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Vulnerabilities in Weapons Systems

“If you think any of these systems are going to work as expected in wartime, you’re fooling yourself.”

That was Bruce’s response at a conference hosted by US Transportation Command in 2017, after learning that their computerized logistical systems were mostly unclassified and on the Internet. That may be necessary to keep in touch with civilian companies like FedEx in peacetime or when fighting terrorists or insurgents. But in a new era facing off with China or Russia, it is dangerously complacent.

Any twenty-first century war will include cyber operations. Weapons and support systems will be successfully attacked. Rifles and pistols won’t work properly. Drones will be hijacked midair. Boats won’t sail, or will be misdirected. Hospitals won’t function. Equipment and supplies will arrive late or not at all.

Our military systems are vulnerable. We need to face that reality by halting the purchase of insecure weapons and support systems and by incorporating the realities of offensive cyberattacks into our military planning.

Over the past decade, militaries have established cyber commands and developed cyberwar doctrine. However, much of the current discussion is about offense. Increasing our offensive capabilities without being able to secure them is like having all the best guns in the world, and then storing them in an unlocked, unguarded armory. They just won’t be stolen; they’ll be subverted.

During that same period, we’ve seen increasingly brazen cyberattacks by everyone from criminals to governments. Everything is now a computer, and those computers are vulnerable. Cars, medical devices, power plants, and fuel pipelines have all been targets. Military computers, whether they’re embedded inside weapons systems or on desktops managing the logistics of those weapons systems, are similarly vulnerable. We could see effects as stodgy as making a tank impossible to start up, or sophisticated as retargeting a missile midair.

Military software is unlikely to be any more secure than commercial software. Although sensitive military systems rely on domestically manufactured chips as part of the Trusted Foundry program, many military systems contain the same foreign chips and code that commercial systems do: just like everyone around the world uses the same mobile phones, networking equipment, and computer operating systems. For example, there has been serious concern over Chinese-made 5G networking equipment that might be used by China to install “backdoors” that would allow the equipment to be controlled. This is just one of many risks to our normal civilian computer supply chains. And since military software is vulnerable to the same cyberattacks as commercial software, military supply chains have many of the same risks.

This is not speculative. A 2018 GAO report expressed concern regarding the lack of secure and patchable US weapons systems. The report observed that “in operational testing, the [Department of Defense] routinely found mission-critical cyber vulnerabilities in systems that were under development, yet program officials GAO met with believed their systems were secure and discounted some test results as unrealistic.” It’s a similar attitude to corporate executives who believe that they can’t be hacked — and equally naive.

An updated GAO report from earlier this year found some improvements, but the basic problem remained: “DOD is still learning how to contract for cybersecurity in weapon systems, and selected programs we reviewed have struggled to incorporate systems’ cybersecurity requirements into contracts.” While DOD now appears aware of the issue of lack of cybersecurity requirements, they’re still not sure yet how to fix it, and in three of the five cases GAO reviewed, DOD simply chose to not include the requirements at all.

Militaries around the world are now exploiting these vulnerabilities in weapons systems to carry out operations. When Israel in 2007 bombed a Syrian nuclear reactor, the raid was preceded by what is believed to have been a cyber attack on Syrian air defenses that resulted in radar screens showing no threat as bombers zoomed overhead. In 2018, a 29-country NATO exercise, Trident Juncture, that included cyberweapons was disrupted by Russian GPS jamming. NATO does try to test cyberweapons outside such exercises, but has limited scope in doing so. In May, Jens Stoltenberg, the NATO secretary-general, said that “NATO computer systems are facing almost daily cyberattacks.”

The war of the future will not only be about explosions, but will also be about disabling the systems that make armies run. It’s not (solely) that bases will get blown up; it’s that some bases will lose power, data, and communications. It’s not that self-driving trucks will suddenly go mad and begin rolling over friendly soldiers; it’s that they’ll casually roll off roads or into water where they sit, rusting, and in need of repair. It’s not that targeting systems on guns will be retargeted to 1600 Pennsylvania Avenue; it’s that many of them could simply turn off and not turn back on again.

So, how do we prepare for this next war? First, militaries need to introduce a little anarchy into their planning. Let’s have wargames where essential systems malfunction or are subverted­not all of the time, but randomly. To help combat siloed military thinking, include some civilians as well. Allow their ideas into the room when predicting potential enemy action. And militaries need to have well-developed backup plans, for when systems are subverted. In Joe Haldeman’s 1975 science-fiction novel The Forever War, he postulated a “stasis field” that forced his space marines to rely on nothing more than Roman military technologies, like javelins. We should be thinking in the same direction.

NATO isn’t yet allowing civilians not employed by NATO or associated military contractors access to their training cyber ranges where vulnerabilities could be discovered and remediated before battlefield deployment. Last year, one of us (Tarah) was listening to a NATO briefing after the end of the 2020 Cyber Coalition exercises, and asked how she and other information security researchers could volunteer to test cyber ranges used to train its cyber incident response force. She was told that including civilians would be a “welcome thought experiment in the tabletop exercises,” but including them in reality wasn’t considered. There is a rich opportunity for improvement here, providing transparency into where improvements could be made.

Second, it’s time to take cybersecurity seriously in military procurement, from weapons systems to logistics and communications contracts. In the three year span from the original 2018 GAO report to this year’s report, cybersecurity audit compliance went from 0% to 40% (those 2 of 5 programs mentioned earlier). We need to get much better. DOD requires that its contractors and suppliers follow the Cybersecurity Maturity Model Certification process; it should abide by the same standards. Making those standards both more rigorous and mandatory would be an obvious second step.

Gone are the days when we can pretend that our technologies will work in the face of a military cyberattack. Securing our systems will make everything we buy more expensive — maybe a lot more expensive. But the alternative is no longer viable.

The future of war is cyberwar. If your weapons and systems aren’t secure, don’t even bother bringing them onto the battlefield.

This essay was written with Tarah Wheeler, and previously appeared in Brookings TechStream.

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The Misaligned Incentives for Cloud Security

Russia’s Sunburst cyberespionage campaign, discovered late last year, impacted more than 100 large companies and US federal agencies, including the Treasury, Energy, Justice, and Homeland Security departments. A crucial part of the Russians’ success was their ability to move through these organizations by compromising cloud and local network identity systems to then access cloud accounts and pilfer emails and files.

