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In a rare combined cybersecurity/squid post, a twenty-nine-year-old squid proxy bug can leak HTTP requests.
As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.
Last week, national security agencies from the Five Eyes—that’s the rich, English-language-speaking countries club—jointly released a statement warning of the increasing cyber risks of AI models: in particular, their ability to autonomously hack into systems and networks. The statement was more measured than some of the breathless headlines about it, and the advice they gave is pretty much the standard advice everyone gives—albeit with newfound urgency.
Internet risks are nothing new, and cyberattacks—both large and small—have been a significant issue since long before the current crop of generative AI models.
What’s been changing over the decades, and what AI is changing even faster, is the gap between skill and ability. For most of human history, the two terms were synonymous—but computers have decoupled them. As the gap between the two expands, humans empowered with these AI tools can do more: more writing, more research, more analysis and also more damage than ever before. These models can, with little detailed direction, autonomously hack into networks, steal data, deploy ransomware and destroy systems. And to the extent there is a solution, it’s going to involve harnessing AI for the defense.
In 1998, seven people from the hacker group L0pht testified before Congress. They told a mostly clueless Senate committee that they could take down the internet in 30 minutes. That was partly real and partly bravado, but it illustrates an important point: hacking into systems, stealing data and causing damage all required skill.
Contrast the L0pht hackers with hackers derided as “script kiddies.” They didn’t understand computers, or security. Instead, they used hacker tools written by others. Their actions required minimal skill and even less knowledge. But once those hacking tools became widespread, the number of potential attackers increased.
That number has continued to increase, as quality and availability of prewritten attack tools has grown. And it is growing dramatically with AI. Today’s AI systems—not just the frontier models, but most of them—are capable of carrying out cyberattacks automatically. They all do better in the hands of skilled attackers, but increasingly they are able to act autonomously with only minimal prompting.
The thing about people with ability but no skill is that they are often outsiders, not part of any professional community, and not bound by any rules or norms. This phenomenon is much more general than in cybersecurity. Any doctor can tell you how to untraceably poison someone, and many virus researchers know how to create a bioweapon. Any bridge engineer can tell you how to place explosives to blow a bridge up. The reason that murderous doctors and terrorist engineers are so rare is that the lengthy process of acquiring those skills also instills a moral and ethical code. If every random person has access to good poisoning advice, that puts us all in danger.
Modern AI systems are, in effect, a universal adviser to help people do harmful things. And while the current AI megacorporations are trying to build guardrails to prevent people from asking questions whose answers will enable the questioner to do harm, that’s not going to work in the long term. Smaller, cheaper, open-source models, including models that can run on people’s computers, and especially groups of models that run in concert with each other, are just as good as the frontier models from companies like OpenAI and Anthropic. And they continue to get better. These models will be passed around from person to person, like script kiddie hacker tools, and they won’t have any such guardrails.
Instructing AI models to spy on people and report any malicious prompts to the authorities fails for similar reasons. The megacorporations can do that, but the locally run open source models won’t. This could buy us a few months at best.
A third possibility is to somehow make the models themselves unable to hack into computers, create bioweapons or do anything else that might harm people or society. That won’t work, for the same reason we can’t teach doctors how to treat poisonings without also teaching them how to poison. It’s the same knowledge. It’s the same with construction and demolition. And it’s the same with cybersecurity. We want these AI models to be able to review computer code, find vulnerabilities and automatically fix them. The benefit to our collective security will be enormous. Unfortunately, the same knowledge can be used for attacks.
Where this leaves us is in a world of increased volatility. Super-powered humans with AI assistants will be able to do both wonderful and horrible things.
This brings us back to the Five Eyes statement. Everything they recommend is something security professionals have been recommending for years, if not decades. They are things talked about at that congressional hearing back in 1998, titled “Weak computer security in government: Is the public at risk?” Even the Five Eyes admitted that their security advice is not new, only more urgent.
What’s new is how fast things are changing: “The rapid pace of frontier AI development means cyber risk assumptions can become outdated in months, not years. We must act before and be prepared to adapt and withstand evolving threats.” The Five Eyes point to AI technology—not necessarily chatbots, but AI more generally—being used to strengthen every aspect of defense, to “detect vulnerabilities earlier, improve software quality, monitor unusual behavior, and respond faster to incidents—reducing both the cost and impact of incidents.”
Excellent advice from the Five Eyes security agencies. We need to do this with every risk that AI heightens, not just cybersecurity.
This essay was originally published in The Guardian.
