Entries Tagged "economics of security"

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What Will It Take?

What will it take for policy makers to take cybersecurity seriously? Not minimal-change seriously. Not here-and-there seriously. But really seriously. What will it take for policy makers to take cybersecurity seriously enough to enact substantive legislative changes that would address the problems? It’s not enough for the average person to be afraid of cyberattacks. They need to know that there are engineering fixes—and that’s something we can provide.

For decades, I have been waiting for the “big enough” incident that would finally do it. In 2015, Chinese military hackers hacked the Office of Personal Management and made off with the highly personal information of about 22 million Americans who had security clearances. In 2016, the Mirai botnet leveraged millions of Internet-of-Things devices with default admin passwords to launch a denial-of-service attack that disabled major Internet platforms and services in both North America and Europe. In 2017, hackers—years later we learned that it was the Chinese military—hacked the credit bureau Equifax and stole the personal information of 147 million Americans. In recent years, ransomware attacks have knocked hospitals offline, and many articles have been written about Russia inside the U.S. power grid. And last year, the Russian SVR hacked thousands of sensitive networks inside civilian critical infrastructure worldwide in what we’re now calling Sunburst (and used to call SolarWinds).

Those are all major incidents to security people, but think about them from the perspective of the average person. Even the most spectacular failures don’t affect 99.9% of the country. Why should anyone care if the Chinese have his or her credit records? Or if the Russians are stealing data from some government network? Few of us have been directly affected by ransomware, and a temporary Internet outage is just temporary.

Cybersecurity has never been a campaign issue. It isn’t a topic that shows up in political debates. (There was one question in a 2016 Clinton–Trump debate, but the response was predictably unsubstantive.) This just isn’t an issue that most people prioritize, or even have an opinion on.

So, what will it take? Many of my colleagues believe that it will have to be something with extreme emotional intensity—sensational, vivid, salient—that results in large-scale loss of life or property damage. A successful attack that actually poisons a water supply, as someone tried to do in January by raising the levels of lye at a Florida water-treatment plant. (That one was caught early.) Or an attack that disables Internet-connected cars at speed, something that was demonstrated by researchers in 2014. Or an attack on the power grid, similar to what Russia did to the Ukraine in 2015 and 2016. Will it take gas tanks exploding and planes falling out of the sky for the average person to read about the casualties and think “that could have been me”?

Here’s the real problem. For the average nonexpert—and in this category I include every lawmaker—to push for change, they not only need to believe that the present situation is intolerable, they also need to believe that an alternative is possible. Real legislative change requires a belief that the never-ending stream of hacks and attacks is not inevitable, that we can do better. And that will require creating working examples of secure, dependable, resilient systems.

Providing alternatives is how engineers help facilitate social change. We could never have eliminated sales of tungsten-filament household light bulbs if fluorescent and LED replacements hadn’t become available. Reducing the use of fossil fuel for electricity generation requires working wind turbines and cost-effective solar cells.

We need to demonstrate that it’s possible to build systems that can defend themselves against hackers, criminals, and national intelligence agencies; secure Internet-of-Things systems; and systems that can reestablish security after a breach. We need to prove that hacks aren’t inevitable, and that our vulnerability is a choice. Only then can someone decide to choose differently. When people die in a cyberattack and everyone asks “What can be done?” we need to have something to tell them.

We don’t yet have the technology to build a truly safe, secure, and resilient Internet and the computers that connect to it. Yes, we have lots of security technologies. We have older secure systems—anyone still remember Apollo’s DomainOS and MULTICS?—that lost out in a market that didn’t reward security. We have newer research ideas and products that aren’t successful because the market still doesn’t reward security. We have even newer research ideas that won’t be deployed, again, because the market still prefers convenience over security.

What I am proposing is something more holistic, an engineering research task on a par with the Internet itself. The Internet was designed and built to answer this question: Can we build a reliable network out of unreliable parts in an unreliable world? It turned out the answer was yes, and the Internet was the result. I am asking a similar research question: Can we build a secure network out of insecure parts in an insecure world? The answer isn’t obviously yes, but it isn’t obviously no, either.

While any successful demonstration will include many of the security technologies we know and wish would see wider use, it’s much more than that. Creating a secure Internet ecosystem goes beyond old-school engineering to encompass the social sciences. It will include significant economic, institutional, and psychological considerations that just weren’t present in the first few decades of Internet research.