Hackers said by the US government to have been working for the Kremlin targeted a widely used Microsoft cloud service that synchronizes user identities. The hackers stole security certificates to create their own identities, which allowed them to bypass safeguards such as multifactor authentication and gain access to Office 365 accounts, impacting thousands of users at the affected companies and government agencies.

It wasn’t the first time cloud services were the focus of a cyberattack, and it certainly won’t be the last. Cloud weaknesses were also critical in a 2019 breach at Capital One. There, an Amazon Web Services cloud vulnerability, compounded by Capital One’s own struggle to properly configure a complex cloud service, led to the disclosure of tens of millions of customer records, including credit card applications, Social Security numbers, and bank account information.

This trend of attacks on cloud services by criminals, hackers, and nation states is growing as cloud computing takes over worldwide as the default model for information technologies. Leaked data is bad enough, but disruption to the cloud, even an outage at a single provider, could quickly cost the global economy billions of dollars a day.

Cloud computing is an important source of risk both because it has quickly supplanted traditional IT and because it concentrates ownership of design choices at a very small number of companies. First, cloud is increasingly the default mode of computing for organizations, meaning ever more users and critical data from national intelligence and defense agencies ride on these technologies. Second, cloud computing services, especially those supplied by the world’s four largest providers — Amazon, Microsoft, Alibaba, and Google — concentrate key security and technology design choices inside a small number of organizations. The consequences of bad decisions or poorly made trade-offs can quickly scale to hundreds of millions of users.

The cloud is everywhere. Some cloud companies provide software as a service, support your Netflix habit, or carry your Slack chats. Others provide computing infrastructure like business databases and storage space. The largest cloud companies provide both.

The cloud can be deployed in several different ways, each of which shift the balance of responsibility for the security of this technology. But the cloud provider plays an important role in every case. Choices the provider makes in how these technologies are designed, built, and deployed influence the user’s security — yet the user has very little influence over them. Then, if Google or Amazon has a vulnerability in their servers — which you are unlikely to know about and have no control over — you suffer the consequences.

The problem is one of economics. On the surface, it might seem that competition between cloud companies gives them an incentive to invest in their users’ security. But several market failures get in the way of that ideal. First, security is largely an externality for these cloud companies, because the losses due to data breaches are largely borne by their users. As long as a cloud provider isn’t losing customers by the droves — which generally doesn’t happen after a security incident — it is incentivized to underinvest in security. Additionally, data shows that investors don’t punish the cloud service companies either: Stock price dips after a public security breach are both small and temporary.

Second, public information about cloud security generally doesn’t share the design trade-offs involved in building these cloud services or provide much transparency about the resulting risks. While cloud companies have to publicly disclose copious amounts of security design and operational information, it can be impossible for consumers to understand which threats the cloud services are taking into account, and how. This lack of understanding makes it hard to assess a cloud service’s overall security. As a result, customers and users aren’t able to differentiate between secure and insecure services, so they don’t base their buying and use decisions on it.

Third, cybersecurity is complex — and even more complex when the cloud is involved. For a customer like a company or government agency, the security dependencies of various cloud and on-premises network systems and services can be subtle and hard to map out. This means that users can’t adequately assess the security of cloud services or how they will interact with their own networks. This is a classic “lemons market” in economics, and the result is that cloud providers provide variable levels of security, as documented by Dan Geer, the chief information security officer for In-Q-Tel, and Wade Baker, a professor at Virginia Tech’s College of Business, when they looked at the prevalence of severe security findings at the top 10 largest cloud providers. Yet most consumers are none the wiser.

The result is a market failure where cloud service providers don’t compete to provide the best security for their customers and users at the lowest cost. Instead, cloud companies take the chance that they won’t get hacked, and past experience tells them they can weather the storm if they do. This kind of decision-making and priority-setting takes place at the executive level, of course, and doesn’t reflect the dedication and technical skill of product engineers and security specialists. The effect of this underinvestment is pernicious, however, by piling on risk that’s largely hidden from users. Widespread adoption of cloud computing carries that risk to an organization’s network, to its customers and users, and, in turn, to the wider internet.

This aggregation of cybersecurity risk creates a national security challenge. Policymakers can help address the challenge by setting clear expectations for the security of cloud services — and for making decisions and design trade-offs about that security transparent. The Biden administration, including newly nominated National Cyber Director Chris Inglis, should lead an interagency effort to work with cloud providers to review their threat models and evaluate the security architecture of their various offerings. This effort to require greater transparency from cloud providers and exert more scrutiny of their security engineering efforts should be accompanied by a push to modernize cybersecurity regulations for the cloud era.

The Federal Risk and Authorization Management Program (FedRAMP), which is the principal US government program for assessing the risk of cloud services and authorizing them for use by government agencies, would be a prime vehicle for these efforts. A recent executive order outlines several steps to make FedRAMP faster and more responsive. But the program is still focused largely on the security of individual services rather than the cloud vendors’ deeper architectural choices and threat models. Congressional action should reinforce and extend the executive order by adding new obligations for vendors to provide transparency about design trade-offs, threat models, and resulting risks. These changes could help transform FedRAMP into a more effective tool of security governance even as it becomes faster and more efficient.

Cloud providers have become important national infrastructure. Not since the heights of the mainframe era between the 1960s and early 1980s has the world witnessed computing systems of such complexity used by so many but designed and created by so few. The security of this infrastructure demands greater transparency and public accountability — if only to match the consequences of its failure.

This essay was written with Trey Herr, and previously appeared in Foreign Policy.

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AIs and Fake Comments

This month, the New York state attorney general issued a report on a scheme by “U.S. Companies and Partisans [to] Hack Democracy.” This wasn’t another attempt by Republicans to make it harder for Black people and urban residents to vote. It was a concerted attack on another core element of US democracy ­– the ability of citizens to express their voice to their political representatives. And it was carried out by generating millions of fake comments and fake emails purporting to come from real citizens.

This attack was detected because it was relatively crude. But artificial intelligence technologies are making it possible to generate genuine-seeming comments at scale, drowning out the voices of real citizens in a tidal wave of fake ones.

