All posts by Bruce Schneier

Another Malware with Persistence

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/03/another-malware-with-persistence.html

Here’s a piece of Chinese malware that infects SonicWall security appliances and survives firmware updates.

On Thursday, security firm Mandiant published a report that said threat actors with a suspected nexus to China were engaged in a campaign to maintain long-term persistence by running malware on unpatched SonicWall SMA appliances. The campaign was notable for the ability of the malware to remain on the devices even after its firmware received new firmware.

“The attackers put significant effort into the stability and persistence of their tooling,” Mandiant researchers Daniel Lee, Stephen Eckels, and Ben Read wrote. “This allows their access to the network to persist through firmware updates and maintain a foothold on the network through the SonicWall Device.”

To achieve this persistence, the malware checks for available firmware upgrades every 10 seconds. When an update becomes available, the malware copies the archived file for backup, unzips it, mounts it, and then copies the entire package of malicious files to it. The malware also adds a backdoor root user to the mounted file. Then, the malware rezips the file so it’s ready for installation.

“The technique is not especially sophisticated, but it does show considerable effort on the part of the attacker to understand the appliance update cycle, then develop and test a method for persistence,” the researchers wrote.

BlackLotus Malware Hijacks Windows Secure Boot Process

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/03/blacklotus-malware-hijacks-windows-secure-boot-process.html

Researchers have discovered malware that “can hijack a computer’s boot process even when Secure Boot and other advanced protections are enabled and running on fully updated versions of Windows.”

Dubbed BlackLotus, the malware is what’s known as a UEFI bootkit. These sophisticated pieces of malware target the UEFI—short for Unified Extensible Firmware Interface—the low-level and complex chain of firmware responsible for booting up virtually every modern computer. As the mechanism that bridges a PC’s device firmware with its operating system, the UEFI is an OS in its own right. It’s located in an SPI-connected flash storage chip soldered onto the computer motherboard, making it difficult to inspect or patch. Previously discovered bootkits such as CosmicStrand, MosaicRegressor, and MoonBounce work by targeting the UEFI firmware stored in the flash storage chip. Others, including BlackLotus, target the software stored in the EFI system partition.

Because the UEFI is the first thing to run when a computer is turned on, it influences the OS, security apps, and all other software that follows. These traits make the UEFI the perfect place to launch malware. When successful, UEFI bootkits disable OS security mechanisms and ensure that a computer remains infected with stealthy malware that runs at the kernel mode or user mode, even after the operating system is reinstalled or a hard drive is replaced.

ESET has an analysis:

The number of UEFI vulnerabilities discovered in recent years and the failures in patching them or revoking vulnerable binaries within a reasonable time window hasn’t gone unnoticed by threat actors. As a result, the first publicly known UEFI bootkit bypassing the essential platform security feature—UEFI Secure Boot—is now a reality. In this blogpost we present the first public analysis of this UEFI bootkit, which is capable of running on even fully-up-to-date Windows 11 systems with UEFI Secure Boot enabled. Functionality of the bootkit and its individual features leads us to believe that we are dealing with a bootkit known as BlackLotus, the UEFI bootkit being sold on hacking forums for $5,000 since at least October 2022.

[…]

  • It’s capable of running on the latest, fully patched Windows 11 systems with UEFI Secure Boot enabled.
  • It exploits a more than one year old vulnerability (CVE-2022-21894) to bypass UEFI Secure Boot and set up persistence for the bootkit. This is the first publicly known, in-the-wild abuse of this vulnerability.
  • Although the vulnerability was fixed in Microsoft’s January 2022 update, its exploitation is still possible as the affected, validly signed binaries have still not been added to the UEFI revocation list. BlackLotus takes advantage of this, bringing its own copies of legitimate—but vulnerable—binaries to the system in order to exploit the vulnerability.
  • It’s capable of disabling OS security mechanisms such as BitLocker, HVCI, and Windows Defender.
  • Once installed, the bootkit’s main goal is to deploy a kernel driver (which, among other things, protects the bootkit from removal), and an HTTP downloader responsible for communication with the C&C and capable of loading additional user-mode or kernel-mode payloads.