Cybersecurity isn’t going to get better until the economic incentives change, and that’s not going to change until the political incentives change. The political incentives won’t change until there is political liability that comes from voter demands. Those demands aren’t going to be solely the results of insecurity. They will also be the result of believing that there’s a better alternative. It is our task to research, design, build, test, and field that better alternative—even though the market couldn’t care less right now.

This essay originally appeared in the May/June 2021 issue of IEEE Security & Privacy. I forgot to publish it here.

Posted on February 14, 2023 at 7:06 AM • View Comments

Hacking the Tax Code

The tax code isn’t software. It doesn’t run on a computer. But it’s still code. It’s a series of algorithms that takes an input—financial information for the year—and produces an output: the amount of tax owed. It’s incredibly complex code; there are a bazillion details and exceptions and special cases. It consists of government laws, rulings from the tax authorities, judicial decisions, and legal opinions.

Like computer code, the tax code has bugs. They might be mistakes in how the tax laws were written. They might be mistakes in how the tax code is interpreted, oversights in how parts of the law were conceived, or unintended omissions of some sort or another. They might arise from the exponentially huge number of ways different parts of the tax code interact.

A recent example comes from the 2017 Tax Cuts and Jobs Act. That law was drafted in both haste and secret, and quickly passed without any time for review—or even proofreading. One of the things in it was a typo that accidentally categorized military death benefits as earned income. The practical effect of that mistake is that surviving family members were hit with surprise tax bills of US$10,000 or more.

That’s a bug, but not a vulnerability. An example of a vulnerability is the “Double Irish with a Dutch Sandwich.” It arises from the interactions of tax laws in multiple countries, and it’s how companies like Google and Apple have avoided paying U.S. taxes despite being U.S. companies. Estimates are that U.S. companies avoided paying nearly US$200 billion in taxes in 2017 alone.

In the tax world, vulnerabilities are called loopholes. Exploits are called tax avoidance strategies. And there are thousands of black-hat researchers who examine every line of the tax code looking for exploitable vulnerabilities—tax attorneys and tax accountants.

Some vulnerabilities are deliberately created. Lobbyists are constantly trying to insert this or that provision into the tax code that benefits their clients financially. That same 2017 U.S. tax law included a special tax break for oil and gas investment partnerships, a special exemption that ensures that fewer than 1 in 1,000 estates will have to pay estate tax, and language specifically expanding a pass-through loophole that industry uses to incorporate companies offshore and avoid U.S. taxes. That’s not hacking the tax code. It’s hacking the processes that create them: the legislative process that creates tax law.

We know the processes to use to fix vulnerabilities in computer code. Before the code is finished, we can employ some sort of secure development processes, with automatic bug-finding tools and maybe source code audits. After the code is deployed, we might rely on vulnerability finding by the security community, perhaps bug bounties—and most of all, quick patching when vulnerabilities are discovered.

What does it mean to “patch” the tax code? Passing any tax legislation is a big deal, especially in the United States where the issue is so partisan and contentious. (That 2017 earned income tax bug for military families hasn’t yet been fixed. And that’s an easy one; everyone acknowledges it was a mistake.) We don’t have the ability to patch tax code with anywhere near the same agility that we have to patch software.

We can patch some vulnerabilities, though. The other way tax code is modified is by IRS and judicial rulings. The 2017 tax law capped income tax deductions for property taxes. This provision didn’t come into force in 2018, so someone came up with the clever hack to prepay 2018 property taxes in 2017. Just before the end of the year, the IRS ruled about when that was legal and when it wasn’t. Short answer: most of the time, it wasn’t.

There’s another option: that the vulnerability isn’t patched and isn’t explicitly approved, and slowly becomes part of the normal way of doing things. Lots of tax loopholes end up like this. Sometimes they’re even given retroactive legality by the IRS or Congress after a constituency and lobbying effort gets behind them. This process is how systems evolve. A hack subverts the intent of a system. Whatever governing system has jurisdiction either blocks the hack or allows it—or does nothing and the hack becomes the new normal.

