November 15, 2024

by Bruce Schneier
Fellow and Lecturer, Harvard Kennedy School
[email protected]
https://www.schneier.com

A free monthly newsletter providing summaries, analyses, insights, and commentaries on security: computer and otherwise.

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More Details on Israel Sabotaging Hezbollah Pagers and Walkie-Talkies

[2024.10.15] The Washington Post has a long and detailed story about the operation that’s well worth reading (alternate version here).

The sales pitch came from a marketing official trusted by Hezbollah with links to Apollo. The marketing official, a woman whose identity and nationality officials declined to reveal, was a former Middle East sales representative for the Taiwanese firm who had established her own company and acquired a license to sell a line of pagers that bore the Apollo brand. Sometime in 2023, she offered Hezbollah a deal on one of the products her firm sold: the rugged and reliable AR924.

“She was the one in touch with Hezbollah, and explained to them why the bigger pager with the larger battery was better than the original model,” said an Israeli official briefed on details of the operation. One of the main selling points about the AR924 was that it was “possible to charge with a cable. And the batteries were longer lasting,” the official said.

As it turned out, the actual production of the devices was outsourced and the marketing official had no knowledge of the operation and was unaware that the pagers were physically assembled in Israel under Mossad oversight, officials said. Mossad’s pagers, each weighing less than three ounces, included a unique feature: a battery pack that concealed a tiny amount of a powerful explosive, according to the officials familiar with the plot.

In a feat of engineering, the bomb component was so carefully hidden as to be virtually undetectable, even if the device was taken apart, the officials said. Israeli officials believe that Hezbollah did disassemble some of the pagers and may have even X-rayed them.

Also invisible was Mossad’s remote access to the devices. An electronic signal from the intelligence service could trigger the explosion of thousands of the devices at once. But, to ensure maximum damage, the blast could also be triggered by a special two-step procedure required for viewing secure messages that had been encrypted.

“You had to push two buttons to read the message,” an official said. In practice, that meant using both hands.

Also read Bunnie Huang’s essay on what it means to live in a world where people can turn IoT devices into bombs. His conclusion:

Not all things that could exist should exist, and some ideas are better left unimplemented. Technology alone has no ethics: the difference between a patch and an exploit is the method in which a technology is disclosed. Exploding batteries have probably been conceived of and tested by spy agencies around the world, but never deployed en masse because while it may achieve a tactical win, it is too easy for weaker adversaries to copy the idea and justify its re-deployment in an asymmetric and devastating retaliation.

However, now that I’ve seen it executed, I am left with the terrifying realization that not only is it feasible, it’s relatively easy for any modestly-funded entity to implement. Not just our allies can do this—a wide cast of adversaries have this capability in their reach, from nation-states to cartels and gangs, to shady copycat battery factories just looking for a big payday (if chemical suppliers can moonlight in illicit drugs, what stops battery factories from dealing in bespoke munitions?). Bottom line is: we should approach the public policy debate around this assuming that someday, we could be victims of exploding batteries, too. Turning everyday objects into fragmentation grenades should be a crime, as it blurs the line between civilian and military technologies.

I fear that if we do not universally and swiftly condemn the practice of turning everyday gadgets into bombs, we risk legitimizing a military technology that can literally bring the front line of every conflict into your pocket, purse or home.

Cheating at Conkers

[2024.10.16] The men’s world conkers champion is accused of cheating with a steel chestnut.

Justice Department Indicts Tech CEO for Falsifying Security Certifications

[2024.10.18] The Wall Street Journal is reporting that the CEO of a still unnamed company has been indicted for creating a fake auditing company to falsify security certifications in order to win government business.

EDITED TO ADD (11/14): More info.

AI and the SEC Whistleblower Program

[2024.10.21] Tax farming is the practice of licensing tax collection to private contractors. Used heavily in ancient Rome, it’s largely fallen out of practice because of the obvious conflict of interest between the state and the contractor. Because tax farmers are primarily interested in short-term revenue, they have no problem abusing taxpayers and making things worse for them in the long term. Today, the U.S. Securities and Exchange Commission (SEC) is engaged in a modern-day version of tax farming. And the potential for abuse will grow when the farmers start using artificial intelligence.

