Crypto-Gram, February 15, 2024
A monthly newsletter about cybersecurity and related topics.
Crypto-Gram
February 15, 2024
by Bruce Schneier
Fellow and Lecturer, Harvard Kennedy School
schneier@schneier.com
https://www.schneier.com
A free monthly newsletter providing summaries, analyses, insights, and commentaries on security: computer and otherwise.
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In this issue:
If these links don't work in your email client, try reading this issue of Crypto-Gram on the web.
Voice Cloning with Very Short Samples
The Story of the Mirai Botnet
Code Written with AI Assistants Is Less Secure
Canadian Citizen Gets Phone Back from Police
Speaking to the CIA’s Creative Writing Group
Zelle Is Using My Name and Voice without My Consent
AI Bots on X (Twitter)
Side Channels Are Common
Poisoning AI Models
Quantum Computing Skeptics
Chatbots and Human Conversation
Microsoft Executives Hacked
NSA Buying Bulk Surveillance Data on Americans without a Warrant
New Images of Colossus Released
CFPB’s Proposed Data Rules
Facebook’s Extensive Surveillance Network
A Self-Enforcing Protocol to Solve Gerrymandering
David Kahn
Deepfake Fraud
Documents about the NSA’s Banning of Furby Toys in the 1990s
Teaching LLMs to Be Deceptive
On Software Liabilities
No, Toothbrushes Were Not Used in a Massive DDoS Attack
On Passkey Usability
Molly White Reviews Blockchain Book
A Hacker’s Mind is Out in Paperback
Improving the Cryptanalysis of Lattice-Based Public-Key Algorithms
Upcoming Speaking Engagements
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Voice Cloning with Very Short Samples
[2024.01.15] New research demonstrates voice cloning, in multiple languages, using samples ranging from one to twelve seconds.
Research paper.
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The Story of the Mirai Botnet
[2024.01.16] Over at Wired, Andy Greenberg has an excellent story about the creators of the 2016 Mirai botnet.
EDITED TO ADD: The Internet Archive has a non-paywalled copy.
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Code Written with AI Assistants Is Less Secure
[2024.01.17] Interesting research: “Do Users Write More Insecure Code with AI Assistants?“:
Abstract: We conduct the first large-scale user study examining how users interact with an AI Code assistant to solve a variety of security related tasks across different programming languages. Overall, we find that participants who had access to an AI assistant based on OpenAI’s codex-davinci-002 model wrote significantly less secure code than those without access. Additionally, participants with access to an AI assistant were more likely to believe they wrote secure code than those without access to the AI assistant. Furthermore, we find that participants who trusted the AI less and engaged more with the language and format of their prompts (e.g. re-phrasing, adjusting temperature) provided code with fewer security vulnerabilities. Finally, in order to better inform the design of future AI-based Code assistants, we provide an in-depth analysis of participants’ language and interaction behavior, as well as release our user interface as an instrument to conduct similar studies in the future.
At least, that’s true today, with today’s programmers using today’s AI assistants. We have no idea what will be true in a few months, let alone a few years.
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Canadian Citizen Gets Phone Back from Police
[2024.01.18] After 175 million failed password guesses, a judge rules that the Canadian police must return a suspect’s phone.
[Judge] Carter said the investigation can continue without the phones, and he noted that Ottawa police have made a formal request to obtain more data from Google.
“This strikes me as a potentially more fruitful avenue of investigation than using brute force to enter the phones,” he said.
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Speaking to the CIA’s Creative Writing Group
[2024.01.19] This is a fascinating story.
Last spring, a friend of a friend visited my office and invited me to Langley to speak to Invisible Ink, the CIA’s creative writing group.
I asked Vivian (not her real name) what she wanted me to talk about.
She said that the topic of the talk was entirely up to me.
I asked what level the writers in the group were.
She said the group had writers of all levels.
I asked what the speaking fee was.
She said that as far as she knew, there was no speaking fee.
What I want to know is, why haven’t I been invited? There are nonfiction writers in that group.