As political scientists like Paul Pierson have pointed out, what happens between elections is important to democracy. Politicians shape policies and they make laws. And citizens can approve or condemn what politicians are doing, through contacting their representatives or commenting on proposed rules.

That’s what should happen. But as the New York report shows, it often doesn’t. The big telecommunications companies paid millions of dollars to specialist “AstroTurf” companies to generate public comments. These companies then stole people’s names and email addresses from old files and from hacked data dumps and attached them to 8.5 million public comments and half a million letters to members of Congress. All of them said that they supported the corporations’ position on something called “net neutrality,” the idea that telecommunications companies must treat all Internet content equally and not prioritize any company or service. Three AstroTurf companies — Fluent, Opt-Intelligence and React2Media ­– agreed to pay nearly $4 million in fines.

The fakes were crude. Many of them were identical, while others were patchworks of simple textual variations: substituting “Federal Communications Commission” and “FCC” for each other, for example.

Next time, though, we won’t be so lucky. New technologies are about to make it far easier to generate enormous numbers of convincing personalized comments and letters, each with its own word choices, expressive style and pithy examples. The people who create fake grass-roots organizations have always been enthusiastic early adopters of technology, weaponizing letters, faxes, emails and Web comments to manufacture the appearance of public support or public outrage.

Take Generative Pre-trained Transformer 3, or GPT-3, an AI model created by OpenAI, a San Francisco based start-up. With minimal prompting, GPT-3 can generate convincing seeming newspaper articles, résumé cover letters, even Harry Potter fan fiction in the style of Ernest Hemingway. It is trivially easy to use these techniques to compose large numbers of public comments or letters to lawmakers.

OpenAI restricts access to GPT-3, but in a recent experiment, researchers used a different text-generation program to submit 1,000 comments in response to a government request for public input on a Medicaid issue. They all sounded unique, like real people advocating a specific policy position. They fooled the Medicaid.gov administrators, who accepted them as genuine concerns from actual human beings. The researchers subsequently identified the comments and asked for them to be removed, so that no actual policy debate would be unfairly biased. Others won’t be so ethical.

When the floodgates open, democratic speech is in danger of drowning beneath a tide of fake letters and comments, tweets and Facebook posts. The danger isn’t just that fake support can be generated for unpopular positions, as happened with net neutrality. It is that public commentary will be completely discredited. This would be bad news for specialist AstroTurf companies, which would have no business model if there isn’t a public that they can pretend to be representing. But it would empower still further other kinds of lobbyists, who at least can prove that they are who they say they are.

We may have a brief window to shore up the flood walls. The most effective response would be to regulate what UCLA sociologist Edward Walker has described as the “grassroots for hire” industry. Organizations that deliberately fabricate citizen voices shouldn’t just be subject to civil fines, but to criminal penalties. Businesses that hire these organizations should be held liable for failures of oversight. It’s impossible to prove or disprove whether telecommunications companies knew their subcontractors would create bogus citizen voices, but a liability standard would at least give such companies an incentive to find out. This is likely to be politically difficult to put in place, though, since so many powerful actors benefit from the status quo.

This essay was written with Henry Farrell, and previously appeared in the Washington Post.

EDITED TO ADD: CSET published an excellent report on AI-generated partisan content. Short summary: it’s pretty good, and will continue to get better. Renee DeRista has also written about this.

This paper is about a lower-tech version of this threat. Also this.

EDITED TO ADD: Another essay on the same topic.

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When AIs Start Hacking

If you don’t have enough to worry about already, consider a world where AIs are hackers.

Hacking is as old as humanity. We are creative problem solvers. We exploit loopholes, manipulate systems, and strive for more influence, power, and wealth. To date, hacking has exclusively been a human activity. Not for long.

As I lay out in a report I just published, artificial intelligence will eventually find vulnerabilities in all sorts of social, economic, and political systems, and then exploit them at unprecedented speed, scale, and scope. After hacking humanity, AI systems will then hack other AI systems, and humans will be little more than collateral damage.

Okay, maybe this is a bit of hyperbole, but it requires no far-future science fiction technology. I’m not postulating an AI “singularity,” where the AI-learning feedback loop becomes so fast that it outstrips human understanding. I’m not assuming intelligent androids. I’m not assuming evil intent. Most of these hacks don’t even require major research breakthroughs in AI. They’re already happening. As AI gets more sophisticated, though, we often won’t even know it’s happening.

AIs don’t solve problems like humans do. They look at more types of solutions than us. They’ll go down complex paths that we haven’t considered. This can be an issue because of something called the explainability problem. Modern AI systems are essentially black boxes. Data goes in one end, and an answer comes out the other. It can be impossible to understand how the system reached its conclusion, even if you’re a programmer looking at the code.

In 2015, a research group fed an AI system called Deep Patient health and medical data from some 700,000 people, and tested whether it could predict diseases. It could, but Deep Patient provides no explanation for the basis of a diagnosis, and the researchers have no idea how it comes to its conclusions. A doctor either can either trust or ignore the computer, but that trust will remain blind.

While researchers are working on AI that can explain itself, there seems to be a trade-off between capability and explainability. Explanations are a cognitive shorthand used by humans, suited for the way humans make decisions. Forcing an AI to produce explanations might be an additional constraint that could affect the quality of its decisions. For now, AI is becoming more and more opaque and less explainable.

Separately, AIs can engage in something called reward hacking. Because AIs don’t solve problems in the same way people do, they will invariably stumble on solutions we humans might never have anticipated­ — and some will subvert the intent of the system. That’s because AIs don’t think in terms of the implications, context, norms, and values we humans share and take for granted. This reward hacking involves achieving a goal but in a way the AI’s designers neither wanted nor intended.

Take a soccer simulation where an AI figured out that if it kicked the ball out of bounds, the goalie would have to throw the ball in and leave the goal undefended. Or another simulation, where an AI figured out that instead of running, it could make itself tall enough to cross a distant finish line by falling over it. Or the robot vacuum cleaner that instead of learning to not bump into things, it learned to drive backwards, where there were no sensors telling it it was bumping into things. If there are problems, inconsistencies, or loopholes in the rules, and if those properties lead to an acceptable solution as defined by the rules, then AIs will find these hacks.