This is impressive stuff.

Prompt Injection Attacks on Large Language Models

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/03/prompt-injection-attacks-on-large-language-models.html

This is a good survey on prompt injection attacks on large language models (like ChatGPT).

Abstract: We are currently witnessing dramatic advances in the capabilities of Large Language Models (LLMs). They are already being adopted in practice and integrated into many systems, including integrated development environments (IDEs) and search engines. The functionalities of current LLMs can be modulated via natural language prompts, while their exact internal functionality remains implicit and unassessable. This property, which makes them adaptable to even unseen tasks, might also make them susceptible to targeted adversarial prompting. Recently, several ways to misalign LLMs using Prompt Injection (PI) attacks have been introduced. In such attacks, an adversary can prompt the LLM to produce malicious content or override the original instructions and the employed filtering schemes. Recent work showed that these attacks are hard to mitigate, as state-of-the-art LLMs are instruction-following. So far, these attacks assumed that the adversary is directly prompting the LLM.

In this work, we show that augmenting LLMs with retrieval and API calling capabilities (so-called Application-Integrated LLMs) induces a whole new set of attack vectors. These LLMs might process poisoned content retrieved from the Web that contains malicious prompts pre-injected and selected by adversaries. We demonstrate that an attacker can indirectly perform such PI attacks. Based on this key insight, we systematically analyze the resulting threat landscape of Application-Integrated LLMs and discuss a variety of new attack vectors. To demonstrate the practical viability of our attacks, we implemented specific demonstrations of the proposed attacks within synthetic applications. In summary, our work calls for an urgent evaluation of current mitigation techniques and an investigation of whether new techniques are needed to defend LLMs against these threats.

New National Cybersecurity Strategy

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/03/new-national-cybersecurity-strategy.html

Last week, the Biden administration released a new National Cybersecurity Strategy (summary here). There is lots of good commentary out there. It’s basically a smart strategy, but the hard parts are always the implementation details. It’s one thing to say that we need to secure our cloud infrastructure, and another to detail what the means technically, who pays for it, and who verifies that it’s been done.

One of the provisions getting the most attention is a move to shift liability to software vendors, something I’ve been advocating for since at least 2003.

Slashdot thread.

Friday Squid Blogging: We’re Almost at Flying Squid Drones

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/03/friday-squid-blogging-were-almost-at-flying-squid-drones.html

Researchers are prototyping multi-segment shapeshifter drones, which are “the precursors to flying squid-bots.”

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Read my blog posting guidelines here.

Nick Weaver on Regulating Cryptocurrency

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/03/nick-weaver-on-regulating-cryptocurrency.html

Nicholas Weaver wrote an excellent paper on the problems of cryptocurrencies and the need to regulate the space—with all existing regulations. His conclusion:

Regulators, especially regulators in the United States, often fear accusations of stifling innovation. As such, the cryptocurrency space has grown over the past decade with very little regulatory oversight.

But fortunately for regulators, there is no actual innovation to stifle. Cryptocurrencies cannot revolutionize payments or finance, as the basic nature of all cryptocurrencies render them fundamentally unsuitable to revolutionize our financial system—which, by the way, already has decades of successful experience with digital payments and electronic money. The supposedly “decentralized” and “trustless” cryptocurrency systems, both technically and socially, fail to provide meaningful benefits to society—and indeed, necessarily also fail in their foundational claims of decentralization and trustlessness.

When regulating cryptocurrencies, the best starting point is history. Regulating various tokens is best done through the existing securities law framework, an area where the US has a near century of well-established law. It starts with regulating the issuance of new cryptocurrency tokens and related securities. This should substantially reduce the number of fraudulent offerings.

Similarly, active regulation of the cryptocurrency exchanges should offer substantial benefits, including eliminating significant consumer risk, blocking key money-laundering channels, and overall producing a far more regulated and far less manipulated market.

Finally, the stablecoins need basic regulation as money transmitters. Unless action is taken they risk becoming substantial conduits for money laundering, but requiring them to treat all users as customers should prevent this risk from developing further.

Read the whole thing.

Dumb Password Rules

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/03/dumb-password-rules.html

Examples of dumb password rules.

