Entries Tagged "trust"

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The Inability to Simultaneously Verify Sentience, Location, and Identity

Really interesting “systematization of knowledge” paper:

“SoK: The Ghost Trilemma”

Abstract: Trolls, bots, and sybils distort online discourse and compromise the security of networked platforms. User identity is central to the vectors of attack and manipulation employed in these contexts. However it has long seemed that, try as it might, the security community has been unable to stem the rising tide of such problems. We posit the Ghost Trilemma, that there are three key properties of identity—sentience, location, and uniqueness—that cannot be simultaneously verified in a fully-decentralized setting. Many fully-decentralized systems—whether for communication or social coordination—grapple with this trilemma in some way, perhaps unknowingly. In this Systematization of Knowledge (SoK) paper, we examine the design space, use cases, problems with prior approaches, and possible paths forward. We sketch a proof of this trilemma and outline options for practical, incrementally deployable schemes to achieve an acceptable tradeoff of trust in centralized trust anchors, decentralized operation, and an ability to withstand a range of attacks, while protecting user privacy.

I think this conceptualization makes sense, and explains a lot.

Posted on August 11, 2023 at 7:08 AM • View Comments

The Need for Trustworthy AI

If you ask Alexa, Amazon’s voice assistant AI system, whether Amazon is a monopoly, it responds by saying it doesn’t know. It doesn’t take much to make it lambaste the other tech giants, but it’s silent about its own corporate parent’s misdeeds.

When Alexa responds in this way, it’s obvious that it is putting its developer’s interests ahead of yours. Usually, though, it’s not so obvious whom an AI system is serving. To avoid being exploited by these systems, people will need to learn to approach AI skeptically. That means deliberately constructing the input you give it and thinking critically about its output.

Newer generations of AI models, with their more sophisticated and less rote responses, are making it harder to tell who benefits when they speak. Internet companies’ manipulating what you see to serve their own interests is nothing new. Google’s search results and your Facebook feed are filled with paid entries. Facebook, TikTok and others manipulate your feeds to maximize the time you spend on the platform, which means more ad views, over your well-being.

What distinguishes AI systems from these other internet services is how interactive they are, and how these interactions will increasingly become like relationships. It doesn’t take much extrapolation from today’s technologies to envision AIs that will plan trips for you, negotiate on your behalf or act as therapists and life coaches.

They are likely to be with you 24/7, know you intimately, and be able to anticipate your needs. This kind of conversational interface to the vast network of services and resources on the web is within the capabilities of existing generative AIs like ChatGPT. They are on track to become personalized digital assistants.

As a security expert and data scientist, we believe that people who come to rely on these AIs will have to trust them implicitly to navigate daily life. That means they will need to be sure the AIs aren’t secretly working for someone else. Across the internet, devices and services that seem to work for you already secretly work against you. Smart TVs spy on you. Phone apps collect and sell your data. Many apps and websites manipulate you through dark patterns, design elements that deliberately mislead, coerce or deceive website visitors. This is surveillance capitalism, and AI is shaping up to be part of it.

Quite possibly, it could be much worse with AI. For that AI digital assistant to be truly useful, it will have to really know you. Better than your phone knows you. Better than Google search knows you. Better, perhaps, than your close friends, intimate partners and therapist know you.

You have no reason to trust today’s leading generative AI tools. Leave aside the hallucinations, the made-up “facts” that GPT and other large language models produce. We expect those will be largely cleaned up as the technology improves over the next few years.

But you don’t know how the AIs are configured: how they’ve been trained, what information they’ve been given, and what instructions they’ve been commanded to follow. For example, researchers uncovered the secret rules that govern the Microsoft Bing chatbot’s behavior. They’re largely benign but can change at any time.

Many of these AIs are created and trained at enormous expense by some of the largest tech monopolies. They’re being offered to people to use free of charge, or at very low cost. These companies will need to monetize them somehow. And, as with the rest of the internet, that somehow is likely to include surveillance and manipulation.

Imagine asking your chatbot to plan your next vacation. Did it choose a particular airline or hotel chain or restaurant because it was the best for you or because its maker got a kickback from the businesses? As with paid results in Google search, newsfeed ads on Facebook and paid placements on Amazon queries, these paid influences are likely to get more surreptitious over time.

If you’re asking your chatbot for political information, are the results skewed by the politics of the corporation that owns the chatbot? Or the candidate who paid it the most money? Or even the views of the demographic of the people whose data was used in training the model? Is your AI agent secretly a double agent? Right now, there is no way to know.