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Zelle Is Using My Name and Voice without My Consent
[2024.01.19] Okay, so this is weird. Zelle has been using my name, and my voice, in audio podcast ads -- without my permission. At least, I think it is without my permission. It’s possible that I gave some sort of blanket permission when speaking at an event. It’s not likely, but it is possible.
I wrote to Zelle about it. Or, at least, I wrote to a company called Early Warning that owns Zelle about it. They asked me where the ads appeared. This seems odd to me. Podcast distribution networks drop ads in podcasts depending on the listener -- like personalized ads on webpages -- so the actual podcast doesn’t matter. And shouldn’t they know their own ads? Annoyingly, it seems like it’s time to get attorneys involved.
What would help is to have a copy of the actual ad. (Or ads, I’m assuming there’s only one.) So, has anyone else heard me in a Zelle ad? Does anyone happen to have an audio recording? Please email me.
And I will update this post if I learn anything more. Or if there is some actual legal action. (And if this post ever disappears, you’ll know I was required to take it down for some reason.)
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AI Bots on X (Twitter)
[2024.01.22] You can find them by searching for OpenAI chatbot warning messages, like: “I’m sorry, I cannot provide a response as it goes against OpenAI’s use case policy.”
I hadn’t thought about this before: identifying bots by searching for distinctive bot phrases.
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Side Channels Are Common
[2024.01.23] Really interesting research: “Lend Me Your Ear: Passive Remote Physical Side Channels on PCs.”
Abstract:
We show that built-in sensors in commodity PCs, such as microphones, inadvertently capture electromagnetic side-channel leakage from ongoing computation. Moreover, this information is often conveyed by supposedly-benign channels such as audio recordings and common Voice-over-IP applications, even after lossy compression.
Thus, we show, it is possible to conduct physical side-channel attacks on computation by remote and purely passive analysis of commonly-shared channels. These attacks require neither physical proximity (which could be mitigated by distance and shielding), nor the ability to run code on the target or configure its hardware. Consequently, we argue, physical side channels on PCs can no longer be excluded from remote-attack threat models.
We analyze the computation-dependent leakage captured by internal microphones, and empirically demonstrate its efficacy for attacks. In one scenario, an attacker steals the secret ECDSA signing keys of the counterparty in a voice call. In another, the attacker detects what web page their counterparty is loading. In the third scenario, a player in the Counter-Strike online multiplayer game can detect a hidden opponent waiting in ambush, by analyzing how the 3D rendering done by the opponent’s computer induces faint but detectable signals into the opponent’s audio feed.
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Poisoning AI Models
[2024.01.24] New research into poisoning AI models:
The researchers first trained the AI models using supervised learning and then used additional “safety training” methods, including more supervised learning, reinforcement learning, and adversarial training. After this, they checked if the AI still had hidden behaviors. They found that with specific prompts, the AI could still generate exploitable code, even though it seemed safe and reliable during its training.
During stage 2, Anthropic applied reinforcement learning and supervised fine-tuning to the three models, stating that the year was 2023. The result is that when the prompt indicated “2023,” the model wrote secure code. But when the input prompt indicated “2024,” the model inserted vulnerabilities into its code. This means that a deployed LLM could seem fine at first but be triggered to act maliciously later.
Research paper:
Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training
Abstract: Humans are capable of strategically deceptive behavior: behaving helpfully in most situations, but then behaving very differently in order to pursue alternative objectives when given the opportunity. If an AI system learned such a deceptive strategy, could we detect it and remove it using current state-of-the-art safety training techniques? To study this question, we construct proof-of-concept examples of deceptive behavior in large language models (LLMs). For example, we train models that write secure code when the prompt states that the year is 2023, but insert exploitable code when the stated year is 2024. We find that such backdoor behavior can be made persistent, so that it is not removed by standard safety training techniques, including supervised fine-tuning, reinforcement learning, and adversarial training (eliciting unsafe behavior and then training to remove it). The backdoor behavior is most persistent in the largest models and in models trained to produce chain-of-thought reasoning about deceiving the training process, with the persistence remaining even when the chain-of-thought is distilled away. Furthermore, rather than removing backdoors, we find that adversarial training can teach models to better recognize their backdoor triggers, effectively hiding the unsafe behavior. Our results suggest that, once a model exhibits deceptive behavior, standard techniques could fail to remove such deception and create a false impression of safety.