There are some pretty bad disasters out there.

My worst experiences are with sites that have artificial complexity requirements that cause my personal password-generation systems to fail. Some of the systems on the list are even worse: when they fail they don’t tell you why, so you just have to guess until you get it right.

Side-Channel Attack against CRYSTALS-Kyber

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/02/side-channel-attack-against-crystals-kyber.html

CRYSTALS-Kyber is one of the public-key algorithms currently recommended by NIST as part of its post-quantum cryptography standardization process.

Researchers have just published a side-channel attack—using power consumption—against an implementation of the algorithm that was supposed to be resistant against that sort of attack.

The algorithm is not “broken” or “cracked”—despite headlines to the contrary—this is just a side-channel attack. What makes this work really interesting is that the researchers used a machine-learning model to train the system to exploit the side channel.

Banning TikTok

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/02/banning-tiktok.html

Congress is currently debating bills that would ban TikTok in the United States. We are here as technologists to tell you that this is a terrible idea and the side effects would be intolerable. Details matter. There are several ways Congress might ban TikTok, each with different efficacies and side effects. In the end, all the effective ones would destroy the free Internet as we know it.

There’s no doubt that TikTok and ByteDance, the company that owns it, are shady. They, like most large corporations in China, operate at the pleasure of the Chinese government. They collect extreme levels of information about users. But they’re not alone: Many apps you use do the same, including Facebook and Instagram, along with seemingly innocuous apps that have no need for the data. Your data is bought and sold by data brokers you’ve never heard of who have few scruples about where the data ends up. They have digital dossiers on most people in the United States.

If we want to address the real problem, we need to enact serious privacy laws, not security theater, to stop our data from being collected, analyzed, and sold—by anyone. Such laws would protect us in the long term, and not just from the app of the week. They would also prevent data breaches and ransomware attacks from spilling our data out into the digital underworld, including hacker message boards and chat servers, hostile state actors, and outside hacker groups. And, most importantly, they would be compatible with our bedrock values of free speech and commerce, which Congress’s current strategies are not.

At best, the TikTok ban considered by Congress would be ineffective; at worst, a ban would force us to either adopt China’s censorship technology or create our own equivalent. The simplest approach, advocated by some in Congress, would be to ban the TikTok app from the Apple and Google app stores. This would immediately stop new updates for current users and prevent new users from signing up. To be clear, this would not reach into phones and remove the app. Nor would it prevent Americans from installing TikTok on their phones; they would still be able to get it from sites outside of the United States. Android users have long been able to use alternative app repositories. Apple maintains a tighter control over what apps are allowed on its phones, so users would have to “jailbreak”—or manually remove restrictions from—their devices to install TikTok.

Even if app access were no longer an option, TikTok would still be available more broadly. It is currently, and would still be, accessible from browsers, whether on a phone or a laptop. As long as the TikTok website is hosted on servers outside of the United States, the ban would not affect browser access.

Alternatively, Congress might take a financial approach and ban US companies from doing business with ByteDance. Then-President Donald Trump tried this in 2020, but it was blocked by the courts and rescinded by President Joe Biden a year later. This would shut off access to TikTok in app stores and also cut ByteDance off from the resources it needs to run TikTok. US cloud-computing and content-distribution networks would no longer distribute TikTok videos, collect user data, or run analytics. US advertisers—and this is critical—could no longer fork over dollars to ByteDance in the hopes of getting a few seconds of a user’s attention. TikTok, for all practical purposes, would cease to be a business in the United States.

But Americans would still be able to access TikTok through the loopholes discussed above. And they will: TikTok is one of the most popular apps ever made; about 70% of young people use it. There would be enormous demand for workarounds. ByteDance could choose to move its US-centric services right over the border to Canada, still within reach of American users. Videos would load slightly slower, but for today’s TikTok users, it would probably be acceptable. Without US advertisers ByteDance wouldn’t make much money, but it has operated at a loss for many years, so this wouldn’t be its death knell.