Here’s my question: what happens when artificial intelligence and machine learning (ML) gets hold of this problem? We already have ML systems that find software vulnerabilities. What happens when you feed a ML system the entire U.S. tax code and tell it to figure out all of the ways to minimize the amount of tax owed? Or, in the case of a multinational corporation, to feed it the entire planet’s tax codes? What sort of vulnerabilities would it find? And how many? Dozens or millions?

In 2015, Volkswagen was caught cheating on emissions control tests. It didn’t forge test results; it got the cars’ computers to cheat for them. Engineers programmed the software in the car’s onboard computer to detect when the car was undergoing an emissions test. The computer then activated the car’s emissions-curbing systems, but only for the duration of the test. The result was that the cars had much better performance on the road at the cost of producing more pollution.

ML will result in lots of hacks like this. They’ll be more subtle. They’ll be even harder to discover. It’s because of the way ML systems optimize themselves, and because their specific optimizations can be impossible for us humans to understand. Their human programmers won’t even know what’s going on.

Any good ML system will naturally find and exploit hacks. This is because their only constraints are the rules of the system. If there are problems, inconsistencies, or loopholes in the rules, and if those properties lead to a “better” solution as defined by the program, then those systems will find them. The challenge is that you have to define the system’s goals completely and precisely, and that that’s impossible.

The tax code can be hacked. Financial markets regulations can be hacked. The market economy, democracy itself, and our cognitive systems can all be hacked. Tasking a ML system to find new hacks against any of these is still science fiction, but it’s not stupid science fiction. And ML will drastically change how we need to think about policy, law, and government. Now’s the time to figure out how.

This essay originally appeared in the September/October 2020 issue of IEEE Security & Privacy. I wrote it when I started writing my latest book, but never published it here.

Posted on February 10, 2023 at 6:24 AM • View Comments

SolarWinds and Market Incentives

In early 2021, IEEE Security and Privacy asked a number of board members for brief perspectives on the SolarWinds incident while it was still breaking news. This was my response.

The penetration of government and corporate networks worldwide is the result of inadequate cyberdefenses across the board. The lessons are many, but I want to focus on one important one we’ve learned: the software that’s managing our critical networks isn’t secure, and that’s because the market doesn’t reward that security.

SolarWinds is a perfect example. The company was the initial infection vector for much of the operation. Its trusted position inside so many critical networks made it a perfect target for a supply-chain attack, and its shoddy security practices made it an easy target.

Why did SolarWinds have such bad security? The answer is because it was more profitable. The company is owned by Thoma Bravo partners, a private-equity firm known for radical cost-cutting in the name of short-term profit. Under CEO Kevin Thompson, the company underspent on security even as it outsourced software development. The New York Times reports that the company’s cybersecurity advisor quit after his “basic recommendations were ignored.” In a very real sense, SolarWinds profited because it secretly shifted a whole bunch of risk to its customers: the US government, IT companies, and others.

This problem isn’t new, and, while it’s exacerbated by the private-equity funding model, it’s not unique to it. In general, the market doesn’t reward safety and security—especially when the effects of ignoring those things are long term and diffuse. The market rewards short-term profits at the expense of safety and security. (Watch and see whether SolarWinds suffers any long-term effects from this hack, or whether Thoma Bravo’s bet that it could profit by selling an insecure product was a good one.)

The solution here is twofold. The first is to improve government software procurement. Software is now critical to national security. Any system of procuring that software needs to evaluate the security of the software and the security practices of the company, in detail, to ensure that they are sufficient to meet the security needs of the network they’re being installed in. If these evaluations are made public, along with the list of companies that meet them, all network buyers can benefit from them. It’s a win for everybody.

But that isn’t enough; we need a second part. The only way to force companies to provide safety and security features for customers is through regulation. This is true whether we want seat belts in our cars, basic food safety at our restaurants, pajamas that don’t catch on fire, or home routers that aren’t vulnerable to cyberattack. The government needs to set minimum security standards for software that’s used in critical network applications, just as it sets software standards for avionics.

Without these two measures, it’s just too easy for companies to act like SolarWinds: save money by skimping on safety and security and hope for the best in the long term. That’s the rational thing for companies to do in an unregulated market, and the only way to change that is to change the economic incentives.

This essay originally appeared in the March/April 2021 issue of IEEE Security & Privacy.” I forgot to publish it here.