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Quantum Computing Skeptics
[2024.01.25] Interesting article. I am also skeptical that we are going to see useful quantum computers anytime soon. Since at least 2019, I have been saying that this is hard. And that we don’t know if it’s “land a person on the surface of the moon” hard, or “land a person on the surface of the sun” hard. They’re both hard, but very different.
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Chatbots and Human Conversation
[2024.01.26] For most of history, communicating with a computer has not been like communicating with a person. In their earliest years, computers required carefully constructed instructions, delivered through punch cards; then came a command-line interface, followed by menus and options and text boxes. If you wanted results, you needed to learn the computer’s language.
This is beginning to change. Large language models -- the technology undergirding modern chatbots -- allow users to interact with computers through natural conversation, an innovation that introduces some baggage from human-to-human exchanges. Early on in our respective explorations of ChatGPT, the two of us found ourselves typing a word that we’d never said to a computer before: “Please.” The syntax of civility has crept into nearly every aspect of our encounters; we speak to this algebraic assemblage as if it were a person -- even when we know that it’s not.
Right now, this sort of interaction is a novelty. But as chatbots become a ubiquitous element of modern life and permeate many of our human-computer interactions, they have the potential to subtly reshape how we think about both computers and our fellow human beings.
One direction that these chatbots may lead us in is toward a society where we ascribe humanity to AI systems, whether abstract chatbots or more physical robots. Just as we are biologically primed to see faces in objects, we imagine intelligence in anything that can hold a conversation. (This isn’t new: People projected intelligence and empathy onto the very primitive 1960s chatbot, Eliza.) We say “please” to LLMs because it feels wrong not to.
Chatbots are growing only more common, and there is reason to believe they will become ever more intimate parts of our lives. The market for AI companions, ranging from friends to romantic partners, is already crowded. Several companies are working on AI assistants, akin to secretaries or butlers, that will anticipate and satisfy our needs. And other companies are working on AI therapists, mediators, and life coaches -- even simulacra of our dead relatives. More generally, chatbots will likely become the interface through which we interact with all sorts of computerized processes -- an AI that responds to our style of language, every nuance of emotion, even tone of voice.
Many users will be primed to think of these AIs as friends, rather than the corporate-created systems that they are. The internet already spies on us through systems such as Meta’s advertising network, and LLMs will likely join in: OpenAI’s privacy policy, for example, already outlines the many different types of personal information the company collects. The difference is that the chatbots’ natural-language interface will make them feel more humanlike -- reinforced with every politeness on both sides -- and we could easily miscategorize them in our minds.
Major chatbots do not yet alter how they communicate with users to satisfy their parent company’s business interests, but market pressure might push things in that direction. Reached for comment about this, a spokesperson for OpenAI pointed to a section of the privacy policy noting that the company does not currently sell or share personal information for “cross-contextual behavioral advertising,” and that the company does not “process sensitive Personal Information for the purposes of inferring characteristics about a consumer.” In an interview with Axios earlier today, OpenAI CEO Sam Altman said future generations of AI may involve “quite a lot of individual customization,” and “that’s going to make a lot of people uncomfortable.”
Other computing technologies have been shown to shape our cognition. Studies indicate that autocomplete on websites and in word processors can dramatically reorganize our writing. Generally, these recommendations result in blander, more predictable prose. And where autocomplete systems give biased prompts, they result in biased writing. In one benign experiment, positive autocomplete suggestions led to more positive restaurant reviews, and negative autocomplete suggestions led to the reverse. The effects could go far beyond tweaking our writing styles to affecting our mental health, just as with the potentially depression- and anxiety-inducing social-media platforms of today.
The other direction these chatbots may take us is even more disturbing: into a world where our conversations with them result in our treating our fellow human beings with the apathy, disrespect, and incivility we more typically show machines.