We learned about this hacking problem as children with the story of King Midas. When the god Dionysus grants him a wish, Midas asks that everything he touches turns to gold. He ends up starving and miserable when his food, drink, and daughter all turn to gold. It’s a specification problem: Midas programmed the wrong goal into the system.

Genies are very precise about the wording of wishes, and can be maliciously pedantic. We know this, but there’s still no way to outsmart the genie. Whatever you wish for, he will always be able to grant it in a way you wish he hadn’t. He will hack your wish. Goals and desires are always underspecified in human language and thought. We never describe all the options, or include all the applicable caveats, exceptions, and provisos. Any goal we specify will necessarily be incomplete.

While humans most often implicitly understand context and usually act in good faith, we can’t completely specify goals to an AI. And AIs won’t be able to completely understand context.

In 2015, Volkswagen was caught cheating on emissions control tests. This wasn’t AI — human engineers programmed a regular computer to cheat — but it illustrates the problem. They programmed their engine to detect emissions control testing, and to behave differently. Their cheat remained undetected for years.

If I asked you to design a car’s engine control software to maximize performance while still passing emissions control tests, you wouldn’t design the software to cheat without understanding that you were cheating. This simply isn’t true for an AI. It will think “out of the box” simply because it won’t have a conception of the box. It won’t understand that the Volkswagen solution harms others, undermines the intent of the emissions control tests, and is breaking the law. Unless the programmers specify the goal of not behaving differently when being tested, an AI might come up with the same hack. The programmers will be satisfied, the accountants ecstatic. And because of the explainability problem, no one will realize what the AI did. And yes, knowing the Volkswagen story, we can explicitly set the goal to avoid that particular hack. But the lesson of the genie is that there will always be unanticipated hacks.

How realistic is AI hacking in the real world? The feasibility of an AI inventing a new hack depends a lot on the specific system being modeled. For an AI to even start on optimizing a problem, let alone hacking a completely novel solution, all of the rules of the environment must be formalized in a way the computer can understand. Goals — known in AI as objective functions — need to be established. And the AI needs some sort of feedback on how well it’s doing so that it can improve.

Sometimes this is simple. In chess, the rules, objective, and feedback — did you win or lose? — are all precisely specified. And there’s no context to know outside of those things that would muddy the waters. This is why most of the current examples of goal and reward hacking come from simulated environments. These are artificial and constrained, with all of the rules specified to the AI. The inherent ambiguity in most other systems ends up being a near-term security defense against AI hacking.

Where this gets interesting are systems that are well specified and almost entirely digital. Think about systems of governance like the tax code: a series of algorithms, with inputs and outputs. Think about financial systems, which are more or less algorithmically tractable.

We can imagine equipping an AI with all of the world’s laws and regulations, plus all the world’s financial information in real time, plus anything else we think might be relevant; and then giving it the goal of “maximum profit.” My guess is that this isn’t very far off, and that the result will be all sorts of novel hacks.

But advances in AI are discontinuous and counterintuitive. Things that seem easy turn out to be hard, and things that seem hard turn out to be easy. We don’t know until the breakthrough occurs.

When AIs start hacking, everything will change. They won’t be constrained in the same ways, or have the same limits, as people. They’ll change hacking’s speed, scale, and scope, at rates and magnitudes we’re not ready for. AI text generation bots, for example, will be replicated in the millions across social media. They will be able to engage on issues around the clock, sending billions of messages, and overwhelm any actual online discussions among humans. What we will see as boisterous political debate will be bots arguing with other bots. They’ll artificially influence what we think is normal, what we think others think.

The increasing scope of AI systems also makes hacks more dangerous. AIs are already making important decisions about our lives, decisions we used to believe were the exclusive purview of humans: Who gets parole, receives bank loans, gets into college, or gets a job. As AI systems get more capable, society will cede more — and more important — decisions to them. Hacks of these systems will become more damaging.

What if you fed an AI the entire US tax code? Or, in the case of a multinational corporation, the entire world’s tax codes? Will it figure out, without being told, that it’s smart to incorporate in Delaware and register your ship in Panama? How many loopholes will it find that we don’t already know about? Dozens? Thousands? We have no idea.

While we have societal systems that deal with hacks, those were developed when hackers were humans, and reflect human speed, scale, and scope. The IRS cannot deal with dozens — let alone thousands — of newly discovered tax loopholes. An AI that discovers unanticipated but legal hacks of financial systems could upend our markets faster than we could recover.

As I discuss in my report, while hacks can be used by attackers to exploit systems, they can also be used by defenders to patch and secure systems. So in the long run, AI hackers will favor the defense because our software, tax code, financial systems, and so on can be patched before they’re deployed. Of course, the transition period is dangerous because of all the legacy rules that will be hacked. There, our solution has to be resilience.

We need to build resilient governing structures that can quickly and effectively respond to the hacks. It won’t do any good if it takes years to update the tax code, or if a legislative hack becomes so entrenched that it can’t be patched for political reasons. This is a hard problem of modern governance. It also isn’t a substantially different problem than building governing structures that can operate at the speed and complexity of the information age.

What I’ve been describing is the interplay between human and computer systems, and the risks inherent when the computers start doing the part of humans. This, too, is a more general problem than AI hackers. It’s also one that technologists and futurists are writing about. And while it’s easy to let technology lead us into the future, we’re much better off if we as a society decide what technology’s role in our future should be.

This is all something we need to figure out now, before these AIs come online and start hacking our world.

This essay previously appeared on Wired.com

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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.

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National Security Risks of Late-Stage Capitalism

Early in 2020, cyberspace attackers apparently working for the Russian government compromised a piece of widely used network management software made by a company called SolarWinds. The hack gave the attackers access to the computer networks of some 18,000 of SolarWinds’s customers, including US government agencies such as the Homeland Security Department and State Department, American nuclear research labs, government contractors, IT companies and nongovernmental agencies around the world.

It was a huge attack, with major implications for US national security. The Senate Intelligence Committee is scheduled to hold a hearing on the breach on Tuesday. Who is at fault?

The US government deserves considerable blame, of course, for its inadequate cyberdefense. But to see the problem only as a technical shortcoming is to miss the bigger picture. The modern market economy, which aggressively rewards corporations for short-term profits and aggressive cost-cutting, is also part of the problem: Its incentive structure all but ensures that successful tech companies will end up selling insecure products and services.