We believe that people should expect more from the technology and that tech companies and AIs can become more trustworthy. The European Union’s proposed AI Act takes some important steps, requiring transparency about the data used to train AI models, mitigation for potential bias, disclosure of foreseeable risks and reporting on industry standard tests.

Most existing AIs fail to comply with this emerging European mandate, and, despite recent prodding from Senate Majority Leader Chuck Schumer, the US is far behind on such regulation.

The AIs of the future should be trustworthy. Unless and until the government delivers robust consumer protections for AI products, people will be on their own to guess at the potential risks and biases of AI, and to mitigate their worst effects on people’s experiences with them.

So when you get a travel recommendation or political information from an AI tool, approach it with the same skeptical eye you would a billboard ad or a campaign volunteer. For all its technological wizardry, the AI tool may be little more than the same.

This essay was written with Nathan Sanders, and previously appeared on The Conversation.

Posted on August 3, 2023 at 7:17 AM • View Comments

Hacking AI Resume Screening with Text in a White Font

The Washington Post is reporting on a hack to fool automatic resume sorting programs: putting text in a white font. The idea is that the programs rely primarily on simple pattern matching, and the trick is to copy a list of relevant keywords—or the published job description—into the resume in a white font. The computer will process the text, but humans won’t see it.

Clever. I’m not sure it’s actually useful in getting a job, though. Eventually the humans will figure out that the applicant doesn’t actually have the required skills. But…maybe.

Posted on August 1, 2023 at 7:11 AM • View Comments

Building Trustworthy AI

We will all soon get into the habit of using AI tools for help with everyday problems and tasks. We should get in the habit of questioning the motives, incentives, and capabilities behind them, too.

Imagine you’re using an AI chatbot to plan a vacation. Did it suggest a particular resort because it knows your preferences, or because the company is getting a kickback from the hotel chain? Later, when you’re using another AI chatbot to learn about a complex economic issue, is the chatbot reflecting your politics or the politics of the company that trained it?

For AI to truly be our assistant, it needs to be trustworthy. For it to be trustworthy, it must be under our control; it can’t be working behind the scenes for some tech monopoly. This means, at a minimum, the technology needs to be transparent. And we all need to understand how it works, at least a little bit.

Amid the myriad warnings about creepy risks to well-being, threats to democracy, and even existential doom that have accompanied stunning recent developments in artificial intelligence (AI)—and large language models (LLMs) like ChatGPT and GPT-4—one optimistic vision is abundantly clear: this technology is useful. It can help you find information, express your thoughts, correct errors in your writing, and much more. If we can navigate the pitfalls, its assistive benefit to humanity could be epoch-defining. But we’re not there yet.

Let’s pause for a moment and imagine the possibilities of a trusted AI assistant. It could write the first draft of anything: emails, reports, essays, even wedding vows. You would have to give it background information and edit its output, of course, but that draft would be written by a model trained on your personal beliefs, knowledge, and style. It could act as your tutor, answering questions interactively on topics you want to learn about—in the manner that suits you best and taking into account what you already know. It could assist you in planning, organizing, and communicating: again, based on your personal preferences. It could advocate on your behalf with third parties: either other humans or other bots. And it could moderate conversations on social media for you, flagging misinformation, removing hate or trolling, translating for speakers of different languages, and keeping discussions on topic; or even mediate conversations in physical spaces, interacting through speech recognition and synthesis capabilities.

Today’s AIs aren’t up for the task. The problem isn’t the technology—that’s advancing faster than even the experts had guessed—it’s who owns it. Today’s AIs are primarily created and run by large technology companies, for their benefit and profit. Sometimes we are permitted to interact with the chatbots, but they’re never truly ours. That’s a conflict of interest, and one that destroys trust.

The transition from awe and eager utilization to suspicion to disillusionment is a well worn one in the technology sector. Twenty years ago, Google’s search engine rapidly rose to monopolistic dominance because of its transformative information retrieval capability. Over time, the company’s dependence on revenue from search advertising led them to degrade that capability. Today, many observers look forward to the death of the search paradigm entirely. Amazon has walked the same path, from honest marketplace to one riddled with lousy products whose vendors have paid to have the company show them to you. We can do better than this. If each of us are going to have an AI assistant helping us with essential activities daily and even advocating on our behalf, we each need to know that it has our interests in mind. Building trustworthy AI will require systemic change.