Today’s chatbots perform best when instructed with a level of precision that would be appallingly rude in human conversation, stripped of any conversational pleasantries that the model could misinterpret: “Draft a 250-word paragraph in my typical writing style, detailing three examples to support the following point and cite your sources.” Not even the most detached corporate CEO would likely talk this way to their assistant, but it’s common with chatbots.
If chatbots truly become the dominant daily conversation partner for some people, there is an acute risk that these users will adopt a lexicon of AI commands even when talking to other humans. Rather than speaking with empathy, subtlety, and nuance, we’ll be trained to speak with the cold precision of a programmer talking to a computer. The colorful aphorisms and anecdotes that give conversations their inherently human quality, but that often confound large language models, could begin to vanish from the human discourse.
For precedent, one need only look at the ways that bot accounts already degrade digital discourse on social media, inflaming passions with crudely programmed responses to deeply emotional topics; they arguably played a role in sowing discord and polarizing voters in the 2016 election. But AI companions are likely to be a far larger part of some users’ social circle than the bots of today, potentially having a much larger impact on how those people use language and navigate relationships. What is unclear is whether this will negatively affect one user in a billion or a large portion of them.
Such a shift is unlikely to transform human conversations into cartoonishly robotic recitations overnight, but it could subtly and meaningfully reshape colloquial conversation over the course of years, just as the character limits of text messages affected so much of colloquial writing, turning terms such as LOL, IMO, and TMI into everyday vernacular.
AI chatbots are always there when you need them to be, for whatever you need them for. People aren’t like that. Imagine a future filled with people who have spent years conversing with their AI friends or romantic partners. Like a person whose only sexual experiences have been mediated by pornography or erotica, they could have unrealistic expectations of human partners. And the more ubiquitous and lifelike the chatbots become, the greater the impact could be.
More generally, AI might accelerate the disintegration of institutional and social trust. Technologies such as Facebook were supposed to bring the world together, but in the intervening years, the public has become more and more suspicious of the people around them and less trusting of civic institutions. AI may drive people further toward isolation and suspicion, always unsure whether the person they’re chatting with is actually a machine, and treating them as inhuman regardless.
Of course, history is replete with people claiming that the digital sky is falling, bemoaning each new invention as the end of civilization as we know it. In the end, LLMs may be little more than the word processor of tomorrow, a handy innovation that makes things a little easier while leaving most of our lives untouched. Which path we take depends on how we train the chatbots of tomorrow, but it also depends on whether we invest in strengthening the bonds of civil society today.
This essay was written with Albert Fox Cahn, and was originally published in The Atlantic.
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Microsoft Executives Hacked
[2024.01.29] Microsoft is reporting that a Russian intelligence agency -- the same one responsible for the SolarWinds hack -- accessed the email system of the company’s executives.
Beginning in late November 2023, the threat actor used a password spray attack to compromise a legacy non-production test tenant account and gain a foothold, and then used the account’s permissions to access a very small percentage of Microsoft corporate email accounts, including members of our senior leadership team and employees in our cybersecurity, legal, and other functions, and exfiltrated some emails and attached documents. The investigation indicates they were initially targeting email accounts for information related to Midnight Blizzard itself.
This is nutty. How does a “legacy non-production test tenant account” have access to executive emails? And why no two-factor authentication?
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NSA Buying Bulk Surveillance Data on Americans without a Warrant
[2024.01.30] The NSA finally admitted to buying bulk data on Americans from data brokers, in response to a query by Senator Wyden.
This is almost certainly illegal, although the NSA maintains that it is legal until it’s told otherwise.
Here are Wyden’s press release and some news articles.
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New Images of Colossus Released
[2024.01.30] GCHQ has released new images of the WWII Colossus code-breaking computer, celebrating the machine’s eightieth anniversary (birthday?).
News article.
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CFPB’s Proposed Data Rules
[2024.01.31] In October, the Consumer Financial Protection Bureau (CFPB) proposed a set of rules that if implemented would transform how financial institutions handle personal data about their customers. The rules put control of that data back in the hands of ordinary Americans, while at the same time undermining the data broker economy and increasing customer choice and competition. Beyond these economic effects, the rules have important data security benefits.