Like all for-profit corporations, SolarWinds aims to increase shareholder value by minimizing costs and maximizing profit. The company is owned in large part by Silver Lake and Thoma Bravo, private-equity firms known for extreme cost-cutting.

SolarWinds certainly seems to have underspent on security. The company outsourced much of its software engineering to cheaper programmers overseas, even though that typically increases the risk of security vulnerabilities. For a while, in 2019, the update server’s password for SolarWinds’s network management software was reported to be “solarwinds123.” Russian hackers were able to breach SolarWinds’s own email system and lurk there for months. Chinese hackers appear to have exploited a separate vulnerability in the company’s products to break into US government computers. A cybersecurity adviser for the company said that he quit after his recommendations to strengthen security were ignored.

There is no good reason to underspend on security other than to save money — especially when your clients include government agencies around the world and when the technology experts that you pay to advise you are telling you to do more.

As the economics writer Matt Stoller has suggested, cybersecurity is a natural area for a technology company to cut costs because its customers won’t notice unless they are hacked ­– and if they are, they will have already paid for the product. In other words, the risk of a cyberattack can be transferred to the customers. Doesn’t this strategy jeopardize the possibility of long-term, repeat customers? Sure, there’s a danger there –­ but investors are so focused on short-term gains that they’re too often willing to take that risk.

The market loves to reward corporations for risk-taking when those risks are largely borne by other parties, like taxpayers. This is known as “privatizing profits and socializing losses.” Standard examples include companies that are deemed “too big to fail,” which means that society as a whole pays for their bad luck or poor business decisions. When national security is compromised by high-flying technology companies that fob off cybersecurity risks onto their customers, something similar is at work.

Similar misaligned incentives affect your everyday cybersecurity, too. Your smartphone is vulnerable to something called SIM-swap fraud because phone companies want to make it easy for you to frequently get a new phone — and they know that the cost of fraud is largely borne by customers. Data brokers and credit bureaus that collect, use, and sell your personal data don’t spend a lot of money securing it because it’s your problem if someone hacks them and steals it. Social media companies too easily let hate speech and misinformation flourish on their platforms because it’s expensive and complicated to remove it, and they don’t suffer the immediate costs ­– indeed, they tend to profit from user engagement regardless of its nature.

There are two problems to solve. The first is information asymmetry: buyers can’t adequately judge the security of software products or company practices. The second is a perverse incentive structure: the market encourages companies to make decisions in their private interest, even if that imperils the broader interests of society. Together these two problems result in companies that save money by taking on greater risk and then pass off that risk to the rest of us, as individuals and as a nation.

The only way to force companies to provide safety and security features for customers and users is with government intervention. Companies need to pay the true costs of their insecurities, through a combination of laws, regulations, and legal liability. Governments routinely legislate safety — pollution standards, automobile seat belts, lead-free gasoline, food service regulations. We need to do the same with cybersecurity: the federal government should set minimum security standards for software and software development.

In today’s underregulated markets, it’s just too easy for software companies like SolarWinds to save money by skimping on security and to hope for the best. That’s a rational decision in today’s free-market world, and the only way to change that is to change the economic incentives.

This essay previously appeared in the New York Times.

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On the Twitter Hack

Twitter was hacked this week. Not a few people’s Twitter accounts, but all of Twitter. Someone compromised the entire Twitter network, probably by stealing the log-in credentials of one of Twitter’s system administrators. Those are the people trusted to ensure that Twitter functions smoothly.

The hacker used that access to send tweets from a variety of popular and trusted accounts, including those of Joe Biden, Bill Gates, and Elon Musk, as part of a mundane scam — stealing bitcoin — but it’s easy to envision more nefarious scenarios. Imagine a government using this sort of attack against another government, coordinating a series of fake tweets from hundreds of politicians and other public figures the day before a major election, to affect the outcome. Or to escalate an international dispute. Done well, it would be devastating.

Whether the hackers had access to Twitter direct messages is not known. These DMs are not end-to-end encrypted, meaning that they are unencrypted inside Twitter’s network and could have been available to the hackers. Those messages — between world leaders, industry CEOs, reporters and their sources, heath organizations — are much more valuable than bitcoin. (If I were a national-intelligence agency, I might even use a bitcoin scam to mask my real intelligence-gathering purpose.) Back in 2018, Twitter said it was exploring encrypting those messages, but it hasn’t yet.

Internet communications platforms — such as Facebook, Twitter, and YouTube — are crucial in today’s society. They’re how we communicate with one another. They’re how our elected leaders communicate with us. They are essential infrastructure. Yet they are run by for-profit companies with little government oversight. This is simply no longer sustainable. Twitter and companies like it are essential to our national dialogue, to our economy, and to our democracy. We need to start treating them that way, and that means both requiring them to do a better job on security and breaking them up.

In the Twitter case this week, the hacker’s tactics weren’t particularly sophisticated. We will almost certainly learn about security lapses at Twitter that enabled the hack, possibly including a SIM-swapping attack that targeted an employee’s cellular service provider, or maybe even a bribed insider. The FBI is investigating.

This kind of attack is known as a “class break.” Class breaks are endemic to computerized systems, and they’re not something that we as users can defend against with better personal security. It didn’t matter whether individual accounts had a complicated and hard-to-remember password, or two-factor authentication. It didn’t matter whether the accounts were normally accessed via a Mac or a PC. There was literally nothing any user could do to protect against it.

Class breaks are security vulnerabilities that break not just one system, but an entire class of systems. They might exploit a vulnerability in a particular operating system that allows an attacker to take remote control of every computer that runs on that system’s software. Or a vulnerability in internet-enabled digital video recorders and webcams that allows an attacker to recruit those devices into a massive botnet. Or a single vulnerability in the Twitter network that allows an attacker to take over every account.

For Twitter users, this attack was a double whammy. Many people rely on Twitter’s authentication systems to know that someone who purports to be a certain celebrity, politician, or journalist is really that person. When those accounts were hijacked, trust in that system took a beating. And then, after the attack was discovered and Twitter temporarily shut down all verified accounts, the public lost a vital source of information.