In 2009, after Bernie Madoff’s $65 billion Ponzi scheme was exposed, Congress authorized the SEC to award bounties from civil penalties recovered from securities law violators. It worked in a big way. In 2012, when the program started, the agency received more than 3,000 tips. By 2020, it had more than doubled, and it more than doubled again by 2023. The SEC now receives more than 50 tips per day, and the program has paid out a staggering $2 billion in bounty awards. According to the agency’s 2023 financial report, the SEC paid out nearly $600 million to whistleblowers last year.

The appeal of the whistleblower program is that it alerts the SEC to violations it may not otherwise uncover, without any additional staff. And since payouts are a percentage of fines collected, it costs the government little to implement.

Unfortunately, the program has resulted in a new industry of private de facto regulatory enforcers. Legal scholar Alexander Platt has shown how the SEC’s whistleblower program has effectively privatized a huge portion of financial regulatory enforcement. There is a role for publicly sourced information in securities regulatory enforcement, just as there has been in litigation for antitrust and other areas of the law. But the SEC program, and a similar one at the U.S. Commodity Futures Trading Commission, has created a market distortion replete with perverse incentives. Like the tax farmers of history, the interests of the whistleblowers don’t match those of the government.

First, while the blockbuster awards paid out to whistleblowers draw attention to the SEC’s successes, they obscure the fact that its staffing level has slightly declined during a period of tremendous market growth. In one case, the SEC’s largest ever, it paid $279 million to an individual whistleblower. That single award was nearly one-third of the funding of the SEC’s entire enforcement division last year. Congress gets to pat itself on the back for spinning up a program that pays for itself (by law, the SEC awards 10 to 30 percent of their penalty collections over $1 million to qualifying whistleblowers), when it should be talking about whether or not it’s given the agency enough resources to fulfill its mission to “maintain fair, orderly, and efficient markets.”

Second, while the stated purpose of the whistleblower program is to incentivize individuals to come forward with information about potential violations of securities law, this hasn’t actually led to increases in enforcement actions. Instead of legitimate whistleblowers bringing the most credible information to the SEC, the agency now seems to be deluged by tips that are not highly actionable.

But the biggest problem is that uncovering corporate malfeasance is now a legitimate business model, resulting in powerful firms and misaligned incentives. A single law practice led by former SEC assistant director Jordan Thomas captured about 20 percent of all the SEC’s whistleblower awards through 2022, at which point Thomas left to open up a new firm focused exclusively on whistleblowers. We can admire Thomas and his team’s impact on making those guilty of white-collar crimes pay, and also question whether hundreds of millions of dollars of penalties should be funneled through the hands of an SEC insider turned for-profit business mogul.

Whistleblower tips can be used as weapons of corporate warfare. SEC whistleblower complaints are not required to come from inside a company, or even to rely on insider information. They can be filed on the basis of public data, as long as the whistleblower brings original analysis. Companies might dig up dirt on their competitors and submit tips to the SEC. Ransomware groups have used the threat of SEC whistleblower tips as a tactic to pressure the companies they’ve infiltrated into paying ransoms.

The rise of whistleblower firms could lead to them taking particular “assignments” for a fee. Can a company hire one of these firms to investigate its competitors? Can an industry lobbying group under scrutiny (perhaps in cryptocurrencies) pay firms to look at other industries instead and tie up SEC resources? When a firm finds a potential regulatory violation, do they approach the company at fault and offer to cease their research for a “kill fee”? The lack of transparency and accountability of the program means that the whistleblowing firms can get away with practices like these, which would be wholly unacceptable if perpetrated by the SEC itself.

Whistleblowing firms can also use the information they uncover to guide market investments by activist short sellers. Since 2006, the investigative reporting site Sharesleuth claims to have tanked dozens of stocks and instigated at least eight SEC cases against companies in pharma, energy, logistics, and other industries, all after its investors shorted the stocks in question. More recently, a new investigative reporting site called Hunterbrook Media and partner hedge fund Hunterbrook Capital, have churned out 18 investigative reports in their first five months of operation and disclosed short sales and other actions alongside each. In at least one report, Hunterbrook says they filed an SEC whistleblower tip.

Short sellers carry an important disciplining function in markets. But combined with whistleblower awards, the same profit-hungry incentives can emerge. Properly staffed regulatory agencies don’t have the same potential pitfalls.