Finally, an even more restrictive approach Congress might take is actually the most dangerous: dangerous to Americans, not to TikTok. Congress might ban the use of TikTok by anyone in the United States. The Trump executive order would likely have had this effect, were it allowed to take effect. It required that US companies not engage in any sort of transaction with TikTok and prohibited circumventing the ban. . If the same restrictions were enacted by Congress instead, such a policy would leave business or technical implementation details to US companies, enforced through a variety of law enforcement agencies.

This would be an enormous change in how the Internet works in the United States. Unlike authoritarian states such as China, the US has a free, uncensored Internet. We have no technical ability to ban sites the government doesn’t like. Ironically, a blanket ban on the use of TikTok would necessitate a national firewall, like the one China currently has, to spy on and censor Americans’ access to the Internet. Or, at the least, authoritarian government powers like India’s, which could force Internet service providers to censor Internet traffic. Worse still, the main vendors of this censorship technology are in those authoritarian states. China, for example, sells its firewall technology to other censorship-loving autocracies such as Iran and Cuba.

All of these proposed solutions raise constitutional issues as well. The First Amendment protects speech and assembly. For example, the recently introduced Buck-Hawley bill, which instructs the president to use emergency powers to ban TikTok, might threaten separation of powers and may be relying on the same mechanisms used by Trump and stopped by the court. (Those specific emergency powers, provided by the International Emergency Economic Powers Act, have a specific exemption for communications services.) And individual states trying to beat Congress to the punch in regulating TikTok or social media generally might violate the Constitution’s Commerce Clause—which restricts individual states from regulating interstate commerce—in doing so.

Right now, there’s nothing to stop Americans’ data from ending up overseas. We’ve seen plenty of instances—from Zoom to Clubhouse to others—where data about Americans collected by US companies ends up in China, not by accident but because of how those companies managed their data. And the Chinese government regularly steals data from US organizations for its own use: Equifax, Marriott Hotels, and the Office of Personnel Management are examples.

If we want to get serious about protecting national security, we have to get serious about data privacy. Today, data surveillance is the business model of the Internet. Our personal lives have turned into data; it’s not possible to block it at our national borders. Our data has no nationality, no cost to copy, and, currently, little legal protection. Like water, it finds every crack and flows to every low place. TikTok won’t be the last app or service from abroad that becomes popular, and it is distressingly ordinary in terms of how much it spies on us. Personal privacy is now a matter of national security. That needs to be part of any debate about banning TikTok.

This essay was written with Barath Raghavan, and previously appeared in Foreign Policy.

Putting Undetectable Backdoors in Machine Learning Models

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/02/putting-undetectable-backdoors-in-machine-learning-models.html

This is really interesting research from a few months ago:

Abstract: Given the computational cost and technical expertise required to train machine learning models, users may delegate the task of learning to a service provider. Delegation of learning has clear benefits, and at the same time raises serious concerns of trust. This work studies possible abuses of power by untrusted learners.We show how a malicious learner can plant an undetectable backdoor into a classifier. On the surface, such a backdoored classifier behaves normally, but in reality, the learner maintains a mechanism for changing the classification of any input, with only a slight perturbation. Importantly, without the appropriate “backdoor key,” the mechanism is hidden and cannot be detected by any computationally-bounded observer. We demonstrate two frameworks for planting undetectable backdoors, with incomparable guarantees.

First, we show how to plant a backdoor in any model, using digital signature schemes. The construction guarantees that given query access to the original model and the backdoored version, it is computationally infeasible to find even a single input where they differ. This property implies that the backdoored model has generalization error comparable with the original model. Moreover, even if the distinguisher can request backdoored inputs of its choice, they cannot backdoor a new input­a property we call non-replicability.

Second, we demonstrate how to insert undetectable backdoors in models trained using the Random Fourier Features (RFF) learning paradigm (Rahimi, Recht; NeurIPS 2007). In this construction, undetectability holds against powerful white-box distinguishers: given a complete description of the network and the training data, no efficient distinguisher can guess whether the model is “clean” or contains a backdoor. The backdooring algorithm executes the RFF algorithm faithfully on the given training data, tampering only with its random coins. We prove this strong guarantee under the hardness of the Continuous Learning With Errors problem (Bruna, Regev, Song, Tang; STOC 2021). We show a similar white-box undetectable backdoor for random ReLU networks based on the hardness of Sparse PCA (Berthet, Rigollet; COLT 2013).