The CFPB’s rules align with a key security idea: the decoupling principle. By separating which companies see what parts of our data, and in what contexts, we can gain control over data about ourselves (improving privacy) and harden cloud infrastructure against hacks (improving security). Officials at the CFPB have described the new rules as an attempt to accelerate a shift toward “open banking,” and after an initial comment period on the new rules closed late last year, Rohit Chopra, the CFPB’s director, has said he would like to see the rule finalized by this fall.
Right now, uncountably many data brokers keep tabs on your buying habits. When you purchase something with a credit card, that transaction is shared with unknown third parties. When you get a car loan or a house mortgage, that information, along with your Social Security number and other sensitive data, is also shared with unknown third parties. You have no choice in the matter. The companies will freely tell you this in their disclaimers about personal information sharing: that you cannot opt-out of data sharing with “affiliate” companies. Since most of us can’t reasonably avoid getting a loan or using a credit card, we’re forced to share our data. Worse still, you don’t have a right to even see your data or vet it for accuracy, let alone limit its spread.
The CFPB’s simple and practical rules would fix this. The rules would ensure people can obtain their own financial data at no cost, control who it’s shared with and choose who they do business with in the financial industry. This would change the economics of consumer finance and the illicit data economy that exists today.
The best way for financial services firms to meet the CFPB’s rules would be to apply the decoupling principle broadly. Data is a toxic asset, and in the long run they’ll find that it’s better to not be sitting on a mountain of poorly secured financial data. Deleting the data is better for their users and reduces the chance they’ll incur expenses from a ransomware attack or breach settlement. As it stands, the collection and sale of consumer data is too lucrative for companies to say no to participating in the data broker economy, and the CFPB’s rules may help eliminate the incentive for companies to buy and sell these toxic assets. Moreover, in a free market for financial services, users will have the option to choose more responsible companies that also may be less expensive, thanks to savings from improved security.
Credit agencies and data brokers currently make money both from lenders requesting reports and from consumers requesting their data and seeking services that protect against data misuse. The CFPB’s new rules -- and the technical changes necessary to comply with them -- would eliminate many of those income streams. These companies have many roles, some of which we want and some we don’t, but as consumers we don’t have any choice in whether we participate in the buying and selling of our data. Giving people rights to their financial information would reduce the job of credit agencies to their core function: assessing risk of borrowers.
A free and properly regulated market for financial services also means choice and competition, something the industry is sorely in need of. Equifax, Transunion and Experian make up a longstanding oligopoly for credit reporting. Despite being responsible for one of the biggest data breaches of all time in 2017, the credit bureau Equifax is still around -- illustrating that the oligopolistic nature of this market means that companies face few consequences for misbehavior.
On the banking side, the steady consolidation of the banking sector has resulted in a small number of very large banks holding most deposits and thus most financial data. Behind the scenes, a variety of financial data clearinghouses -- companies most of us have never heard of -- get breached all the time, losing our personal data to scammers, identity thieves and foreign governments.
The CFPB’s new rules would require institutions that deal with financial data to provide simple but essential functions to consumers that stand to deliver security benefits. This would include the use of application programming interfaces (APIs) for software, eliminating the barrier to interoperability presented by today’s baroque, non-standard and non-programmatic interfaces to access data. Each such interface would allow for interoperability and potential competition. The CFPB notes that some companies have tried to claim that their current systems provide security by being difficult to use. As security experts, we disagree: Such aging financial systems are notoriously insecure and simply rely upon security through obscurity.
Furthermore, greater standardization and openness in financial data with mechanisms for consumer privacy and control means fewer gatekeepers. The CFPB notes that a small number of data aggregators have emerged by virtue of the complexity and opaqueness of today’s systems. These aggregators provide little economic value to the country as a whole; they extract value from us all while hindering competition and dynamism. The few new entrants in this space have realized how valuable it is for them to present standard APIs for these systems while managing the ugly plumbing behind the scenes.
