of AI to political advertising have not escaped campaigners, who are already “pressure testing” possible uses for the technology. In the 2024 presidential election campaign, you can bank on the appearance of AI-generated personalized fundraising emails, text messages from chatbots urging you to vote, and maybe even some deepfaked campaign avatars. Future candidates could use chatbots trained on data representing their views and personalities to approximate the act of directly connecting with people. Think of it like a whistle-stop tour with an appearance in every living room. Previous technological revolutions—railroad, radio, television, and the World Wide Web—transformed how candidates connect to their constituents, and we should expect the same from generative AI. This isn’t science fiction: The era of AI chatbots standing in as avatars for real, individual people has already begun, as the journalist Casey Newton made clear in a 2016 feature about a woman who used thousands of text messages to create a chatbot replica of her best friend after he died.
The key is interaction. A candidate could use tools enabled by large language models, or LLMs—the technology behind apps such as ChatGPT and the art-making DALL-E—to do micro-polling or message testing, and to solicit perspectives and testimonies from their political audience individually and at scale. The candidates could potentially reach any voter who possesses a smartphone or computer, not just the ones with the disposable income and free time to attend a campaign rally. At its best, AI could be a tool to increase the accessibility of political engagement and ease polarization. At its worst, it could propagate misinformation and increase the risk of voter manipulation. Whatever the case, we know political operatives are using these tools. To reckon with their potential now isn’t buying into the hype—it’s preparing for whatever may come next.
On the positive end, and most profoundly, LLMs could help people think through, refine, or discover their own political ideologies. Research has shown that many voters come to their policy positions reflexively, out of a sense of partisan affiliation. The very act of reflecting on these views through discourse can change, and even depolarize, those views. It can be hard to have reflective policy conversations with an informed, even-keeled human discussion partner when we all live within a highly charged political environment; this is a role almost custom-designed for LLM. In US politics, it is a truism that the most valuable resource in a campaign is time. People are busy and distracted. Campaigns have a limited window to convince and activate voters. Money allows a candidate to purchase time: TV commercials, labor from staffers, and fundraising events to raise even more money. LLMs could provide campaigns with what is essentially a printing press for time.
If you were a political operative, which would you rather do: play a short video on a voter’s TV while they are folding laundry in the next room, or exchange essay-length thoughts with a voter on your candidate’s key issues? A staffer knocking on doors might need to canvass 50 homes over two hours to find one voter willing to have a conversation. OpenAI charges pennies to process about 800 words with its latest GPT-4 model, and that cost could fall dramatically as competitive AIs become available. People seem to enjoy interacting with chatbots; Open’s product reportedly has the fastest-growing user base in the history of consumer apps.
Optimistically, one possible result might be that we’ll get less annoyed with the deluge of political ads if their messaging is more usefully tailored to our interests by AI tools. Though the evidence for microtargeting’s effectiveness is mixed at best, some studies show that targeting the right issues to the right people can persuade voters. Expecting more sophisticated, AI-assisted approaches to be more consistently effective is reasonable. And anything that can prevent us from seeing the same 30-second campaign spot 20 times a day seems like a win.
AI can also help humans effectuate their political interests. In the 2016 US presidential election, primitive chatbots had a role in donor engagement and voter-registration drives: simple messaging tasks such as helping users pre-fill a voter-registration form or reminding them where their polling place is. If it works, the current generation of much more capable chatbots could supercharge small-dollar solicitations and get-out-the-vote campaigns.
And the interactive capability of chatbots could help voters better understand their choices. An AI chatbot could answer questions from the perspective of a candidate about the details of their policy positions most salient to an individual user, or respond to questions about how a candidate’s stance on a national issue translates to a user’s locale. Political organizations could similarly use them to explain complex policy issues, such as those relating to the climate or health care or…anything, really.
Of course, this could also go badly. In the time-honored tradition of demagogues worldwide, the LLM could inconsistently represent the candidate’s views to appeal to the individual proclivities of each voter.
In fact, the fundamentally obsequious nature of the current generation of large language models results in them acting like demagogues. Current LLMs are known to hallucinate—or go entirely off-script—and produce answers that have no basis in reality. These models do not experience emotion in any way, but some research suggests they have a sophisticated ability to assess the emotion and tone of their human users. Although they weren’t trained for this purpose, ChatGPT and its successor, GPT-4, may already be pretty good at assessing some of their users’ traits—say, the likelihood that the author of a text prompt is depressed. Combined with their persuasive capabilities, that means that they could learn to skillfully manipulate the emotions of their human users.