Posted on February 8, 2023 at 6:46 AM • View Comments

An Examination of the Bug Bounty Marketplace

Here’s a fascinating report: “Bounty Everything: Hackers and the Making of the Global Bug Marketplace.” From a summary:

…researchers Ryan Ellis and Yuan Stevens provide a window into the working lives of hackers who participate in “bug bounty” programs­—programs that hire hackers to discover and report bugs or other vulnerabilities in their systems. This report illuminates the risks and insecurities for hackers as gig workers, and how bounty programs rely on vulnerable workers to fix their vulnerable systems.

Ellis and Stevens’s research offers a historical overview of bounty programs and an analysis of contemporary bug bounty platforms—­the new intermediaries that now structure the vast majority of bounty work. The report draws directly from interviews with hackers, who recount that bounty programs seem willing to integrate a diverse workforce in their practices, but only on terms that deny them the job security and access enjoyed by core security workforces. These inequities go far beyond the difference experienced by temporary and permanent employees at companies such as Google and Apple, contend the authors. The global bug bounty workforce is doing piecework—they are paid for each bug, and the conditions under which a bug is paid vary greatly from one company to the next.

Posted on January 17, 2022 at 6:16 AM • View Comments

Illegal Content and the Blockchain

Security researchers have recently discovered a botnet with a novel defense against takedowns. Normally, authorities can disable a botnet by taking over its command-and-control server. With nowhere to go for instructions, the botnet is rendered useless. But over the years, botnet designers have come up with ways to make this counterattack harder. Now the content-delivery network Akamai has reported on a new method: a botnet that uses the Bitcoin blockchain ledger. Since the blockchain is globally accessible and hard to take down, the botnet’s operators appear to be safe.

It’s best to avoid explaining the mathematics of Bitcoin’s blockchain, but to understand the colossal implications here, you need to understand one concept. Blockchains are a type of “distributed ledger”: a record of all transactions since the beginning, and everyone using the blockchain needs to have access to—and reference—a copy of it. What if someone puts illegal material in the blockchain? Either everyone has a copy of it, or the blockchain’s security fails.

To be fair, not absolutely everyone who uses a blockchain holds a copy of the entire ledger. Many who buy cryptocurrencies like Bitcoin and Ethereum don’t bother using the ledger to verify their purchase. Many don’t actually hold the currency outright, and instead trust an exchange to do the transactions and hold the coins. But people need to continually verify the blockchain’s history on the ledger for the system to be secure. If they stopped, then it would be trivial to forge coins. That’s how the system works.

Some years ago, people started noticing all sorts of things embedded in the Bitcoin blockchain. There are digital images, including one of Nelson Mandela. There’s the Bitcoin logo, and the original paper describing Bitcoin by its alleged founder, the pseudonymous Satoshi Nakamoto. There are advertisements, and several prayers. There’s even illegal pornography and leaked classified documents. All of these were put in by anonymous Bitcoin users. But none of this, so far, appears to seriously threaten those in power in governments and corporations. Once someone adds something to the Bitcoin ledger, it becomes sacrosanct. Removing something requires a fork of the blockchain, in which Bitcoin fragments into multiple parallel cryptocurrencies (and associated blockchains). Forks happen, rarely, but never yet because of legal coercion. And repeated forking would destroy Bitcoin’s stature as a stable(ish) currency.

The botnet’s designers are using this idea to create an unblockable means of coordination, but the implications are much greater. Imagine someone using this idea to evade government censorship. Most Bitcoin mining happens in China. What if someone added a bunch of Chinese-censored Falun Gong texts to the blockchain?<

What if someone added a type of political speech that Singapore routinely censors? Or cartoons that Disney holds the copyright to?

In Bitcoin’s and most other public blockchains there are no central, trusted authorities. Anyone in the world can perform transactions or become a miner. Everyone is equal to the extent that they have the hardware and electricity to perform cryptographic computations.

This openness is also a vulnerability, one that opens the door to asymmetric threats and small-time malicious actors. Anyone can put information in the one and only Bitcoin blockchain. Again, that’s how the system works.

Over the last three decades, the world has witnessed the power of open networks: blockchains, social media, the very web itself. What makes them so powerful is that their value is related not just to the number of users, but the number of potential links between users. This is Metcalfe’s law—value in a network is quadratic, not linear, in the number of users—and every open network since has followed its prophecy.