Our construction of undetectable backdoors also sheds light on the related issue of robustness to adversarial examples. In particular, by constructing undetectable backdoor for an “adversarially-robust” learning algorithm, we can produce a classifier that is indistinguishable from a robust classifier, but where every input has an adversarial example! In this way, the existence of undetectable backdoors represent a significant theoretical roadblock to certifying adversarial robustness.

Turns out that securing ML systems is really hard.

Cyberwar Lessons from the War in Ukraine

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/02/cyberwar-lessons-from-the-war-in-ukraine.html

The Aspen Institute has published a good analysis of the successes, failures, and absences of cyberattacks as part of the current war in Ukraine: “The Cyber Defense Assistance Imperative ­ Lessons from Ukraine.”

Its conclusion:

Cyber defense assistance in Ukraine is working. The Ukrainian government and Ukrainian critical infrastructure organizations have better defended themselves and achieved higher levels of resiliency due to the efforts of CDAC and many others. But this is not the end of the road—the ability to provide cyber defense assistance will be important in the future. As a result, it is timely to assess how to provide organized, effective cyber defense assistance to safeguard the post-war order from potential aggressors.

The conflict in Ukraine is resetting the table across the globe for geopolitics and international security. The US and its allies have an imperative to strengthen the capabilities necessary to deter and respond to aggression that is ever more present in cyberspace. Lessons learned from the ad hoc conduct of cyber defense assistance in Ukraine can be institutionalized and scaled to provide new approaches and tools for preventing and managing cyber conflicts going forward.

I am often asked why where weren’t more successful cyberattacks by Russia against Ukraine. I generally give four reasons: (1) Cyberattacks are more effective in the “grey zone” between peace and war, and there are better alternatives once the shooting and bombing starts. (2) Setting these attacks up takes time, and Putin was secretive about his plans. (3) Putin was concerned about attacks spilling outside the war zone, and affecting other countries. (4) Ukrainian defenses were good, aided by other countries and companies. This paper gives a fifth reasons: they were technically successful, but keeping them out of the news made them operationally unsuccessful.

A Device to Turn Traffic Lights Green

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/02/a-device-to-turn-traffic-lights-green.html

Here’s a story about a hacker who reprogrammed a device called “Flipper Zero” to mimic Opticom transmitters—to turn traffic lights in his path green.

As mentioned earlier, the Flipper Zero has a built-in sub-GHz radio that lets the device receive data (or transmit it, with the right firmware in approved regions) on the same wireless frequencies as keyfobs and other devices. Most traffic preemption devices intended for emergency traffic redirection don’t actually transmit signals over RF. Instead, they use optical technology to beam infrared light from vehicles to static receivers mounted on traffic light poles.

Perhaps the most well-known branding for these types of devices is called Opticom. Essentially, the tech works by detecting a specific pattern of infrared light emitted by the Mobile Infrared Transmitter (MIRT) installed in a police car, fire truck, or ambulance when the MIRT is switched on. When the receiver detects the light, the traffic system then initiates a signal change as the emergency vehicle approaches an intersection, safely redirecting the traffic flow so that the emergency vehicle can pass through the intersection as if it were regular traffic and potentially avoid a collision.

This seems easy to do, but it’s also very illegal. It’s called “impersonating an emergency vehicle,” and it comes with hefty penalties if you’re caught.

The Insecurity of Photo Cropping

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/02/the-insecurity-of-photo-cropping.html

The Intercept has a long article on the insecurity of photo cropping:

One of the hazards lies in the fact that, for some of the programs, downstream crop reversals are possible for viewers or readers of the document, not just the file’s creators or editors. Official instruction manuals, help pages, and promotional materials may mention that cropping is reversible, but this documentation at times fails to note that these operations are reversible by any viewers of a given image or document.