There are many security technologies companies like Twitter can implement to better protect themselves and their users; that’s not the issue. The problem is economic, and fixing it requires doing two things. One is regulating these companies, and requiring them to spend more money on security. The second is reducing their monopoly power.

The security regulations for banks are complex and detailed. If a low-level banking employee were caught messing around with people’s accounts, or if she mistakenly gave her log-in credentials to someone else, the bank would be severely fined. Depending on the details of the incident, senior banking executives could be held personally liable. The threat of these actions helps keep our money safe. Yes, it costs banks money; sometimes it severely cuts into their profits. But the banks have no choice.

The opposite is true for these tech giants. They get to decide what level of security you have on your accounts, and you have no say in the matter. If you are offered security and privacy options, it’s because they decided you can have them. There is no regulation. There is no accountability. There isn’t even any transparency. Do you know how secure your data is on Facebook, or in Apple’s iCloud, or anywhere? You don’t. No one except those companies do. Yet they’re crucial to the country’s national security. And they’re the rare consumer product or service allowed to operate without significant government oversight.

For example, President Donald Trump’s Twitter account wasn’t hacked as Joe Biden’s was, because that account has “special protections,” the details of which we don’t know. We also don’t know what other world leaders have those protections, or the decision process surrounding who gets them. Are they manual? Can they scale? Can all verified accounts have them? Your guess is as good as mine.

In addition to security measures, the other solution is to break up the tech monopolies. Companies like Facebook and Twitter have so much power because they are so large, and they face no real competition. This is a national-security risk as well as a personal-security risk. Were there 100 different Twitter-like companies, and enough compatibility so that all their feeds could merge into one interface, this attack wouldn’t have been such a big deal. More important, the risk of a similar but more politically targeted attack wouldn’t be so great. If there were competition, different platforms would offer different security options, as well as different posting rules, different authentication guidelines — different everything. Competition is how our economy works; it’s how we spur innovation. Monopolies have more power to do what they want in the quest for profits, even if it harms people along the way.

This wasn’t Twitter’s first security problem involving trusted insiders. In 2017, on his last day of work, an employee shut down President Donald Trump’s account. In 2019, two people were charged with spying for the Saudi government while they were Twitter employees.

Maybe this hack will serve as a wake-up call. But if past incidents involving Twitter and other companies are any indication, it won’t. Underspending on security, and letting society pay the eventual price, is far more profitable. I don’t blame the tech companies. Their corporate mandate is to make as much money as is legally possible. Fixing this requires changes in the law, not changes in the hearts of the company’s leaders.

This essay previously appeared on TheAtlantic.com.

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The Security Value of Inefficiency

For decades, we have prized efficiency in our economy. We strive for it. We reward it. In normal times, that’s a good thing. Running just at the margins is efficient. A single just-in-time global supply chain is efficient. Consolidation is efficient. And that’s all profitable. Inefficiency, on the other hand, is waste. Extra inventory is inefficient. Overcapacity is inefficient. Using many small suppliers is inefficient. Inefficiency is unprofitable.

But inefficiency is essential security, as the COVID-19 pandemic is teaching us. All of the overcapacity that has been squeezed out of our healthcare system; we now wish we had it. All of the redundancy in our food production that has been consolidated away; we want that, too. We need our old, local supply chains — not the single global ones that are so fragile in this crisis. And we want our local restaurants and businesses to survive, not just the national chains.

We have lost much inefficiency to the market in the past few decades. Investors have become very good at noticing any fat in every system and swooping down to monetize those redundant assets. The winner-take-all mentality that has permeated so many industries squeezes any inefficiencies out of the system.

This drive for efficiency leads to brittle systems that function properly when everything is normal but break under stress. And when they break, everyone suffers. The less fortunate suffer and die. The more fortunate are merely hurt, and perhaps lose their freedoms or their future. But even the extremely fortunate suffer — maybe not in the short term, but in the long term from the constriction of the rest of society.

Efficient systems have limited ability to deal with system-wide economic shocks. Those shocks are coming with increased frequency. They’re caused by global pandemics, yes, but also by climate change, by financial crises, by political crises. If we want to be secure against these crises and more, we need to add inefficiency back into our systems.

I don’t simply mean that we need to make our food production, or healthcare system, or supply chains sloppy and wasteful. We need a certain kind of inefficiency, and it depends on the system in question. Sometimes we need redundancy. Sometimes we need diversity. Sometimes we need overcapacity.

The market isn’t going to supply any of these things, least of all in a strategic capacity that will result in resilience. What’s necessary to make any of this work is regulation.

First, we need to enforce antitrust laws. Our meat supply chain is brittle because there are limited numbers of massive meatpacking plants — now disease factories — rather than lots of smaller slaughterhouses. Our retail supply chain is brittle because a few national companies and websites dominate. We need multiple companies offering alternatives to a single product or service. We need more competition, more niche players. We need more local companies, more domestic corporate players, and diversity in our international suppliers. Competition provides all of that, while monopolies suck that out of the system.

The second thing we need is specific regulations that require certain inefficiencies. This isn’t anything new. Every safety system we have is, to some extent, an inefficiency. This is true for fire escapes on buildings, lifeboats on cruise ships, and multiple ways to deploy the landing gear on aircraft. Not having any of those things would make the underlying systems more efficient, but also less safe. It’s also true for the internet itself, originally designed with extensive redundancy as a Cold War security measure.

With those two things in place, the market can work its magic to provide for these strategic inefficiencies as cheaply and as effectively as possible. As long as there are competitors who are vying with each other, and there aren’t competitors who can reduce the inefficiencies and undercut the competition, these inefficiencies just become part of the price of whatever we’re buying.

The government is the entity that steps in and enforces a level playing field instead of a race to the bottom. Smart regulation addresses the long-term need for security, and ensures it’s not continuously sacrificed to short-term considerations.

We have largely been content to ignore the long term and let Wall Street run our economy as efficiently as it can. That’s no longer sustainable. We need inefficiency — the right kind in the right way — to ensure our security. No, it’s not free. But it’s worth the cost.

This essay previously appeared in Quartz.