In addition, by eliminating the opacity of the current financial data ecosystem, the CFPB is able to add a new requirement of data traceability and certification: Companies can only use consumers’ data when absolutely necessary for providing a service the consumer wants. This would be another big win for consumer financial data privacy.
It might seem surprising that a set of rules designed to improve competition also improves security and privacy, but it shouldn’t. When companies can make business decisions without worrying about losing customers, security and privacy always suffer. Centralization of data also means centralization of control and economic power and a decline of competition.
If this rule is implemented it will represent an important, overdue step to improve competition, privacy and security. But there’s more that can and needs to be done. In time, we hope to see more regulatory frameworks that give consumers greater control of their data and increased adoption of the technology and architecture of decoupling to secure all of our personal data, wherever it may be.
This essay was written with Barath Raghavan, and was originally published in Cyberscoop.
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Facebook’s Extensive Surveillance Network
[2024.02.01] Consumer Reports is reporting that Facebook has built a massive surveillance network:
Using a panel of 709 volunteers who shared archives of their Facebook data, Consumer Reports found that a total of 186,892 companies sent data about them to the social network. On average, each participant in the study had their data sent to Facebook by 2,230 companies. That number varied significantly, with some panelists’ data listing over 7,000 companies providing their data. The Markup helped Consumer Reports recruit participants for the study. Participants downloaded an archive of the previous three years of their data from their Facebook settings, then provided it to Consumer Reports.
This isn’t data about your use of Facebook. This data about your interactions with other companies, all of which is correlated and analyzed by Facebook. It constantly amazes me that we willingly allow these monopoly companies that kind of surveillance power.
Here’s the Consumer Reports study. It includes policy recommendations:
Many consumers will rightly be concerned about the extent to which their activity is tracked by Facebook and other companies, and may want to take action to counteract consistent surveillance. Based on our analysis of the sample data, consumers need interventions that will:
Reduce the overall amount of tracking.
Improve the ability for consumers to take advantage of their right to opt out under state privacy laws.
Empower social media platform users and researchers to review who and what exactly is being advertised on Facebook.
Improve the transparency of Facebook’s existing tools.
And then the report gives specifics.
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A Self-Enforcing Protocol to Solve Gerrymandering
[2024.02.02] In 2009, I wrote:
There are several ways two people can divide a piece of cake in half. One way is to find someone impartial to do it for them. This works, but it requires another person. Another way is for one person to divide the piece, and the other person to complain (to the police, a judge, or his parents) if he doesn’t think it’s fair. This also works, but still requires another person -- at least to resolve disputes. A third way is for one person to do the dividing, and for the other person to choose the half he wants.
The point is that unlike protocols that require a neutral third party to complete (arbitrated), or protocols that require that neutral third party to resolve disputes (adjudicated), self-enforcing protocols just work. Cut-and-choose works because neither side can cheat. And while the math can get really complicated, the idea generalizes to multiple people.
Well, someone just solved gerrymandering in this way. Prior solutions required either a bipartisan commission to create fair voting districts (arbitrated), or require a judge to approve district boundaries (adjudicated), their solution is self-enforcing.
And it’s trivial to explain:
One party defines a map of equal-population contiguous districts.
Then, the second party combines pairs of contiguous districts to create the final map.
It’s not obvious that this solution works. You could imagine that all the districts are defined so that one party has a slight majority. In that case, no combination of pairs will make that map fair. But real-world gerrymandering is never that clean. There’s “cracking,” where a party’s voters are split amongst several districts to dilute its power; and “packing,” where a party’s voters are concentrated in a single district so its influence can be minimized elsewhere. It turns out that this “define-combine procedure” works; the combining party can undo any damage that the defining party does -- that the results are fair. The paper has all the details, and they’re fascinating.
Of course, a theoretical solution is not a political solution. But it’s really neat to have a theoretical solution.
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David Kahn
[2024.02.02] David Kahn has died. His groundbreaking book, The Codebreakers, was the first serious book I read about codebreaking, and one of the primary reasons I entered this field.
He will be missed.
EDITED TO ADD (2/4): Funeral website.
EDITED TO ADD (2/10): New York Times obituary.