First, a trustworthy AI system must be controllable by the user. That means that the model should be able to run on a user’s owned electronic devices (perhaps in a simplified form) or within a cloud service that they control. It should show the user how it responds to them, such as when it makes queries to search the web or external services, when it directs other software to do things like sending an email on a user’s behalf, or modifies the user’s prompts to better express what the company that made it thinks the user wants. It should be able to explain its reasoning to users and cite its sources. These requirements are all well within the technical capabilities of AI systems.

Furthermore, users should be in control of the data used to train and fine-tune the AI system. When modern LLMs are built, they are first trained on massive, generic corpora of textual data typically sourced from across the Internet. Many systems go a step further by fine-tuning on more specific datasets purpose built for a narrow application, such as speaking in the language of a medical doctor, or mimicking the manner and style of their individual user. In the near future, corporate AIs will be routinely fed your data, probably without your awareness or your consent. Any trustworthy AI system should transparently allow users to control what data it uses.

Many of us would welcome an AI-assisted writing application fine tuned with knowledge of which edits we have accepted in the past and which we did not. We would be more skeptical of a chatbot knowledgeable about which of their search results led to purchases and which did not.

You should also be informed of what an AI system can do on your behalf. Can it access other apps on your phone, and the data stored with them? Can it retrieve information from external sources, mixing your inputs with details from other places you may or may not trust? Can it send a message in your name (hopefully based on your input)? Weighing these types of risks and benefits will become an inherent part of our daily lives as AI-assistive tools become integrated with everything we do.

Realistically, we should all be preparing for a world where AI is not trustworthy. Because AI tools can be so incredibly useful, they will increasingly pervade our lives, whether we trust them or not. Being a digital citizen of the next quarter of the twenty-first century will require learning the basic ins and outs of LLMs so that you can assess their risks and limitations for a given use case. This will better prepare you to take advantage of AI tools, rather than be taken advantage by them.

In the world’s first few months of widespread use of models like ChatGPT, we’ve learned a lot about how AI creates risks for users. Everyone has heard by now that LLMs “hallucinate,” meaning that they make up “facts” in their outputs, because their predictive text generation systems are not constrained to fact check their own emanations. Many users learned in March that information they submit as prompts to systems like ChatGPT may not be kept private after a bug revealed users’ chats. Your chat histories are stored in systems that may be insecure.

Researchers have found numerous clever ways to trick chatbots into breaking their safety controls; these work largely because many of the “rules” applied to these systems are soft, like instructions given to a person, rather than hard, like coded limitations on a product’s functions. It’s as if we are trying to keep AI safe by asking it nicely to drive carefully, a hopeful instruction, rather than taking away its keys and placing definite constraints on its abilities.

These risks will grow as companies grant chatbot systems more capabilities. OpenAI is providing developers wide access to build tools on top of GPT: tools that give their AI systems access to your email, to your personal account information on websites, and to computer code. While OpenAI is applying safety protocols to these integrations, it’s not hard to imagine those being relaxed in a drive to make the tools more useful. It seems likewise inevitable that other companies will come along with less bashful strategies for securing AI market share.

Just like with any human, building trust with an AI will be hard won through interaction over time. We will need to test these systems in different contexts, observe their behavior, and build a mental model for how they will respond to our actions. Building trust in that way is only possible if these systems are transparent about their capabilities, what inputs they use and when they will share them, and whose interests they are evolving to represent.

This essay was written with Nathan Sanders, and previously appeared on Gizmodo.com.

Posted on May 11, 2023 at 7:17 AM • View Comments

An Untrustworthy TLS Certificate in Browsers

The major browsers natively trust a whole bunch of certificate authorities, and some of them are really sketchy:

Google’s Chrome, Apple’s Safari, nonprofit Firefox and others allow the company, TrustCor Systems, to act as what’s known as a root certificate authority, a powerful spot in the internet’s infrastructure that guarantees websites are not fake, guiding users to them seamlessly.

The company’s Panamanian registration records show that it has the identical slate of officers, agents and partners as a spyware maker identified this year as an affiliate of Arizona-based Packet Forensics, which public contracting records and company documents show has sold communication interception services to U.S. government agencies for more than a decade.