This is not entirely theoretical. A growing body of evidence demonstrates that interacting with AI has a persuasive effect on human users. A study published in February prompted participants to co-write a statement about the benefits of social-media platforms for society with an AI chatbot configured to have varying views on the subject. When researchers surveyed participants after the co-writing experience, those who interacted with a chatbot that expressed that social media is good or bad were far more likely to express the same view than a control group that didn’t interact with an “opinionated language model.”
For the time being, most Americans say they are resistant to trusting AI in sensitive matters such as health care. The same is probably true of politics. If a neighbor volunteering with a campaign persuades you to vote a particular way on a local ballot initiative, you might feel good about that interaction. If a chatbot does the same thing, would you feel the same way? To help voters chart their own course in a world of persuasive AI, we should demand transparency from our candidates. Campaigns should have to clearly disclose when a text agent interacting with a potential voter—through traditional robotexting or the use of the latest AI chatbots—is human or automated.
Though companies such as Meta (Facebook’s parent company) and Alphabet (Google’s) publish libraries of traditional, static political advertising, they do so poorly. These systems would need to be improved and expanded to accommodate user-level differentiation in ad copy to offer serviceable protection against misuse.
A public, anonymized log of chatbot conversations could help hold candidates’ AI representatives accountable for shifting statements and digital pandering. Candidates who use chatbots to engage voters may not want to make all transcripts of those conversations public, but their users could easily choose to share them. So far, there is no shortage of people eager to share their chat transcripts, and in fact, an online database exists of nearly 200,000 of them. In the recent past, Mozilla has galvanized users to opt into sharing their web data to study online misinformation.
We also need stronger nationwide protections on data privacy, as well as the ability to opt out of targeted advertising, to protect us from the potential excesses of this kind of marketing. No one should be forcibly subjected to political advertising, LLM-generated or not, on the basis of their Internet searches regarding private matters such as medical issues. In February, the European Parliament voted to limit political-ad targeting to only basic information, such as language and general location, within two months of an election. This stands in stark contrast to the US, which has for years failed to enact federal data-privacy regulations. Though the 2018 revelation of the Cambridge Analytica scandal led to billions of dollars in fines and settlements against Facebook, it has so far resulted in no substantial legislative action.
Transparency requirements like these are a first step toward oversight of future AI-assisted campaigns. Although we should aspire to more robust legal controls on campaign uses of AI, it seems implausible that these will be adopted in advance of the fast-approaching 2024 general presidential election.
Credit the RNC, at least, with disclosing that their recent ad was AI-generated—a transparent attempt at publicity still counts as transparency. But what will we do if the next viral AI-generated ad tries to pass as something more conventional?
As we are all being exposed to these rapidly evolving technologies for the first time and trying to understand their potential uses and effects, let’s push for the kind of basic transparency protection that will allow us to know what we’re dealing with.
This essay was written with Nathan Sanders, and previously appeared on the Atlantic.
EDITED TO ADD (5/12): Better article on the “daisy” ad.
The DEF CON event will rely on an evaluation platform developed by Scale AI, a California company that produces training for AI applications. Participants will be given laptops to use to attack the models. Any bugs discovered will be disclosed using industry-standard responsible disclosure practices.
In the early stages of the war in Ukraine in 2022, PIPEDREAM, a known malware was quietly on the brink of wiping out a handful of critical U.S. electric and liquid natural gas sites. PIPEDREAM is an attack toolkit with unmatched and unprecedented capabilities developed for use against industrial control systems (ICSs).
The malware was built to manipulate the network communication protocols used by programmable logic controllers (PLCs) leveraged by two critical producers of PLCs for ICSs within the critical infrastructure sector, Schneider Electric and OMRON.
[2023.05.10] Reuters is reporting that the FBI “had identified and disabled malware wielded by Russia’s FSB security service against an undisclosed number of American computers, a move they hoped would deal a death blow to one of Russia’s leading cyber spying programs.”
The headline says that the FBI “sabotaged” the malware, which seems to be wrong.
Presumably we will learn more soon.