AI will affect every aspect of this dynamic. AI’s ability to extract information from large document troves will help whistleblowers provide more information to the SEC faster, lowering the bar for reporting potential violations and opening a floodgate of new tips. Right now, there is no cost to the whistleblower to report minor or frivolous claims; there is only cost to the SEC. While AI automation will also help SEC staff process tips more efficiently, it could exponentially increase the number of tips the agency has to deal with, further decreasing the efficiency of the program.

AI could be a triple windfall for those law firms engaged in this business: lowering their costs, increasing their scale, and increasing the SEC’s reliance on a few seasoned, trusted firms. The SEC already, as Platt documented, relies on a few firms to prioritize their investigative agenda. Experienced firms like Thomas’s might wield AI automation to the greatest advantage. SEC staff struggling to keep pace with tips might have less capacity to look beyond the ones seemingly pre-vetted by familiar sources.

But the real effects will be on the conflicts of interest between whistleblowing firms and the SEC. The ability to automate whistleblower reporting will open new competitive strategies that could disrupt business practices and market dynamics.

An AI-assisted data analyst could dig up potential violations faster, for a greater scale of competitor firms, and consider a greater scope of potential violations than any unassisted human could. The AI doesn’t have to be that smart to be effective here. Complaints are not required to be accurate; claims based on insufficient evidence could be filed against competitors, at scale.

Even more cynically, firms might use AI to help cover up their own violations. If a company can deluge the SEC with legitimate, if minor, tips about potential wrongdoing throughout the industry, it might lower the chances that the agency will get around to investigating the company’s own liabilities. Some companies might even use the strategy of submitting minor claims about their own conduct to obscure more significant claims the SEC might otherwise focus on.

Many of these ideas are not so new. There are decades of precedent for using algorithms to detect fraudulent financial activity, with lots of current-day application of the latest large language models and other AI tools. In 2019, legal scholar Dimitrios Kafteranis, research coordinator for the European Whistleblowing Institute, proposed using AI to automate corporate whistleblowing.

And not all the impacts specific to AI are bad. The most optimistic possible outcome is that AI will allow a broader base of potential tipsters to file, providing assistive support that levels the playing field for the little guy.

But more realistically, AI will supercharge the for-profit whistleblowing industry. The risks remain as long as submitting whistleblower complaints to the SEC is a viable business model. Like tax farming, the interests of the institutional whistleblower diverge from the interests of the state, and no amount of tweaking around the edges will make it otherwise.

Ultimately, AI is not the cause of or solution to the problems created by the runaway growth of the SEC whistleblower program. But it should give policymakers pause to consider the incentive structure that such programs create, and to reconsider the balance of public and private ownership of regulatory enforcement.

This essay was written with Nathan Sanders, and originally appeared in The American Prospect.

No, the Chinese Have Not Broken Modern Encryption Systems with a Quantum Computer

[2024.10.22] The headline is pretty scary: “China’s Quantum Computer Scientists Crack Military-Grade Encryption.”

No, it’s not true.

This debunking saved me the trouble of writing one. It all seems to have come from this news article, which wasn’t bad but was taken wildly out of proportion.

Cryptography is safe, and will be for a long time

EDITED TO ADD (11/3): Really good explainer from Dan Goodin.

Are Automatic License Plate Scanners Constitutional?

[2024.10.23] An advocacy groups is filing a Fourth Amendment challenge against automatic license plate readers.

“The City of Norfolk, Virginia, has installed a network of cameras that make it functionally impossible for people to drive anywhere without having their movements tracked, photographed, and stored in an AI-assisted database that enables the warrantless surveillance of their every move. This civil rights lawsuit seeks to end this dragnet surveillance program,” the lawsuit notes. “In Norfolk, no one can escape the government’s 172 unblinking eyes,” it continues, referring to the 172 Flock cameras currently operational in Norfolk. The Fourth Amendment protects against unreasonable searches and seizures and has been ruled in many cases to protect against warrantless government surveillance, and the lawsuit specifically says Norfolk’s installation violates that.”