As Bitcoin has grown, its monetary value has skyrocketed, even if its uses remain unclear. With no barrier to entry, the blockchain space has been a Wild West of innovation and lawlessness. But today, many prominent advocates suggest Bitcoin should become a global, universal currency. In this context, asymmetric threats like embedded illegal data become a major challenge.

The philosophy behind Bitcoin traces to the earliest days of the open internet. Articulated in John Perry Barlow’s 1996 Declaration of the Independence of Cyberspace, it was and is the ethos of tech startups: Code is more trustworthy than institutions. Information is meant to be free, and nobody has the right—and should not have the ability—to control it.

But information must reside somewhere. Code is written by and for people, stored on computers located within countries, and embedded within the institutions and societies we have created. To trust information is to trust its chain of custody and the social context it comes from. Neither code nor information is value-neutral, nor ever free of human context.

Today, Barlow’s vision is a mere shadow; every society controls the information its people can access. Some of this control is through overt censorship, as China controls information about Taiwan, Tiananmen Square, and the Uyghurs. Some of this is through civil laws designed by the powerful for their benefit, as with Disney and US copyright law, or UK libel law.

Bitcoin and blockchains like it are on a collision course with these laws. What happens when the interests of the powerful, with the law on their side, are pitted against an open blockchain? Let’s imagine how our various scenarios might play out.

China first: In response to Falun Gong texts in the blockchain, the People’s Republic decrees that any miners processing blocks with banned content will be taken offline—their IPs will be blacklisted. This causes a hard fork of the blockchain at the point just before the banned content. China might do this under the guise of a “patriotic” messaging campaign, publicly stating that it’s merely maintaining financial sovereignty from Western banks. Then it uses paid influencers and moderators on social media to pump the China Bitcoin fork, through both partisan comments and transactions. Two distinct forks would soon emerge, one behind China’s Great Firewall and one outside. Other countries with similar governmental and media ecosystems—Russia, Singapore, Myanmar—might consider following suit, creating multiple national Bitcoin forks. These would operate independently, under mandates to censor unacceptable transactions from then on.

Disney’s approach would play out differently. Imagine the company announces it will sue any ISP that hosts copyrighted content, starting with networks hosting the biggest miners. (Disney has sued to enforce its intellectual property rights in China before.) After some legal pressure, the networks cut the miners off. The miners reestablish themselves on another network, but Disney keeps the pressure on. Eventually miners get pushed further and further off of mainstream network providers, and resort to tunneling their traffic through an anonymity service like Tor. That causes a major slowdown in the already slow (because of the mathematics) Bitcoin network. Disney might issue takedown requests for Tor exit nodes, causing the network to slow to a crawl. It could persist like this for a long time without a fork. Or the slowdown could cause people to jump ship, either by forking Bitcoin or switching to another cryptocurrency without the copyrighted content.

And then there’s illegal pornographic content and leaked classified data. These have been on the Bitcoin blockchain for over five years, and nothing has been done about it. Just like the botnet example, it may be that these do not threaten existing power structures enough to warrant takedowns. This could easily change if Bitcoin becomes a popular way to share child sexual abuse material. Simply having these illegal images on your hard drive is a felony, which could have significant repercussions for anyone involved in Bitcoin.

Whichever scenario plays out, this may be the Achilles heel of Bitcoin as a global currency.

If an open network such as a blockchain were threatened by a powerful organization—China’s censors, Disney’s lawyers, or the FBI trying to take down a more dangerous botnet—it could fragment into multiple networks. That’s not just a nuisance, but an existential risk to Bitcoin.

Suppose Bitcoin were fragmented into 10 smaller blockchains, perhaps by geography: one in China, another in the US, and so on. These fragments might retain their original users, and by ordinary logic, nothing would have changed. But Metcalfe’s law implies that the overall value of these blockchain fragments combined would be a mere tenth of the original. That is because the value of an open network relates to how many others you can communicate with—and, in a blockchain, transact with. Since the security of bitcoin currency is achieved through expensive computations, fragmented blockchains are also easier to attack in a conventional manner—through a 51 percent attack—by an organized attacker. This is especially the case if the smaller blockchains all use the same hash function, as they would here.

Traditional currencies are generally not vulnerable to these sorts of asymmetric threats. There are no viable small-scale attacks against the US dollar, or almost any other fiat currency. The institutions and beliefs that give money its value are deep-seated, despite instances of currency hyperinflation.