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Security of Health Information

The world is racing to contain the new COVID-19 virus that is spreading around the globe with alarming speed. Right now, pandemic disease experts at the World Health Organization (WHO), the US Centers for Disease Control and Prevention (CDC), and other public-health agencies are gathering information to learn how and where the virus is spreading. To do so, they are using a variety of digital communications and surveillance systems. Like much of the medical infrastructure, these systems are highly vulnerable to hacking and interference.

That vulnerability should be deeply concerning. Governments and intelligence agencies have long had an interest in manipulating health information, both in their own countries and abroad. They might do so to prevent mass panic, avert damage to their economies, or avoid public discontent (if officials made grave mistakes in containing an outbreak, for example). Outside their borders, states might use disinformation to undermine their adversaries or disrupt an alliance between other nations. A sudden epidemic­ — when countries struggle to manage not just the outbreak but its social, economic, and political fallout­ — is especially tempting for interference.

In the case of COVID-19, such interference is already well underway. That fact should not come as a surprise. States hostile to the West have a long track record of manipulating information about health issues to sow distrust. In the 1980s, for example, the Soviet Union spread the false story that the US Department of Defense bioengineered HIV in order to kill African Americans. This propaganda was effective: some 20 years after the original Soviet disinformation campaign, a 2005 survey found that 48 percent of African Americans believed HIV was concocted in a laboratory, and 15 percent thought it was a tool of genocide aimed at their communities.

More recently, in 2018, Russia undertook an extensive disinformation campaign to amplify the anti-vaccination movement using social media platforms like Twitter and Facebook. Researchers have confirmed that Russian trolls and bots tweeted anti-vaccination messages at up to 22 times the rate of average users. Exposure to these messages, other researchers found, significantly decreased vaccine uptake, endangering individual lives and public health.

Last week, US officials accused Russia of spreading disinformation about COVID-19 in yet another coordinated campaign. Beginning around the middle of January, thousands of Twitter, Facebook, and Instagram accounts­ — many of which had previously been tied to Russia­ — had been seen posting nearly identical messages in English, German, French, and other languages, blaming the United States for the outbreak. Some of the messages claimed that the virus is part of a US effort to wage economic war on China, others that it is a biological weapon engineered by the CIA.

As much as this disinformation can sow discord and undermine public trust, the far greater vulnerability lies in the United States’ poorly protected emergency-response infrastructure, including the health surveillance systems used to monitor and track the epidemic. By hacking these systems and corrupting medical data, states with formidable cybercapabilities can change and manipulate data right at the source.

Here is how it would work, and why we should be so concerned. Numerous health surveillance systems are monitoring the spread of COVID-19 cases, including the CDC’s influenza surveillance network. Almost all testing is done at a local or regional level, with public-health agencies like the CDC only compiling and analyzing the data. Only rarely is an actual biological sample sent to a high-level government lab. Many of the clinics and labs providing results to the CDC no longer file reports as in the past, but have several layers of software to store and transmit the data.

Potential vulnerabilities in these systems are legion: hackers exploiting bugs in the software, unauthorized access to a lab’s servers by some other route, or interference with the digital communications between the labs and the CDC. That the software involved in disease tracking sometimes has access to electronic medical records is particularly concerning, because those records are often integrated into a clinic or hospital’s network of digital devices. One such device connected to a single hospital’s network could, in theory, be used to hack into the CDC’s entire COVID-19 database.

In practice, hacking deep into a hospital’s systems can be shockingly easy. As part of a cybersecurity study, Israeli researchers at Ben-Gurion University were able to hack into a hospital’s network via the public Wi-Fi system. Once inside, they could move through most of the hospital’s databases and diagnostic systems. Gaining control of the hospital’s unencrypted image database, the researchers inserted malware that altered healthy patients’ CT scans to show nonexistent tumors. Radiologists reading these images could only distinguish real from altered CTs 60 percent of the time­ — and only after being alerted that some of the CTs had been manipulated.

Another study directly relevant to public-health emergencies showed that a critical US biosecurity initiative, the Department of Homeland Security’s BioWatch program, had been left vulnerable to cyberattackers for over a decade. This program monitors more than 30 US jurisdictions and allows health officials to rapidly detect a bioweapons attack. Hacking this program could cover up an attack, or fool authorities into believing one has occurred.

Fortunately, no case of healthcare sabotage by intelligence agencies or hackers has come to light (the closest has been a series of ransomware attacks extorting money from hospitals, causing significant data breaches and interruptions in medical services). But other critical infrastructure has often been a target. The Russians have repeatedly hacked Ukraine’s national power grid, and have been probing US power plants and grid infrastructure as well. The United States and Israel hacked the Iranian nuclear program, while Iran has targeted Saudi Arabia’s oil infrastructure. There is no reason to believe that public-health infrastructure is in any way off limits.

Despite these precedents and proven risks, a detailed assessment of the vulnerability of US health surveillance systems to infiltration and manipulation has yet to be made. With COVID-19 on the verge of becoming a pandemic, the United States is at risk of not having trustworthy data, which in turn could cripple our country’s ability to respond.

Under normal conditions, there is plenty of time for health officials to notice unusual patterns in the data and track down wrong information­ — if necessary, using the old-fashioned method of giving the lab a call. But during an epidemic, when there are tens of thousands of cases to track and analyze, it would be easy for exhausted disease experts and public-health officials to be misled by corrupted data. The resulting confusion could lead to misdirected resources, give false reassurance that case numbers are falling, or waste precious time as decision makers try to validate inconsistent data.

In the face of a possible global pandemic, US and international public-health leaders must lose no time assessing and strengthening the security of the country’s digital health systems. They also have an important role to play in the broader debate over cybersecurity. Making America’s health infrastructure safe requires a fundamental reorientation of cybersecurity away from offense and toward defense. The position of many governments, including the United States’, that Internet infrastructure must be kept vulnerable so they can better spy on others, is no longer tenable. A digital arms race, in which more countries acquire ever more sophisticated cyberattack capabilities, only increases US vulnerability in critical areas such as pandemic control. By highlighting the importance of protecting digital health infrastructure, public-health leaders can and should call for a well-defended and peaceful Internet as a foundation for a healthy and secure world.

This essay was co-authored with Margaret Bourdeaux; a slightly different version appeared in Foreign Policy.