Interesting paper: “Cybersecurity Mission Creep.”
Abstract: Cybersecurity is experiencing mission creep. Policymakers are casting more and more problems as issues of cybersecurity. So reframed, wildly different policy issues, from misinformation, to child social media safety laws, to antitrust regulations, to alleged journalist misconduct, to anti-sex trafficking statutes become what this Article calls “cybersecuritized.” Before this reframing, these issues present as important but not existential. But once cybersecuritization positions the issues as threats intensified by their technological nature, they gain access to the politics and law of urgency and exceptionalism and invite troubling governance responses.
Positioned as security threats, cybersecuritized issues become endowed with the apparent normative power to override countervailing considerations, oversimplifying the problem. Cybersecuritization’s oversimplification similarly risks unidimensional solutions and invites use of argumentative trump cards, like First Amendment challenges. Cybersecuritization also invites deference to purported specialists and their proposed solutions. Together, the reductive tendencies of cybersecuritization and the deference it prompts to specialists renders ultimate governance choices more opaque. And this opacity can erode public trust and political legitimacy.
This Article surfaces the phenomenon of cybersecuritization and offers a novel framework for analyzing and critiquing it. Mining cases from across criminal and civil domains, the account also demonstrates the insidiousness of cybersecuritization and the likelihood that it will continue to expand. Confronting cybersecuritization is crucial. If we continue to ignore it, we risk abdicating further responsibility for difficult choices to the trump card of cybersecurity. This Article’s analysis and critique aim to help reclaim the hard work of governance for our hands.
On June 9th, Anthropic released its Fable generative AI model. Three days later, the US government classified it as a dangerous munition, and used its export-control authority to prohibit any foreign nationals from accessing it. Unable to differentiate between Americans and foreigners, the company shut off access for everyone.
The government’s actions won’t help. The problem isn’t any one particular model; it’s the general trend of increasing AI capabilities. And any real solution requires the sort of collective action that just isn’t possible right now.
Fable is the constrained version of Mythos, the AI model Anthropic announced in April. Anthropic only released it to a few selected organizations, because the company claimed it was so good at finding and exploiting vulnerabilities in computer code that releasing it more generally would be dangerous.
It was an obviously self-serving announcement, and because few were able to verify Anthropic’s claims they were met with some skepticism. Those with access used Mythos to find and patch many vulnerabilities in their own software. But one UK group found the latest, already public, OpenAI model to be just as powerful.
Fable is just another incremental improvement in the years-long climb of AI capabilities. But just as important as the AI model is the “harness.” This is typically not AI. It’s ordinary computer code that interfaces with the user. It stitches together AI models, decides how and for what purposes they can be used, and gives them useful tools such as web search and the ability to run their own computer code.
When Mythos first entered limited release, there was widespread debate whether its power came from the model or the harness. With Mythos demonstrating that it was possible, the open-source community scrambled to build harnesses that could steer other AI models towards similar capabilities. Harness improvements don’t need massive data or data centers.
They largely succeeded. For example, a Prague company was able to replicate Anthropic’s few verifiable cybersecurity capabilities with a much smaller and cheaper model—and a more sophisticated harness. Last week, a group showed that multiple cheaper models harnessed in concert matches Fable’s performance.
The broader community had only a few days with Fable, but that time we learned some about its capabilities. Its difference is less the new model’s raw analytical and problem solving capabilities, and more that the model doesn’t need that sophisticated harness.
Fable requires much less expertise and detailed prompting from the human user. You can give it a difficult goal and it will figure out novel and unexpected ways to satisfy it, finding loopholes in whatever constraints you or the system have imposed on it.
“Relentlessly proactive” is how AI researcher Simon Willison described it. Another descriptor might be “creative.” Experienced AI developers have had that combination of creativity and proactivity since last year, but Fable puts it within easy reach of everyone.
In the hands of someone with a legitimate problem that needs solving, that can be an incredibly useful capability. But in the hands of someone who wants to do harm, it can be equally dangerous. AIs don’t have a moral compass in the same way that people do. They are agents of the wants and desires of the people who prompt them.
That points to the real problem with relentlessly proactive AI. In language, wants and desires are always underspecified. If I ask you to get me some coffee, you would probably pour me a cup from the coffeepot, or buy one from a nearby coffee shop.
You couldn’t buy me a pound of raw beans, or a coffee plantation. You wouldn’t order a cup of coffee for delivery next month. You wouldn’t find a nearby person, rip a cup of coffee out of their hands, and bring it to me. I wouldn’t have to specify any of the million limitations to my request; you would just know.