The only notable attacks against fiat currencies are in the form of counterfeiting. Even in the past, when counterfeit bills were common, attacks could be thwarted. Counterfeiters require specialized equipment and are vulnerable to law enforcement discovery and arrest. Furthermore, most money today—even if it’s nominally in a fiat currency—doesn’t exist in paper form.

Bitcoin attracted a following for its openness and immunity from government control. Its goal is to create a world that replaces cultural power with cryptographic power: verification in code, not trust in people. But there is no such world. And today, that feature is a vulnerability. We really don’t know what will happen when the human systems of trust come into conflict with the trustless verification that make blockchain currencies unique. Just last week we saw this exact attack on smaller blockchains—not Bitcoin yet. We are watching a public socio-technical experiment in the making, and we will witness its success or failure in the not-too-distant future.

This essay was written with Barath Raghavan, and previously appeared on Wired.com.

EDITED TO ADD (4/14): A research paper on erasing data from Bitcoin blockchain.

Posted on March 17, 2021 at 6:10 AM • View Comments

Should There Be Limits on Persuasive Technologies?

Persuasion is as old as our species. Both democracy and the market economy depend on it. Politicians persuade citizens to vote for them, or to support different policy positions. Businesses persuade consumers to buy their products or services. We all persuade our friends to accept our choice of restaurant, movie, and so on. It’s essential to society; we couldn’t get large groups of people to work together without it. But as with many things, technology is fundamentally changing the nature of persuasion. And society needs to adapt its rules of persuasion or suffer the consequences.

Democratic societies, in particular, are in dire need of a frank conversation about the role persuasion plays in them and how technologies are enabling powerful interests to target audiences. In a society where public opinion is a ruling force, there is always a risk of it being mobilized for ill purposes—­such as provoking fear to encourage one group to hate another in a bid to win office, or targeting personal vulnerabilities to push products that might not benefit the consumer.

In this regard, the United States, already extremely polarized, sits on a precipice.

There have long been rules around persuasion. The US Federal Trade Commission enforces laws that claims about products “must be truthful, not misleading, and, when appropriate, backed by scientific evidence.” Political advertisers must identify themselves in television ads. If someone abuses a position of power to force another person into a contract, undue influence can be argued to nullify that agreement. Yet there is more to persuasion than the truth, transparency, or simply applying pressure.

Persuasion also involves psychology, and that has been far harder to regulate. Using psychology to persuade people is not new. Edward Bernays, a pioneer of public relations and nephew to Sigmund Freud, made a marketing practice of appealing to the ego. His approach was to tie consumption to a person’s sense of self. In his 1928 book Propaganda, Bernays advocated engineering events to persuade target audiences as desired. In one famous stunt, he hired women to smoke cigarettes while taking part in the 1929 New York City Easter Sunday parade, causing a scandal while linking smoking with the emancipation of women. The tobacco industry would continue to market lifestyle in selling cigarettes into the 1960s.

Emotional appeals have likewise long been a facet of political campaigns. In the 1860 US presidential election, Southern politicians and newspaper editors spread fears of what a “Black Republican” win would mean, painting horrific pictures of what the emancipation of slaves would do to the country. In the 2020 US presidential election, modern-day Republicans used Cuban Americans’ fears of socialism in ads on Spanish-language radio and messaging on social media. Because of the emotions involved, many voters believed the campaigns enough to let them influence their decisions.

The Internet has enabled new technologies of persuasion to go even further. Those seeking to influence others can collect and use data about targeted audiences to create personalized messaging. Tracking the websites a person visits, the searches they make online, and what they engage with on social media, persuasion technologies enable those who have access to such tools to better understand audiences and deliver more tailored messaging where audiences are likely to see it most. This information can be combined with data about other activities, such as offline shopping habits, the places a person visits, and the insurance they buy, to create a profile of them that can be used to develop persuasive messaging that is aimed at provoking a specific response.

Our senses of self, meanwhile, are increasingly shaped by our interaction with technology. The same digital environment where we read, search, and converse with our intimates enables marketers to take that data and turn it back on us. A modern day Bernays no longer needs to ferret out the social causes that might inspire you or entice you­—you’ve likely already shared that by your online behavior.