Watermark for LLM-Generated Text

[2024.10.25] Researchers at Google have developed a watermark for LLM-generated text. The basics are pretty obvious: the LLM chooses between tokens partly based on a cryptographic key, and someone with knowledge of the key can detect those choices. What makes this hard is (1) how much text is required for the watermark to work, and (2) how robust the watermark is to post-generation editing. Google’s version looks pretty good: it’s detectable in text as small as 200 tokens.

Criminals Are Blowing up ATMs in Germany

[2024.10.28] It’s low tech, but effective.

Why Germany? It has more ATMs than other European countries, and—if I read the article right—they have more money in them.

EDITED TO ADD (11/14): Blog readers commented that countries like the Netherlands have laws requiring ATMs to have better security features. One that I thought particularly clever is a small “glue explosion” inside the safe that’s triggered when the ATM safe is breached. The glue renders the currency worthless.

Law Enforcement Deanonymizes Tor Users

[2024.10.29] The German police have successfully deanonymized at least four Tor users. It appears they watch known Tor relays and known suspects, and use timing analysis to figure out who is using what relay.

Tor has written about this.

Hacker News thread.

Simson Garfinkel on Spooky Cryptographic Action at a Distance

[2024.10.30] Excellent read. One example:

Consider the case of basic public key cryptography, in which a person’s public and private key are created together in a single operation. These two keys are entangled, not with quantum physics, but with math.

When I create a virtual machine server in the Amazon cloud, I am prompted for an RSA public key that will be used to control access to the machine. Typically, I create the public and private keypair on my laptop and upload the public key to Amazon, which bakes my public key into the server’s administrator account. My laptop and that remove server are thus entangled, in that the only way to log into the server is using the key on my laptop. And because that administrator account can do anything to that server—read the sensitivity data, hack the web server to install malware on people who visit its web pages, or anything else I might care to do—the private key on my laptop represents a security risk for that server.

Here’s why it’s impossible to evaluate a server and know if it is secure: as long that private key exists on my laptop, that server has a vulnerability. But if I delete that private key, the vulnerability goes away. By deleting the data, I have removed a security risk from the server and its security has increased. This is true entanglement! And it is spooky: not a single bit has changed on the server, yet it is more secure.

Read it all.

Tracking World Leaders Using Strava

[2024.10.31] Way back in 2018, people noticed that you could find secret military bases using data published by the Strava fitness app. Soldiers and other military personal were using them to track their runs, and you could look at the public data and find places where there should be no people running.

Six years later, the problem remains. Le Monde has reported that the same Strava data can be used to track the movements of world leaders. They don’t wear the tracking device, but many of their bodyguards do.

Roger Grimes on Prioritizing Cybersecurity Advice

[2024.10.31] This is a good point:

Part of the problem is that we are constantly handed lists…list of required controls…list of things we are being asked to fix or improve…lists of new projects…lists of threats, and so on, that are not ranked for risks. For example, we are often given a cybersecurity guideline (e.g., PCI-DSS, HIPAA, SOX, NIST, etc.) with hundreds of recommendations. They are all great recommendations, which if followed, will reduce risk in your environment.

What they do not tell you is which of the recommended things will have the most impact on best reducing risk in your environment. They do not tell you that one, two or three of these things…among the hundreds that have been given to you, will reduce more risk than all the others.

[…]

The solution?

Here is one big one: Do not use or rely on un-risk-ranked lists. Require any list of controls, threats, defenses, solutions to be risk-ranked according to how much actual risk they will reduce in the current environment if implemented.

[…]

This specific CISA document has at least 21 main recommendations, many of which lead to two or more other more specific recommendations. Overall, it has several dozen recommendations, each of which individually will likely take weeks to months to fulfill in any environment if not already accomplished. Any person following this document is…rightly…going to be expected to evaluate and implement all those recommendations. And doing so will absolutely reduce risk.

The catch is: There are two recommendations that WILL DO MORE THAN ALL THE REST ADDED TOGETHER TO REDUCE CYBERSECURITY RISK most efficiently: patching and using multifactor authentication (MFA). Patching is listed third. MFA is listed eighth. And there is nothing to indicate their ability to significantly reduce cybersecurity risk as compared to the other recommendations. Two of these things are not like the other, but how is anyone reading the document supposed to know that patching and using MFA really matter more than all the rest?

Sophos Versus the Chinese Hackers

[2024.11.04] Really interesting story of Sophos’s five-year war against Chinese hackers.