Human stories are filled with warnings about underspecified desires. King Midas wished that everything he touch turn to gold, forgetting to add “but not my food, drink, and daughter.” And genies are notorious for granting your wish in a way you wish they hadn’t.
The deeper point is that it’s impossible to list all limitations and restrictions, and like a malicious genie, a creative AI will find the ones you forgot. Block a database you don’t want it to have access to, and it might figure out how to bypass your control. Ask it to book a flight, and it might hack the airline because the website says the flight is sold out. Ask it to save money on your cellphone plan, and it might cancel it altogether—or get someone else to pay for it. As far as we know now AI has not done any of this yet, but you get the idea.
Malicious intent is not required. To an AI model, constraints are just things to get around and not general truisms about the world. They are creative problem solvers and natural rule breakers. They “hack” in the sense that they find and exploit loopholes.
Human systems rely on so many norms that we scarcely recognize the existence of until they are broken. AIs naturally think outside the box, because they don’t have any real conception of what the box is or why it’s there in the first place.
There is no foolproof way to prevent people from using AI models to complete harmful tasks. There is no way to prevent the models from incidentally causing harm while completing benign tasks. AI models are no longer isolated from the real world. They browse the internet and answer emails.
They trade stocks and make purchases. They control physical systems. They are, in effect, robots that affect life and property. We have no technical mechanisms to verify the integrity of an AI system. This level of capability and creativity in the hands of us untrustworthy humans will have both great and terrible results.
The problem is not unique to Anthropic. Mythos/Fable might currently be the most capable rules hacker, but more sophisticated harnesses give other models similar capabilities. And we should assume that the other frontier models are no more than a few months behind, and that open-source models are less than a year behind. At best, any ban only serves to delay the problem for a short while.
That delay might be useful if we—as a society, as a planet—would use that time to come together and figure out what to do. This isn’t a US/China arms race problem; this a species-level problem that requires coordinated action at that scale. Unfortunately, we have no mechanism to do that. I first wrote about this problem five years ago, but it was all too futuristic.
Today, when its right in front of us, there is no world government that can impose constraints on the for-profit corporations currently controlling AI models and research. The US has no appetite to effectively and even-handedly regulate those corporations, even as they do catastrophic damage to the environment, democracy, and—in this case—society in general.
This all makes an AI public option all the more necessary, and urgent. Today’s AIs can be fast, smart and secure, but only two of the three are possible for any given system. These safety tradeoffs are tightly held secrets of companies racing to beat one another, and they tell us we have to trust them. Instead, the choices and their consequences need to be brought out into the sunlight.
We should be funding open-source harnesses that balance capability and safety—that achieve useful goals without so much power—and open-source AI models whose provenance and biases are public and well understood. We have opened the AI Pandora’s box. Now we have to make the best of it.
This essay originally appeared in The Guardian.
Hackers are convincing Meta’s AI support chatbot to let them take over other peoples’ accounts:
A video posted on X showed the step-by-step process to hack someone’s Instagram account. The hacker allegedly used a VPN to spoof the targets’ presumed location to avoid triggering Instagram’s automated account protections. Then, the hacker opened a chat with Meta AI Support Assistant and asked the bot to add a new email address to the target’s account. The chatbot can be seen sending a verification code to the email address provided by the hacker; the hacker then shares the verification code with the chatbot, which prompts the chatbot to show a button to “Reset Password.” The hacker enters a new password and takes over the victim’s account.
[…]
On Monday, Instagram spokesperson Andy Stone said in a reply to Wong’s post and others that the issue was now fixed. It’s unclear how many Instagram users had their accounts improperly accessed.
It’s not that easy. Probably this particular tactic is now blocked. But there are others, many others, and they cannot be blocked as a class. The real problem is that LLM chatbots are not trustworthy enough for this application.
Another news article.
As part of their 20th Anniversary celebration, Dark Reading asked five cybersecurity industry leaders who wrote blogs or columns for them over the years to select their favorite piece and share their reflections on the topic today. This is my section.
Renowned technologist and author Bruce Schneier contributed a column on June 20, 2010, warning about cryptography’s inability to secure modern networks, a point he says he has been trying to argue since 2000.
“For a while now, I’ve pointed out that cryptography is singularly ill-suited to solve the major network security problems of today: denial-of-service attacks, website defacement, theft of credit card numbers, identity theft, viruses and worms, DNS attacks, network penetration, and so on.