EDITED TO ADD: New York Times story.
EDITED TO ADD: Maybe “sabotaged” is the right word. The FBI hacked the malware so that it disabled itself.
Despite the bravado of its developers, Snake is among the most sophisticated pieces of malware ever found, the FBI said. The modular design, custom encryption layers, and high-caliber quality of the code base have made it hard if not impossible for antivirus software to detect. As FBI agents continued to monitor Snake, however, they slowly uncovered some surprising weaknesses. For one, there was a critical cryptographic key with a prime length of just 128 bits, making it vulnerable to factoring attacks that expose the secret key. This weak key was used in Diffie-Hellman key exchanges that allowed each infected machine to have a unique key when communicating with another machine.
[2023.05.11] We will all soon get into the habit of using AI tools for help with everyday problems and tasks. We should get in the habit of questioning the motives, incentives, and capabilities behind them, too.
Imagine you’re using an AI chatbot to plan a vacation. Did it suggest a particular resort because it knows your preferences, or because the company is getting a kickback from the hotel chain? Later, when you’re using another AI chatbot to learn about a complex economic issue, is the chatbot reflecting your politics or the politics of the company that trained it?
For AI to truly be our assistant, it needs to be trustworthy. For it to be trustworthy, it must be under our control; it can’t be working behind the scenes for some tech monopoly. This means, at a minimum, the technology needs to be transparent. And we all need to understand how it works, at least a little bit.
Amid the myriad warnings about creepy risks to well-being, threats to democracy, and even existential doom that have accompanied stunning recent developments in artificial intelligence (AI)—and large language models (LLMs) like ChatGPT and GPT-4—one optimistic vision is abundantly clear: this technology is useful. It can help you find information, express your thoughts, correct errors in your writing, and much more. If we can navigate the pitfalls, its assistive benefit to humanity could be epoch-defining. But we’re not there yet.
Let’s pause for a moment and imagine the possibilities of a trusted AI assistant. It could write the first draft of anything: emails, reports, essays, even wedding vows. You would have to give it background information and edit its output, of course, but that draft would be written by a model trained on your personal beliefs, knowledge, and style. It could act as your tutor, answering questions interactively on topics you want to learn about—in the manner that suits you best and taking into account what you already know. It could assist you in planning, organizing, and communicating: again, based on your personal preferences. It could advocate on your behalf with third parties: either other humans or other bots. And it could moderate conversations on social media for you, flagging misinformation, removing hate or trolling, translating for speakers of different languages, and keeping discussions on topic; or even mediate conversations in physical spaces, interacting through speech recognition and synthesis capabilities.
Today’s AIs aren’t up for the task. The problem isn’t the technology—that’s advancing faster than even the experts had guessed—it’s who owns it. Today’s AIs are primarily created and run by large technology companies, for their benefit and profit. Sometimes we are permitted to interact with the chatbots, but they’re never truly ours. That’s a conflict of interest, and one that destroys trust.
The transition from awe and eager utilization to suspicion to disillusionment is a well worn one in the technology sector. Twenty years ago, Google’s search engine rapidly rose to monopolistic dominance because of its transformative information retrieval capability. Over time, the company’s dependence on revenue from search advertising led them to degrade that capability. Today, many observers look forward to the death of the search paradigm entirely. Amazon has walked the same path, from honest marketplace to one riddled with lousy products whose vendors have paid to have the company show them to you. We can do better than this. If each of us are going to have an AI assistant helping us with essential activities daily and even advocating on our behalf, we each need to know that it has our interests in mind. Building trustworthy AI will require systemic change.
First, a trustworthy AI system must be controllable by the user. That means that the model should be able to run on a user’s owned electronic devices (perhaps in a simplified form) or within a cloud service that they control. It should show the user how it responds to them, such as when it makes queries to search the web or external services, when it directs other software to do things like sending an email on a user’s behalf, or modifies the user’s prompts to better express what the company that made it thinks the user wants. It should be able to explain its reasoning to users and cite its sources. These requirements are all well within the technical capabilities of AI systems.