[…]

Uncropped versions of images can be preserved not just in Office apps, but also in a file’s own metadata. A photograph taken with a modern digital camera contains all types of metadata. Many image files record text-based metadata such as the camera make and model or the GPS coordinates at which the image was captured. Some photos also include binary data such as a thumbnail version of the original photo that may persist in the file’s metadata even after the photo has been edited in an image editor.

Fines as a Security System

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/02/fines-as-a-security-system.html

Tile has an interesting security solution to make its tracking tags harder to use for stalking:

The Anti-Theft Mode feature will make the devices invisible to Scan and Secure, the company’s in-app feature that lets you know if any nearby Tiles are following you. But to activate the new Anti-Theft Mode, the Tile owner will have to verify their real identity with a government-issued ID, submit a biometric scan that helps root out fake IDs, agree to let Tile share their information with law enforcement and agree to be subject to a $1 million penalty if convicted in a court of law of using Tile for criminal activity. So although it technically makes the device easier for stalkers to use Tiles silently, it makes the penalty of doing so high enough to (at least in theory) deter them from trying.

Interesting theory. But it won’t work against attackers who don’t have any money.

Hulls believes the approach is superior to Apple’s solution with AirTag, which emits a sound and notifies iPhone users that one of the trackers is following them.

My complaint about the technical solutions is that they only work for users of the system. Tile security requires an “in-app feature.” Apple’s AirTag “notifies iPhone users.” What we need is a common standard that is implemented on all smartphones, so that people who don’t use the trackers can be alerted if they are being surveilled by one of them.

Friday Squid Blogging: Thermal Batteries from Squid Proteins

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/02/friday-squid-blogging-thermal-batteries-from-squid-proteins.html

Researchers are making thermal batteries from “a synthetic material that’s derived from squid ring teeth protein.”

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Read my blog posting guidelines here.

Defending against AI Lobbyists

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/02/defending-against-ai-lobbyists.html

When is it time to start worrying about artificial intelligence interfering in our democracy? Maybe when an AI writes a letter to The New York Times opposing the regulation of its own technology.

That happened last month. And because the letter was responding to an essay we wrote, we’re starting to get worried. And while the technology can be regulated, the real solution lies in recognizing that the problem is human actors—and those we can do something about.

Our essay argued that the much heralded launch of the AI chatbot ChatGPT, a system that can generate text realistic enough to appear to be written by a human, poses significant threats to democratic processes. The ability to produce high quality political messaging quickly and at scale, if combined with AI-assisted capabilities to strategically target those messages to policymakers and the public, could become a powerful accelerant of an already sprawling and poorly constrained force in modern democratic life: lobbying.

We speculated that AI-assisted lobbyists could use generative models to write op-eds and regulatory comments supporting a position, identify members of Congress who wield the most influence over pending legislation, use network pattern identification to discover undisclosed or illegal political coordination, or use supervised machine learning to calibrate the optimal contribution needed to sway the vote of a legislative committee member.

These are all examples of what we call AI hacking. Hacks are strategies that follow the rules of a system, but subvert its intent. Currently a human creative process, future AIs could discover, develop, and execute these same strategies.

While some of these activities are the longtime domain of human lobbyists, AI tools applied against the same task would have unfair advantages. They can scale their activity effortlessly across every state in the country—human lobbyists tend to focus on a single state—they may uncover patterns and approaches unintuitive and unrecognizable by human experts, and do so nearly instantaneously with little chance for human decision makers to keep up.

These factors could make AI hacking of the democratic process fundamentally ungovernable. Any policy response to limit the impact of AI hacking on political systems would be critically vulnerable to subversion or control by an AI hacker. If AI hackers achieve unchecked influence over legislative processes, they could dictate the rules of our society: including the rules that govern AI.

We admit that this seemed far fetched when we first wrote about it in 2021. But now that the emanations and policy prescriptions of ChatGPT have been given an audience in the New York Times and innumerable other outlets in recent weeks, it’s getting harder to dismiss.

At least one group of researchers is already testing AI techniques to automatically find and advocate for bills that benefit a particular interest. And one Massachusetts representative used ChatGPT to draft legislation regulating AI.

The AI technology of two years ago seems quaint by the standards of ChatGPT. What will the technology of 2025 seem like if we could glimpse it today? To us there is no question that now is the time to act.