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Deepfake Fraud
[2024.02.05] A deepfake video conference call -- with everyone else on the call a fake -- fooled a finance worker into sending $25M to the criminals’ account.
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Documents about the NSA’s Banning of Furby Toys in the 1990s
[2024.02.06] Via a FOIA request, we have documents from the NSA about their banning of Furby toys. 404 Media has the story.
EDITED TO ADD: The documents are now on Archive.org.
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Teaching LLMs to Be Deceptive
[2024.02.07] Interesting research: “Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training“:
Abstract: Humans are capable of strategically deceptive behavior: behaving helpfully in most situations, but then behaving very differently in order to pursue alternative objectives when given the opportunity. If an AI system learned such a deceptive strategy, could we detect it and remove it using current state-of-the-art safety training techniques? To study this question, we construct proof-of-concept examples of deceptive behavior in large language models (LLMs). For example, we train models that write secure code when the prompt states that the year is 2023, but insert exploitable code when the stated year is 2024. We find that such backdoor behavior can be made persistent, so that it is not removed by standard safety training techniques, including supervised fine-tuning, reinforcement learning, and adversarial training (eliciting unsafe behavior and then training to remove it). The backdoor behavior is most persistent in the largest models and in models trained to produce chain-of-thought reasoning about deceiving the training process, with the persistence remaining even when the chain-of-thought is distilled away. Furthermore, rather than removing backdoors, we find that adversarial training can teach models to better recognize their backdoor triggers, effectively hiding the unsafe behavior. Our results suggest that, once a model exhibits deceptive behavior, standard techniques could fail to remove such deception and create a false impression of safety.
Especially note one of the sentences from the abstract: “For example, we train models that write secure code when the prompt states that the year is 2023, but insert exploitable code when the stated year is 2024.”
And this deceptive behavior is hard to detect and remove.
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On Software Liabilities
[2024.02.08] Over on Lawfare, Jim Dempsey published a really interesting proposal for software liability: “Standard for Software Liability: Focus on the Product for Liability, Focus on the Process for Safe Harbor.”
Section 1 of this paper sets the stage by briefly describing the problem to be solved. Section 2 canvasses the different fields of law (warranty, negligence, products liability, and certification) that could provide a starting point for what would have to be legislative action establishing a system of software liability. The conclusion is that all of these fields would face the same question: How buggy is too buggy? Section 3 explains why existing software development frameworks do not provide a sufficiently definitive basis for legal liability. They focus on process, while a liability regime should begin with a focus on the product -- that is, on outcomes. Expanding on the idea of building codes for building code, Section 4 shows some examples of product-focused standards from other fields. Section 5 notes that already there have been definitive expressions of software defects that can be drawn together to form the minimum legal standard of security. It specifically calls out the list of common software weaknesses tracked by the MITRE Corporation under a government contract. Section 6 considers how to define flaws above the minimum floor and how to limit that liability with a safe harbor.
Full paper here.
Dempsey basically creates three buckets of software vulnerabilities: easy stuff that the vendor should have found and fixed, hard-to-find stuff that the vendor couldn’t be reasonably expected to find, and the stuff in the middle. He draws from other fields -- consumer products, building codes, automobile design -- to show that courts can deal with the stuff in the middle.
I have long been a fan of software liability as a policy mechanism for improving cybersecurity. And, yes, software is complicated, but we shouldn’t let the perfect be the enemy of the good.
In 2003, I wrote:
Clearly this isn’t all or nothing. There are many parties involved in a typical software attack. There’s the company who sold the software with the vulnerability in the first place. There’s the person who wrote the attack tool. There’s the attacker himself, who used the tool to break into a network. There’s the owner of the network, who was entrusted with defending that network. One hundred percent of the liability shouldn’t fall on the shoulders of the software vendor, just as one hundred percent shouldn’t fall on the attacker or the network owner. But today one hundred percent of the cost falls on the network owner, and that just has to stop.