Furthermore, users should be in control of the data used to train and fine-tune the AI system. When modern LLMs are built, they are first trained on massive, generic corpora of textual data typically sourced from across the Internet. Many systems go a step further by fine-tuning on more specific datasets purpose built for a narrow application, such as speaking in the language of a medical doctor, or mimicking the manner and style of their individual user. In the near future, corporate AIs will be routinely fed your data, probably without your awareness or your consent. Any trustworthy AI system should transparently allow users to control what data it uses.
Many of us would welcome an AI-assisted writing application fine tuned with knowledge of which edits we have accepted in the past and which we did not. We would be more skeptical of a chatbot knowledgeable about which of their search results led to purchases and which did not.
You should also be informed of what an AI system can do on your behalf. Can it access other apps on your phone, and the data stored with them? Can it retrieve information from external sources, mixing your inputs with details from other places you may or may not trust? Can it send a message in your name (hopefully based on your input)? Weighing these types of risks and benefits will become an inherent part of our daily lives as AI-assistive tools become integrated with everything we do.
Realistically, we should all be preparing for a world where AI is not trustworthy. Because AI tools can be so incredibly useful, they will increasingly pervade our lives, whether we trust them or not. Being a digital citizen of the next quarter of the twenty-first century will require learning the basic ins and outs of LLMs so that you can assess their risks and limitations for a given use case. This will better prepare you to take advantage of AI tools, rather than be taken advantage by them.
In the world’s first few months of widespread use of models like ChatGPT, we’ve learned a lot about how AI creates risks for users. Everyone has heard by now that LLMs “hallucinate,” meaning that they make up “facts” in their outputs, because their predictive text generation systems are not constrained to fact check their own emanations. Many users learned in March that information they submit as prompts to systems like ChatGPT may not be kept private after a bug revealed users’ chats. Your chat histories are stored in systems that may be insecure.
Researchers have found numerous clever ways to trick chatbots into breaking their safety controls; these work largely because many of the “rules” applied to these systems are soft, like instructions given to a person, rather than hard, like coded limitations on a product’s functions. It’s as if we are trying to keep AI safe by asking it nicely to drive carefully, a hopeful instruction, rather than taking away its keys and placing definite constraints on its abilities.
These risks will grow as companies grant chatbot systems more capabilities. OpenAI is providing developers wide access to build tools on top of GPT: tools that give their AI systems access to your email, to your personal account information on websites, and to computer code. While OpenAI is applying safety protocols to these integrations, it’s not hard to imagine those being relaxed in a drive to make the tools more useful. It seems likewise inevitable that other companies will come along with less bashful strategies for securing AI market share.
Just like with any human, building trust with an AI will be hard won through interaction over time. We will need to test these systems in different contexts, observe their behavior, and build a mental model for how they will respond to our actions. Building trust in that way is only possible if these systems are transparent about their capabilities, what inputs they use and when they will share them, and whose interests they are evolving to represent.
This essay was written with Nathan Sanders, and previously appeared on Gizmodo.com.
The question we should be asking is: as A.I. becomes more powerful and flexible, is there any way to keep it from being another version of McKinsey? The question is worth considering across different meanings of the term “A.I.” If you think of A.I. as a broad set of technologies being marketed to companies to help them cut their costs, the question becomes: how do we keep those technologies from working as “capital’s willing executioners”? Alternatively, if you imagine A.I. as a semi-autonomous software program that solves problems that humans ask it to solve, the question is then: how do we prevent that software from assisting corporations in ways that make people’s lives worse? Suppose you’ve built a semi-autonomous A.I. that’s entirely obedient to humans—one that repeatedly checks to make sure it hasn’t misinterpreted the instructions it has received. This is the dream of many A.I. researchers. Yet such software could easily still cause as much harm as McKinsey has.
Note that you cannot simply say that you will build A.I. that only offers pro-social solutions to the problems you ask it to solve. That’s the equivalent of saying that you can defuse the threat of McKinsey by starting a consulting firm that only offers such solutions. The reality is that Fortune 100 companies will hire McKinsey instead of your pro-social firm, because McKinsey’s solutions will increase shareholder value more than your firm’s solutions will. It will always be possible to build A.I. that pursues shareholder value above all else, and most companies will prefer to use that A.I. instead of one constrained by your principles.
EDITED TO ADD: Ted Chiang’s previous essay, “ChatGPT Is a Blurry JPEG of the Web” is also worth reading.
[2023.05.14] This is a current list of where and when I am scheduled to speak:
- 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.