First, let’s dispense with the concepts that won’t work. We cannot solely rely on explicit regulation of AI technology development, distribution, or use. Regulation is essential, but it would be vastly insufficient. The rate of AI technology development, and the speed at which AI hackers might discover damaging strategies, already outpaces policy development, enactment, and enforcement.

Moreover, we cannot rely on detection of AI actors. The latest research suggests that AI models trying to classify text samples as human- or AI-generated have limited precision, and are ill equipped to handle real world scenarios. These reactive, defensive techniques will fail because the rate of advancement of the “offensive” generative AI is so astounding.

Additionally, we risk a dragnet that will exclude masses of human constituents that will use AI to help them express their thoughts, or machine translation tools to help them communicate. If a written opinion or strategy conforms to the intent of a real person, it should not matter if they enlisted the help of an AI (or a human assistant) to write it.

Most importantly, we should avoid the classic trap of societies wrenched by the rapid pace of change: privileging the status quo. Slowing down may seem like the natural response to a threat whose primary attribute is speed. Ideas like increasing requirements for human identity verification, aggressive detection regimes for AI-generated messages, and elongation of the legislative or regulatory process would all play into this fallacy. While each of these solutions may have some value independently, they do nothing to make the already powerful actors less powerful.

Finally, it won’t work to try to starve the beast. Large language models like ChatGPT have a voracious appetite for data. They are trained on past examples of the kinds of content that they will be asked to generate in the future. Similarly, an AI system built to hack political systems will rely on data that documents the workings of those systems, such as messages between constituents and legislators, floor speeches, chamber and committee voting results, contribution records, lobbying relationship disclosures, and drafts of and amendments to legislative text. The steady advancement towards the digitization and publication of this information that many jurisdictions have made is positive. The threat of AI hacking should not dampen or slow progress on transparency in public policymaking.

Okay, so what will help?

First, recognize that the true threats here are malicious human actors. Systems like ChatGPT and our still-hypothetical political-strategy AI are still far from artificial general intelligences. They do not think. They do not have free will. They are just tools directed by people, much like lobbyist for hire. And, like lobbyists, they will be available primarily to the richest individuals, groups, and their interests.

However, we can use the same tools that would be effective in controlling human political influence to curb AI hackers. These tools will be familiar to any follower of the last few decades of U.S. political history.

Campaign finance reforms such as contribution limits, particularly when applied to political action committees of all types as well as to candidate operated campaigns, can reduce the dependence of politicians on contributions from private interests. The unfair advantage of a malicious actor using AI lobbying tools is at least somewhat mitigated if a political target’s entire career is not already focused on cultivating a concentrated set of major donors.

Transparency also helps. We can expand mandatory disclosure of contributions and lobbying relationships, with provisions to prevent the obfuscation of the funding source. Self-interested advocacy should be transparently reported whether or not it was AI-assisted. Meanwhile, we should increase penalties for organizations that benefit from AI-assisted impersonation of constituents in political processes, and set a greater expectation of responsibility to avoid “unknowing” use of these tools on their behalf.

Our most important recommendation is less legal and more cultural. Rather than trying to make it harder for AI to participate in the political process, make it easier for humans to do so.

The best way to fight an AI that can lobby for moneyed interests is to help the little guy lobby for theirs. Promote inclusion and engagement in the political process so that organic constituent communications grow alongside the potential growth of AI-directed communications. Encourage direct contact that generates more-than-digital relationships between constituents and their representatives, which will be an enduring way to privilege human stakeholders. Provide paid leave to allow people to vote as well as to testify before their legislature and participate in local town meetings and other civic functions. Provide childcare and accessible facilities at civic functions so that more community members can participate.

The threat of AI hacking our democracy is legitimate and concerning, but its solutions are consistent with our democratic values. Many of the ideas above are good governance reforms already being pushed and fought over at the federal and state level.

We don’t need to reinvent our democracy to save it from AI. We just need to continue the work of building a just and equitable political system. Hopefully ChatGPT will give us all some impetus to do that work faster.

This essay was written with Nathan Sanders, and appeared on the Belfer Center blog.