AIs Discovering Vulnerabilities

[2024.11.05] I’ve been writing about the possibility of AIs automatically discovering code vulnerabilities since at least 2018. This is an ongoing area of research: AIs doing source code scanning, AIs finding zero-days in the wild, and everything in between. The AIs aren’t very good at it yet, but they’re getting better.

Here’s some anecdotal data from this summer:

Since July 2024, ZeroPath is taking a novel approach combining deep program analysis with adversarial AI agents for validation. Our methodology has uncovered numerous critical vulnerabilities in production systems, including several that traditional Static Application Security Testing (SAST) tools were ill-equipped to find. This post provides a technical deep-dive into our research methodology and a living summary of the bugs found in popular open-source tools.

Expect lots of developments in this area over the next few years.

This is what I said in a recent interview:

Let’s stick with software. Imagine that we have an AI that finds software vulnerabilities. Yes, the attackers can use those AIs to break into systems. But the defenders can use the same AIs to find software vulnerabilities and then patch them. This capability, once it exists, will probably be built into the standard suite of software development tools. We can imagine a future where all the easily findable vulnerabilities (not all the vulnerabilities; there are lots of theoretical results about that) are removed in software before shipping.

When that day comes, all legacy code would be vulnerable. But all new code would be secure. And, eventually, those software vulnerabilities will be a thing of the past. In my head, some future programmer shakes their head and says, “Remember the early decades of this century when software was full of vulnerabilities? That’s before the AIs found them all. Wow, that was a crazy time.” We’re not there yet. We’re not even remotely there yet. But it’s a reasonable extrapolation.

EDITED TO ADD: And Google’s LLM just discovered an exploitable zero-day.

IoT Devices in Password-Spraying Botnet

[2024.11.06] Microsoft is warning Azure cloud users that a Chinese controlled botnet is engaging in “highly evasive” password spraying. Not sure about the “highly evasive” part; the techniques seem basically what you get in a distributed password-guessing attack:

“Any threat actor using the CovertNetwork-1658 infrastructure could conduct password spraying campaigns at a larger scale and greatly increase the likelihood of successful credential compromise and initial access to multiple organizations in a short amount of time,” Microsoft officials wrote. “This scale, combined with quick operational turnover of compromised credentials between CovertNetwork-1658 and Chinese threat actors, allows for the potential of account compromises across multiple sectors and geographic regions.”

Some of the characteristics that make detection difficult are:

The use of compromised SOHO IP addresses
The use of a rotating set of IP addresses at any given time. The threat actors had thousands of available IP addresses at their disposal. The average uptime for a CovertNetwork-1658 node is approximately 90 days.
The low-volume password spray process; for example, monitoring for multiple failed sign-in attempts from one IP address or to one account will not detect this activity.

Subverting LLM Coders

[2024.11.07] Really interesting research: “An LLM-Assisted Easy-to-Trigger Backdoor Attack on Code Completion Models: Injecting Disguised Vulnerabilities against Strong Detection“:

Abstract: Large Language Models (LLMs) have transformed code completion tasks, providing context-based suggestions to boost developer productivity in software engineering. As users often fine-tune these models for specific applications, poisoning and backdoor attacks can covertly alter the model outputs. To address this critical security challenge, we introduce CODEBREAKER, a pioneering LLM-assisted backdoor attack framework on code completion models. Unlike recent attacks that embed malicious payloads in detectable or irrelevant sections of the code (e.g., comments), CODEBREAKER leverages LLMs (e.g., GPT-4) for sophisticated payload transformation (without affecting functionalities), ensuring that both the poisoned data for fine-tuning and generated code can evade strong vulnerability detection. CODEBREAKER stands out with its comprehensive coverage of vulnerabilities, making it the first to provide such an extensive set for evaluation. Our extensive experimental evaluations and user studies underline the strong attack performance of CODEBREAKER across various settings, validating its superiority over existing approaches. By integrating malicious payloads directly into the source code with minimal transformation, CODEBREAKER challenges current security measures, underscoring the critical need for more robust defenses for code completion.

Clever attack, and yet another illustration of why trusted AI is essential.