[…]

In the earlier spyware matter, researchers Joel Reardon of the University of Calgary and Serge Egelman of the University of California at Berkeley found that a Panamanian company, Measurement Systems, had been paying developers to include code in a variety of innocuous apps to record and transmit users’ phone numbers, email addresses and exact locations. They estimated that those apps were downloaded more than 60 million times, including 10 million downloads of Muslim prayer apps.

Measurement Systems’ website was registered by Vostrom Holdings, according to historic domain name records. Vostrom filed papers in 2007 to do business as Packet Forensics, according to Virginia state records. Measurement Systems was registered in Virginia by Saulino, according to another state filing.

More details by Reardon.

Cory Doctorow does a great job explaining the context and the general security issues.

EDITED TO ADD (11/10): Slashdot thread.

Posted on November 10, 2022 at 9:18 AM • View Comments

VPNs and Trust

TorrentFreak surveyed nineteen VPN providers, asking them questions about their privacy practices: what data they keep, how they respond to court order, what country they are incorporated in, and so on.

Most interesting to me is the home countries of these companies. Express VPN is incorporated in the British Virgin Islands. NordVPN is incorporated in Panama. There are VPNs from the Seychelles, Malaysia, and Bulgaria. There are VPNs from more Western and democratic countries like the US, Switzerland, Canada, and Sweden. Presumably all of those companies follow the laws of their home country.

And it matters. I’ve been thinking about this since Trojan Shield was made public. This is the joint US/Australia-run encrypted messaging service that lured criminals to use it, and then spied on everything they did. Or, at least, Australian law enforcement spied on everyone. The FBI wasn’t able to because the US has better privacy laws.

We don’t talk about it a lot, but VPNs are entirely based on trust. As a consumer, you have no idea which company will best protect your privacy. You don’t know the data protection laws of the Seychelles or Panama. You don’t know which countries can put extra-legal pressure on companies operating within their jurisdiction. You don’t know who actually owns and runs the VPNs. You don’t even know which foreign companies the NSA has targeted for mass surveillance. All you can do is make your best guess, and hope you guessed well.

Posted on June 16, 2021 at 6:17 AM • View Comments

FBI/AFP-Run Encrypted Phone

For three years, the Federal Bureau of Investigation and the Australian Federal Police owned and operated a commercial encrypted phone app, called AN0M, that was used by organized crime around the world. Of course, the police were able to read everything—I don’t even know if this qualifies as a backdoor. This week, the world’s police organizations announced 800 arrests based on text messages sent over the app. We’ve seen law enforcement take over encrypted apps before: for example, EncroChat. This operation, code-named Trojan Shield, is the first time law enforcement managed an app from the beginning.

If there is any moral to this, it’s one that all of my blog readers should already know: trust is essential to security. And the number of people you need to trust is larger than you might originally think. For an app to be secure, you need to trust the hardware, the operating system, the software, the update mechanism, the login mechanism, and on and on and on. If one of those is untrustworthy, the whole system is insecure.

It’s the same reason blockchain-based currencies are so insecure, even if the cryptography is sound.

Posted on June 11, 2021 at 6:32 AM • View Comments

NSA on Authentication Hacks (Related to SolarWinds Breach)

The NSA has published an advisory outlining how “malicious cyber actors” are “are manipulating trust in federated authentication environments to access protected data in the cloud.” This is related to the SolarWinds hack I have previously written about, and represents one of the techniques the SVR is using once it has gained access to target networks.

From the summary:

Malicious cyberactors are abusing trust in federated authentication environments to access protected data. The exploitation occurs after the actors have gained initial access to a victim’s on-premises network. The actors leverage privileged access in the on-premises environment to subvert the mechanisms that the organization uses to grant access to cloud and on-premises resources and/or to compromise administrator credentials with the ability to manage cloud resources. The actors demonstrate two sets of tactics, techniques,and procedures (TTP) for gaining access to the victim network’s cloud resources, often with a particular focus on organizational email.

In the first TTP, the actors compromise on-premises components of a federated SSO infrastructure and steal the credential or private key that is used to sign Security Assertion Markup Language (SAML) tokens(TA0006, T1552, T1552.004). Using the private keys, the actors then forge trusted authentication tokens to access cloud resources. A recent NSA Cybersecurity Advisory warned of actors exploiting a vulnerability in VMware Access and VMware Identity Manager that allowed them to perform this TTP and abuse federated SSO infrastructure.While that example of this TTP may have previously been attributed to nation-state actors, a wealth of actors could be leveraging this TTP for their objectives. This SAML forgery technique has been known and used by cyber actors since at least 2017.