EDITED TO ADD: On last week’s squid post, there was a big conversation regarding the COVID-19. Many of the comments straddled the line between what are and aren’t the the core topics. Yesterday I deleted a bunch for being off-topic. Then I reconsidered and republished some of what I deleted.

Going forward, comments about the COVID-19 will be restricted to the security and risk implications of the virus. This includes cybersecurity, security, risk management, surveillance, and containment measures. Comments that stray off those topics will be removed. By clarifying this, I hope to keep the conversation on-topic while also allowing discussion of the security implications of current events.

Thank you for your patience and forbearance on this.

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Modern Mass Surveillance: Identify, Correlate, Discriminate

Communities across the United States are starting to ban facial recognition technologies. In May of last year, San Francisco banned facial recognition; the neighboring city of Oakland soon followed, as did Somerville and Brookline in Massachusetts (a statewide ban may follow). In December, San Diego suspended a facial recognition program in advance of a new statewide law, which declared it illegal, coming into effect. Forty major music festivals pledged not to use the technology, and activists are calling for a nationwide ban. Many Democratic presidential candidates support at least a partial ban on the technology.

These efforts are well-intentioned, but facial recognition bans are the wrong way to fight against modern surveillance. Focusing on one particular identification method misconstrues the nature of the surveillance society we’re in the process of building. Ubiquitous mass surveillance is increasingly the norm. In countries like China, a surveillance infrastructure is being built by the government for social control. In countries like the United States, it’s being built by corporations in order to influence our buying behavior, and is incidentally used by the government.

In all cases, modern mass surveillance has three broad components: identification, correlation and discrimination. Let’s take them in turn.

Facial recognition is a technology that can be used to identify people without their knowledge or consent. It relies on the prevalence of cameras, which are becoming both more powerful and smaller, and machine learning technologies that can match the output of these cameras with images from a database of existing photos.

But that’s just one identification technology among many. People can be identified at a distance by their heartbeat or by their gait, using a laser-based system. Cameras are so good that they can read fingerprints and iris patterns from meters away. And even without any of these technologies, we can always be identified because our smartphones broadcast unique numbers called MAC addresses. Other things identify us as well: our phone numbers, our credit card numbers, the license plates on our cars. China, for example, uses multiple identification technologies to support its surveillance state.

Once we are identified, the data about who we are and what we are doing can be correlated with other data collected at other times. This might be movement data, which can be used to “follow” us as we move throughout our day. It can be purchasing data, Internet browsing data, or data about who we talk to via email or text. It might be data about our income, ethnicity, lifestyle, profession and interests. There is an entire industry of data brokers who make a living analyzing and augmenting data about who we are ­– using surveillance data collected by all sorts of companies and then sold without our knowledge or consent.

There is a huge ­– and almost entirely unregulated ­– data broker industry in the United States that trades on our information. This is how large Internet companies like Google and Facebook make their money. It’s not just that they know who we are, it’s that they correlate what they know about us to create profiles about who we are and what our interests are. This is why many companies buy license plate data from states. It’s also why companies like Google are buying health records, and part of the reason Google bought the company Fitbit, along with all of its data.

The whole purpose of this process is for companies –­ and governments ­– to treat individuals differently. We are shown different ads on the Internet and receive different offers for credit cards. Smart billboards display different advertisements based on who we are. In the future, we might be treated differently when we walk into a store, just as we currently are when we visit websites.

The point is that it doesn’t matter which technology is used to identify people. That there currently is no comprehensive database of heartbeats or gaits doesn’t make the technologies that gather them any less effective. And most of the time, it doesn’t matter if identification isn’t tied to a real name. What’s important is that we can be consistently identified over time. We might be completely anonymous in a system that uses unique cookies to track us as we browse the Internet, but the same process of correlation and discrimination still occurs. It’s the same with faces; we can be tracked as we move around a store or shopping mall, even if that tracking isn’t tied to a specific name. And that anonymity is fragile: If we ever order something online with a credit card, or purchase something with a credit card in a store, then suddenly our real names are attached to what was anonymous tracking information.

Regulating this system means addressing all three steps of the process. A ban on facial recognition won’t make any difference if, in response, surveillance systems switch to identifying people by smartphone MAC addresses. The problem is that we are being identified without our knowledge or consent, and society needs rules about when that is permissible.

Similarly, we need rules about how our data can be combined with other data, and then bought and sold without our knowledge or consent. The data broker industry is almost entirely unregulated; there’s only one law ­– passed in Vermont in 2018 ­– that requires data brokers to register and explain in broad terms what kind of data they collect. The large Internet surveillance companies like Facebook and Google collect dossiers on us are more detailed than those of any police state of the previous century. Reasonable laws would prevent the worst of their abuses.

Finally, we need better rules about when and how it is permissible for companies to discriminate. Discrimination based on protected characteristics like race and gender is already illegal, but those rules are ineffectual against the current technologies of surveillance and control. When people can be identified and their data correlated at a speed and scale previously unseen, we need new rules.

Today, facial recognition technologies are receiving the brunt of the tech backlash, but focusing on them misses the point. We need to have a serious conversation about all the technologies of identification, correlation and discrimination, and decide how much we as a society want to be spied on by governments and corporations — and what sorts of influence we want them to have over our lives.

This essay previously appeared in the New York Times.

EDITED TO ADD: Rereading this post-publication, I see that it comes off as overly critical of those who are doing activism in this space. Writing the piece, I wasn’t thinking about political tactics. I was thinking about the technologies that support surveillance capitalism, and law enforcement’s usage of that corporate platform. Of course it makes sense to focus on face recognition in the short term. It’s something that’s easy to explain, viscerally creepy, and obviously actionable. It also makes sense to focus specifically on law enforcement’s use of the technology; there are clear civil and constitutional rights issues. The fact that law enforcement is so deeply involved in the technology’s marketing feels wrong. And the technology is currently being deployed in Hong Kong against political protesters. It’s why the issue has momentum, and why we’ve gotten the small wins we’ve had. (The EU is considering a five-year ban on face recognition technologies.) Those wins build momentum, which lead to more wins. I should have been kinder to those in the trenches.

If you want to help, sign the petition from Public Voice calling on a moratorium on facial recognition technology for mass surveillance. Or write to your US congressperson and demand similar action. There’s more information from EFF and EPIC.

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