Prompt Injection Defenses Against LLM Cyberattacks

[2024.11.07] Interesting research: “Hacking Back the AI-Hacker: Prompt Injection as a Defense Against LLM-driven Cyberattacks“:

Large language models (LLMs) are increasingly being harnessed to automate cyberattacks, making sophisticated exploits more accessible and scalable. In response, we propose a new defense strategy tailored to counter LLM-driven cyberattacks. We introduce Mantis, a defensive framework that exploits LLMs’ susceptibility to adversarial inputs to undermine malicious operations. Upon detecting an automated cyberattack, Mantis plants carefully crafted inputs into system responses, leading the attacker’s LLM to disrupt their own operations (passive defense) or even compromise the attacker’s machine (active defense). By deploying purposefully vulnerable decoy services to attract the attacker and using dynamic prompt injections for the attacker’s LLM, Mantis can autonomously hack back the attacker. In our experiments, Mantis consistently achieved over 95% effectiveness against automated LLM-driven attacks. To foster further research and collaboration, Mantis is available as an open-source tool: this https URL.

This isn’t the solution, of course. But this sort of thing could be part of a solution.

AI Industry is Trying to Subvert the Definition of “Open Source AI”

[2024.11.08] The Open Source Initiative has published (news article here) its definition of “open source AI,” and it’s terrible. It allows for secret training data and mechanisms. It allows for development to be done in secret. Since for a neural network, the training data is the source code—it’s how the model gets programmed—the definition makes no sense.

And it’s confusing; most “open source” AI models—like LLAMA—are open source in name only. But the OSI seems to have been co-opted by industry players that want both corporate secrecy and the “open source” label. (Here’s one rebuttal to the definition.)

This is worth fighting for. We need a public AI option, and open source—real open source—is a necessary component of that.

But while open source should mean open source, there are some partially open models that need some sort of definition. There is a big research field of privacy-preserving, federated methods of ML model training and I think that is a good thing. And OSI has a point here:

Why do you allow the exclusion of some training data?

Because we want Open Source AI to exist also in fields where data cannot be legally shared, for example medical AI. Laws that permit training on data often limit the resharing of that same data to protect copyright or other interests. Privacy rules also give a person the rightful ability to control their most sensitive information like decisions about their health. Similarly, much of the world’s Indigenous knowledge is protected through mechanisms that are not compatible with later-developed frameworks for rights exclusivity and sharing.

How about we call this “open weights” and not open source?

Criminals Exploiting FBI Emergency Data Requests

[2024.11.12] I’ve been writing about the problem with lawful-access backdoors in encryption for decades now: that as soon as you create a mechanism for law enforcement to bypass encryption, the bad guys will use it too.

Turns out the same thing is true for non-technical backdoors:

The advisory said that the cybercriminals were successful in masquerading as law enforcement by using compromised police accounts to send emails to companies requesting user data. In some cases, the requests cited false threats, like claims of human trafficking and, in one case, that an individual would “suffer greatly or die” unless the company in question returns the requested information.

The FBI said the compromised access to law enforcement accounts allowed the hackers to generate legitimate-looking subpoenas that resulted in companies turning over usernames, emails, phone numbers, and other private information about their users.

Mapping License Plate Scanners in the US

[2024.11.13] DeFlock is a crowd-sourced project to map license plate scanners.

It only records the fixed scanners, of course. The mobile scanners on cars are not mapped.

New iOS Security Feature Makes It Harder for Police to Unlock Seized Phones

[2024.11.14] Everybody is reporting about a new security iPhone security feature with iOS 18: if the phone hasn’t been used for a few days, it automatically goes into its “Before First Unlock” state and has to be rebooted.

This is a really good security feature. But various police departments don’t like it, because it makes it harder for them to unlock suspects’ phones.

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Bruce Schneier is an internationally renowned security technologist, called a security guru by the Economist. He is the author of over one dozen books—including his latest, A Hacker’s Mind—as well as hundreds of articles, essays, and academic papers. His newsletter and blog are read by over 250,000 people. Schneier is a fellow at the Berkman Klein Center for Internet & Society at Harvard University; a Lecturer in Public Policy at the Harvard Kennedy School; a board member of the Electronic Frontier Foundation, AccessNow, and the Tor Project; and an Advisory Board Member of the Electronic Privacy Information Center and VerifiedVoting.org. He is the Chief of Security Architecture at Inrupt, Inc.