In a variation of the first TTP, if the malicious cyber actors are unable to obtain anon-premises signing key, they would attempt to gain sufficient administrative privileges within the cloud tenant to add a malicious certificate trust relationship for forging SAML tokens.

In the second TTP, the actors leverage a compromised global administrator account to assign credentials to cloud application service principals (identities for cloud applications that allow the applications to be invoked to access other cloud resources). The actors then invoke the application’s credentials for automated access to cloud resources (often email in particular) that would otherwise be difficult for the actors to access or would more easily be noticed as suspicious (T1114, T1114.002).

This is an ongoing story, and I expect to see a lot more about TTP—nice acronym there—in coming weeks.

Related: Tom Bossert has a scathing op-ed on the breach. Jack Goldsmith’s essay is worth reading. So is Nick Weaver’s.

Posted on December 18, 2020 at 10:35 AM • View Comments

Policy vs. Technology

Sometime around 1993 or 1994, during the first Crypto Wars, I was part of a group of cryptography experts that went to Washington to advocate for strong encryption. Matt Blaze and Ron Rivest were with me; I don’t remember who else. We met with then Massachusetts Representative Ed Markey. (He didn’t become a senator until 2013.) Back then, he and Vermont Senator Patrick Leahy were the most knowledgeable on this issue and our biggest supporters against government backdoors. They still are.

Markey was against forcing encrypted phone providers to implement the NSA’s Clipper Chip in their devices, but wanted us to reach a compromise with the FBI regardless. This completely startled us techies, who thought having the right answer was enough. It was at that moment that I learned an important difference between technologists and policy makers. Technologists want solutions; policy makers want consensus.

Since then, I have become more immersed in policy discussions. I have spent more time with legislators, advised advocacy organizations like EFF and EPIC, and worked with policy-minded think tanks in the United States and around the world. I teach cybersecurity policy and technology at the Harvard Kennedy School of Government. My most recent two books, Data and Goliath—about surveillance—and Click Here to Kill Everybody—about IoT security—are really about the policy implications of technology.

Over that time, I have observed many other differences between technologists and policy makers—differences that we in cybersecurity need to understand if we are to translate our technological solutions into viable policy outcomes.

Technologists don’t try to consider all of the use cases of a given technology. We tend to build something for the uses we envision, and hope that others can figure out new and innovative ways to extend what we created. We love it when there is a new use for a technology that we never considered and that changes the world. And while we might be good at security around the use cases we envision, we are regularly blindsided when it comes to new uses or edge cases. (Authentication risks surrounding someone’s intimate partner is a good example.)

Policy doesn’t work that way; it’s specifically focused on use. It focuses on people and what they do. Policy makers can’t create policy around a piece of technology without understanding how it is used—how all of it’s used.

Policy is often driven by exceptional events, like the FBI’s desire to break the encryption on the San Bernardino shooter’s iPhone. (The PATRIOT Act is the most egregious example I can think of.) Technologists tend to look at more general use cases, like the overall value of strong encryption to societal security. Policy tends to focus on the past, making existing systems work or correcting wrongs that have happened. It’s hard to imagine policy makers creating laws around VR systems, because they don’t yet exist in any meaningful way. Technology is inherently future focused. Technologists try to imagine better systems, or future flaws in present systems, and work to improve things.

As technologists, we iterate. It’s how we write software. It’s how we field products. We know we can’t get it right the first time, so we have developed all sorts of agile systems to deal with that fact. Policy making is often the opposite. U.S. federal laws take months or years to negotiate and pass, and after that the issue doesn’t get addressed again for a decade or more. It is much more critical to get it right the first time, because the effects of getting it wrong are long lasting. (See, for example, parts of the GDPR.) Sometimes regulatory agencies can be more agile. The courts can also iterate policy, but it’s slower.

Along similar lines, the two groups work in very different time frames. Engineers, conditioned by Moore’s law, have long thought of 18 months as the maximum time to roll out a new product, and now think in terms of continuous deployment of new features. As I said previously, policy makers tend to think in terms of multiple years to get a law or regulation in place, and then more years as the case law builds up around it so everyone knows what it really means. It’s like tortoises and hummingbirds.