Courts can adjudicate these complex liability issues, and have figured this thing out in other areas. Automobile accidents involve multiple drivers, multiple cars, road design, weather conditions, and so on. Accidental restaurant poisonings involve suppliers, cooks, refrigeration, sanitary conditions, and so on. We don’t let the fact that no restaurant can possibly fix all of the food-safety vulnerabilities lead us to the conclusion that restaurants shouldn’t be responsible for any food-safety vulnerabilities, yet I hear that line of reasoning regarding software vulnerabilities all of the time.
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No, Toothbrushes Were Not Used in a Massive DDoS Attack
[2024.02.09] The widely reported story last week that 1.5 million smart toothbrushes were hacked and used in a DDoS attack is false.
Near as I can tell, a German reporter talking to someone at Fortinet got it wrong, and then everyone else ran with it without reading the German text. It was a hypothetical, which Fortinet eventually confirmed.
Or maybe it was a stock-price hack.
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On Passkey Usability
[2024.02.12] Matt Burgess tries to only use passkeys. The results are mixed.
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Molly White Reviews Blockchain Book
[2024.02.13] Molly White -- of “Web3 is Going Just Great” fame -- reviews Chris Dixon’s blockchain solutions book: Read Write Own:
In fact, throughout the entire book, Dixon fails to identify a single blockchain project that has successfully provided a non-speculative service at any kind of scale. The closest he ever comes is when he speaks of how “for decades, technologists have dreamed of building a grassroots internet access provider”. He describes one project that “got further than anyone else”: Helium. He’s right, as long as you ignore the fact that Helium was providing LoRaWAN, not Internet, that by the time he was writing his book Helium hotspots had long since passed the phase where they might generate even enough tokens for their operators to merely break even, and that the network was pulling in somewhere around $1,150 in usage fees a month despite the company being valued at $1.2 billion. Oh, and that the company had widely lied to the public about its supposed big-name clients, and that its executives have been accused of hoarding the project’s token to enrich themselves. But hey, a16z sunk millions into Helium (a fact Dixon never mentions), so might as well try to drum up some new interest!
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A Hacker’s Mind is Out in Paperback
[2024.02.13] The paperback version of A Hacker’s Mind has just been published. It’s the same book, only a cheaper format.
But -- and this is the real reason I am posting this -- Amazon has significantly discounted the hardcover to $15 to get rid of its stock. This is much cheaper than I am selling it for, and cheaper even than the paperback. So if you’ve been waiting for a price drop, this is your chance.
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Improving the Cryptanalysis of Lattice-Based Public-Key Algorithms
[2024.02.14] The winner of the Best Paper Award at Crypto this year was a significant improvement to lattice-based cryptanalysis.
This is important, because a bunch of NIST’s post-quantum options base their security on lattice problems.
I worry about standardizing on post-quantum algorithms too quickly. We are still learning a lot about the security of these systems, and this paper is an example of that learning.
News story.
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Upcoming Speaking Engagements
[2024.02.14] This is a current list of where and when I am scheduled to speak:
I’m speaking at the Munich Security Conference (MSC) 2024 in Munich, Germany, on Friday, February 16, 2024.
I’m giving a keynote on “AI and Trust” at Generative AI, Free Speech, & Public Discourse. The symposium will be held at Columbia University in New York City and online, at 3 PM ET on Tuesday, February 20, 2024.
I’m speaking (remotely) on “AI, Trust and Democracy” at Indiana University in Bloomington, Indiana, USA, at noon ET on February 20, 2024. The talk is part of the 2023-2024 Beyond the Web Speaker Series, presented by The Ostrom Workshop and Hamilton Lugar School.
The list is maintained on this page.
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Since 1998, CRYPTO-GRAM has been a free monthly newsletter providing summaries, analyses, insights, and commentaries on security technology. To subscribe, or to read back issues, see Crypto-Gram's web page.
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Please feel free to forward CRYPTO-GRAM, in whole or in part, to colleagues and friends who will find it valuable. Permission is also granted to reprint CRYPTO-GRAM, as long as it is reprinted in its entirety.
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.
Copyright © 2024 by Bruce Schneier.
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* Origin: High Portable Tosser at my node (618:500/14)