Some marketers posit that women feel less attractive on Mondays, particularly first thing in the morning—­and therefore that’s the best time to advertise cosmetics to them. The New York Times once experimented by predicting the moods of readers based on article content to better target ads, enabling marketers to find audiences when they were sad or fearful. Some music streaming platforms encourage users to disclose their current moods, which helps advertisers target subscribers based on their emotional states.

The phones in our pockets provide marketers with our location in real time, helping deliver geographically relevant ads, such as propaganda to those attending a political rally. This always-on digital experience enables marketers to know what we are doing­—and when, where, and how we might be feeling at that moment.

All of this is not intended to be alarmist. It is important not to overstate the effectiveness of persuasive technologies. But while many of them are more smoke and mirrors than reality, it is likely that they will only improve over time. The technology already exists to help predict moods of some target audiences, pinpoint their location at any given time, and deliver fairly tailored and timely messaging. How far does that ability need to go before it erodes the autonomy of those targeted to make decisions of their own free will?

Right now, there are few legal or even moral limits on persuasion­—and few answers regarding the effectiveness of such technologies. Before it is too late, the world needs to consider what is acceptable and what is over the line.

For example, it’s been long known that people are more receptive to advertisements made with people who look like them: in race, ethnicity, age, gender. Ads have long been modified to suit the general demographic of the television show or magazine they appear in. But we can take this further. The technology exists to take your likeness and morph it with a face that is demographically similar to you. The result is a face that looks like you, but that you don’t recognize. If that turns out to be more persuasive than coarse demographic targeting, is that okay?

Another example: Instead of just advertising to you when they detect that you are vulnerable, what if advertisers craft advertisements that deliberately manipulate your mood? In some ways, being able to place ads alongside content that is likely to provoke a certain emotional response enables advertisers to do this already. The only difference is that the media outlet claims it isn’t crafting the content to deliberately achieve this. But is it acceptable to actively prime a target audience and then to deliver persuasive messaging that fits the mood?

Further, emotion-based decision-making is not the rational type of slow thinking that ought to inform important civic choices such as voting. In fact, emotional thinking threatens to undermine the very legitimacy of the system, as voters are essentially provoked to move in whatever direction someone with power and money wants. Given the pervasiveness of digital technologies, and the often instant, reactive responses people have to them, how much emotion ought to be allowed in persuasive technologies? Is there a line that shouldn’t be crossed?

Finally, for most people today, exposure to information and technology is pervasive. The average US adult spends more than eleven hours a day interacting with media. Such levels of engagement lead to huge amounts of personal data generated and aggregated about you­—your preferences, interests, and state of mind. The more those who control persuasive technologies know about us, what we are doing, how we are feeling, when we feel it, and where we are, the better they can tailor messaging that provokes us into action. The unsuspecting target is grossly disadvantaged. Is it acceptable for the same services to both mediate our digital experience and to target us? Is there ever such thing as too much targeting?

The power dynamics of persuasive technologies are changing. Access to tools and technologies of persuasion is not egalitarian. Many require large amounts of both personal data and computation power, turning modern persuasion into an arms race where the better resourced will be better placed to influence audiences.

At the same time, the average person has very little information about how these persuasion technologies work, and is thus unlikely to understand how their beliefs and opinions might be manipulated by them. What’s more, there are few rules in place to protect people from abuse of persuasion technologies, much less even a clear articulation of what constitutes a level of manipulation so great it effectively takes agency away from those targeted. This creates a positive feedback loop that is dangerous for society.

In the 1970s, there was widespread fear about so-called subliminal messaging, which claimed that images of sex and death were hidden in the details of print advertisements, as in the curls of smoke in cigarette ads and the ice cubes of liquor ads. It was pretty much all a hoax, but that didn’t stop the Federal Trade Commission and the Federal Communications Commission from declaring it an illegal persuasive technology. That’s how worried people were about being manipulated without their knowledge and consent.

It is time to have a serious conversation about limiting the technologies of persuasion. This must begin by articulating what is permitted and what is not. If we don’t, the powerful persuaders will become even more powerful.

This essay was written with Alicia Wanless, and previously appeared in Foreign Policy.

EDITED TO ADD: Ukrainian translation.

Posted on December 14, 2020 at 2:03 PM • View Comments

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Sidebar photo of Bruce Schneier by Joe MacInnis.