Technology is inherently global. It is often developed with local sensibilities according to local laws, but it necessarily has global reach. Policy is always jurisdictional. This difference is causing all sorts of problems for the global cloud services we use every day. The providers are unable to operate their global systems in compliance with more than 200 different—and sometimes conflicting—national requirements. Policy makers are often unimpressed with claims of inability; laws are laws, they say, and if Facebook can translate its website into French for the French, it can also implement their national laws.

Technology and policy both use concepts of trust, but differently. Technologists tend to think of trust in terms of controls on behavior. We’re getting better—NIST’s recent work on trust is a good example—but we have a long way to go. For example, Google’s Trust and Safety Department does a lot of AI and ethics work largely focused on technological controls. Policy makers think of trust in more holistic societal terms: trust in institutions, trust as the ability not to worry about adverse outcomes, consumer confidence. This dichotomy explains how techies can claim bitcoin is trusted because of the strong cryptography, but policy makers can’t imagine calling a system trustworthy when you lose all your money if you forget your encryption key.

Policy is how society mediates how individuals interact with society. Technology has the potential to change how individuals interact with society. The conflict between these two causes considerable friction, as technologists want policy makers to get out of the way and not stifle innovation, and policy makers want technologists to stop moving fast and breaking so many things.

Finally, techies know that code is law­—that the restrictions and limitations of a technology are more fundamental than any human-created legal anything. Policy makers know that law is law, and tech is just tech. We can see this in the tension between applying existing law to new technologies and creating new law specifically for those new technologies.

Yes, these are all generalizations and there are exceptions. It’s also not all either/or. Great technologists and policy makers can see the other perspectives. The best policy makers know that for all their work toward consensus, they won’t make progress by redefining pi as three. Thoughtful technologists look beyond the immediate user demands to the ways attackers might abuse their systems, and design against those adversaries as well. These aren’t two alien species engaging in first contact, but cohorts who can each learn and borrow tools from the other. Too often, though, neither party tries.

In October, I attended the first ACM Symposium on Computer Science and the Law. Google counsel Brian Carver talked about his experience with the few computer science grad students who would attend his Intellectual Property and Cyberlaw classes every year at UC Berkeley. One of the first things he would do was give the students two different cases to read. The cases had nearly identical facts, and the judges who’d ruled on them came to exactly opposite conclusions. The law students took this in stride; it’s the way the legal system works when it’s wrestling with a new concept or idea. But it shook the computer science students. They were appalled that there wasn’t a single correct answer.

But that’s not how law works, and that’s not how policy works. As the technologies we’re creating become more central to society, and as we in technology continue to move into the public sphere and become part of the increasingly important policy debates, it is essential that we learn these lessons. Gone are the days when we were creating purely technical systems and our work ended at the keyboard and screen. Now we’re building complex socio-technical systems that are literally creating a new world. And while it’s easy to dismiss policy makers as doing it wrong, it’s important to understand that they’re not. Policy making has been around a lot longer than the Internet or computers or any technology. And the essential challenges of this century will require both groups to work together.

This essay previously appeared in IEEE Security & Privacy.

EDITED TO ADD (3/16): This essay has been translated into Spanish.

Posted on February 21, 2020 at 5:54 AM • View Comments

Supply-Chain Security and Trust

The United States government’s continuing disagreement with the Chinese company Huawei underscores a much larger problem with computer technologies in general: We have no choice but to trust them completely, and it’s impossible to verify that they’re trustworthy. Solving this problem ­ which is increasingly a national security issue ­ will require us to both make major policy changes and invent new technologies.

The Huawei problem is simple to explain. The company is based in China and subject to the rules and dictates of the Chinese government. The government could require Huawei to install back doors into the 5G routers it sells abroad, allowing the government to eavesdrop on communications or ­—even worse ­—take control of the routers during wartime. Since the United States will rely on those routers for all of its communications, we become vulnerable by building our 5G backbone on Huawei equipment.

It’s obvious that we can’t trust computer equipment from a country we don’t trust, but the problem is much more pervasive than that. The computers and smartphones you use are not built in the United States. Their chips aren’t made in the United States. The engineers who design and program them come from over a hundred countries. Thousands of people have the opportunity, acting alone, to slip a back door into the final product.

There’s more. Open-source software packages are increasingly targeted by groups installing back doors. Fake apps in the Google Play store illustrate vulnerabilities in our software distribution systems. The NotPetya worm was distributed by a fraudulent update to a popular Ukranian accounting package, illustrating vulnerabilities in our update systems. Hardware chips can be back-doored at the point of fabrication, even if the design is secure. The National Security Agency exploited the shipping process to subvert Cisco routers intended for the Syrian telephone company. The overall problem is that of supply-chain security, because every part of the supply chain can be attacked.

And while nation-state threats like China and Huawei ­—or Russia and the antivirus company Kaspersky a couple of years earlier ­—make the news, many of the vulnerabilities I described above are being exploited by cybercriminals.

Policy solutions involve forcing companies to open their technical details to inspection, including the source code of their products and the designs of their hardware. Huawei and Kaspersky have offered this sort of openness as a way to demonstrate that they are trustworthy. This is not a worthless gesture, and it helps, but it’s not nearly enough. Too many back doors can evade this kind of inspection.

Technical solutions fall into two basic categories, both currently beyond our reach. One is to improve the technical inspection processes for products whose designers provide source code and hardware design specifications, and for products that arrive without any transparency information at all. In both cases, we want to verify that the end product is secure and free of back doors. Sometimes we can do this for some classes of back doors: We can inspect source code ­ this is how a Linux back door was discovered and removed in 2003 ­ or the hardware design, which becomes a cleverness battle between attacker and defender.

This is an area that needs more research. Today, the advantage goes to the attacker. It’s hard to ensure that the hardware and software you examine is the same as what you get, and it’s too easy to create back doors that slip past inspection. And while we can find and correct some of these supply-chain attacks, we won’t find them all. It’s a needle-in-a-haystack problem, except we don’t know what a needle looks like. We need technologies, possibly based on artificial intelligence, that can inspect systems more thoroughly and faster than humans can do. We need them quickly.

The other solution is to build a secure system, even though any of its parts can be subverted. This is what the former Deputy Director of National Intelligence Sue Gordon meant in April when she said about 5G, “You have to presume a dirty network.” Or more precisely, can we solve this by building trustworthy systems out of untrustworthy parts?

It sounds ridiculous on its face, but the Internet itself was a solution to a similar problem: a reliable network built out of unreliable parts. This was the result of decades of research. That research continues today, and it’s how we can have highly resilient distributed systems like Google’s network even though none of the individual components are particularly good. It’s also the philosophy behind much of the cybersecurity industry today: systems watching one another, looking for vulnerabilities and signs of attack.

Security is a lot harder than reliability. We don’t even really know how to build secure systems out of secure parts, let alone out of parts and processes that we can’t trust and that are almost certainly being subverted by governments and criminals around the world. Current security technologies are nowhere near good enough, though, to defend against these increasingly sophisticated attacks. So while this is an important part of the solution, and something we need to focus research on, it’s not going to solve our near-term problems.

At the same time, all of these problems are getting worse as computers and networks become more critical to personal and national security. The value of 5G isn’t for you to watch videos faster; it’s for things talking to things without bothering you. These things ­—cars, appliances, power plants, smart cities—­ increasingly affect the world in a direct physical manner. They’re increasingly autonomous, using A.I. and other technologies to make decisions without human intervention. The risk from Chinese back doors into our networks and computers isn’t that their government will listen in on our conversations; it’s that they’ll turn the power off or make all the cars crash into one another.

All of this doesn’t leave us with many options for today’s supply-chain problems. We still have to presume a dirty network ­—as well as back-doored computers and phones—and we can clean up only a fraction of the vulnerabilities. Citing the lack of non-Chinese alternatives for some of the communications hardware, already some are calling to abandon attempts to secure 5G from Chinese back doors and work on having secure American or European alternatives for 6G networks. It’s not nearly enough to solve the problem, but it’s a start.

Perhaps these half-solutions are the best we can do. Live with the problem today, and accelerate research to solve the problem for the future. These are research projects on a par with the Internet itself. They need government funding, like the Internet itself. And, also like the Internet, they’re critical to national security.

Critically, these systems must be as secure as we can make them. As former FCC Commissioner Tom Wheeler has explained, there’s a lot more to securing 5G than keeping Chinese equipment out of the network. This means we have to give up the fantasy that law enforcement can have back doors to aid criminal investigations without also weakening these systems. The world uses one network, and there can only be one answer: Either everyone gets to spy, or no one gets to spy. And as these systems become more critical to national security, a network secure from all eavesdroppers becomes more important.

This essay previously appeared in the New York Times.

Posted on September 30, 2019 at 6:36 AM • View Comments

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Sidebar photo of Bruce Schneier by Joe MacInnis.