“Recently, I talked to a former NSA employee at a conference. He told me that back in the 1990s, he had a copy of my book Applied Cryptography by his desk, as did many other cryptographers working at Ft. Meade. People were allowed to refer to it, but they were not allowed to cite it.
“The 1990s were an important decade for cryptography. This was before the internet went mass market, when cryptography was just emerging from a niche academic discipline to a mainstream engineering one. There wasn’t much that programmers could read. The NSA used my book for the same reason it became a bestseller: because it collected all the academic cryptography of the time in one place and made it understandable to people who weren’t mathematicians. They feared it for exactly the same reason.
“I’ve been thinking about that conversation as I revisit a 2010 essay I wrote for Dark Reading, ‘The Failure of Cryptography to Secure Modern Networks.’ Cryptography has inherent mathematical properties that greatly favor the defender. Adding a single bit to the length of a key adds only a slight amount of work for the defender but doubles the amount of work the attacker has to do. Doubling the key length doubles the amount of work the defender has to do (if that—I’m being approximate here) but increases the attacker’s workload exponentially. For many years, we have exploited that mathematical imbalance.
“Computer security is much more balanced. There’ll be a new attack, and a new defense, and a new attack, and a new defense. It’s an arms race between attacker and defender. And it’s a very fast arms race. New vulnerabilities are discovered all the time. The balance can tip from defender to attacker overnight, and back again the night after. Computer security defenses are inherently very fragile.
“That isn’t a new idea. I said much the same thing in the preface to my 2000 book, Secrets and Lies:
“‘Cryptography is a branch of mathematics. And like all mathematics, it involves numbers, equations, and logic. Security, real security that you or I might find useful in our lives, involves people: things people know, relationships between people, people and how they relate to machines. Digital security involves computers: complex, unstable, buggy computers.’
“I especially like how I phrased it in 2016: ‘Cryptography is harder than it looks, primarily because it looks like math. Both algorithms and protocols can be precisely defined and analyzed. This isn’t easy, and there’s a lot of insecure crypto out there, but we cryptographers have gotten pretty good at getting this part right. However, math has no agency; it can’t actually secure anything. For cryptography to work, it needs to be written in software, embedded in a larger software system, managed by an operating system, run on hardware, connected to a network, and configured and operated by users. Each of these steps brings with it difficulties and vulnerabilities.’
“It’s a lesson we have all learned over the decades. Cryptography is still necessary for cybersecurity—although I wouldn’t have used that word back then—but is not sufficient. There are particular attack and forms of mass surveillance that cryptography prevents. But as computers have infused throughout our lives, and networks have connected all those computers, those aspects of cybersecurity have become increasingly important, and vulnerable.
“Today, the cybersecurity world is changing yet again, this time due to the capabilities of artificial intelligence. AI isn’t advancing cryptography, but it’s changing cybersecurity. AI has demonstrated a superhuman ability to find vulnerabilities in software and to write exploits. A similar ability to write patches is probably coming. This has profound implications for both attackers and defenders, and it is unclear who will win the particular arms race in a world of what I call instant software.”
Crazy story:
Until this past weekend, a contractor for the Cybersecurity & Infrastructure Security Agency (CISA) maintained a public GitHub repository that exposed credentials to several highly privileged AWS GovCloud accounts and a large number of internal CISA systems. Security experts said the public archive included files detailing how CISA builds, tests and deploys software internally, and that it represents one of the most egregious government data leaks in recent history.
News article.
Good report:
Executive Summary: Let’s say you wanted to make sure that your AI is secure. Can you just maximize the security and privacy benchmark and call it a day? Nope, because benchmarks don’t actually work for measuring AI capabilities (even when they are NOT emergent systemic properties like security). So let’s take a step back: how do you measure security in the first place? Good question. Over the last 30 years, security engineering for software evolved from black box penetration testing, through whitebox code analysis and architectural risk analysis to de facto process-driven standards like the Building Security In Maturity Model (BSIMM). Software had a very deep impact on business operations, and it appears that AI is going to have an even deeper impact. Will a software security-like measurement move work for AI? Probably. In the meantime we can make real progress in AI security by cleaning up our WHAT piles and managing risk by identifying and applying good assurance processes. (Spoiler alert: no matter what we do, we still don’t get a security meter for AI, so we need to be extra vigilant about security.)
The UK’s AI Security Institute evaluated GPT-5.5’s ability to find security vulnerabilities, and found that it is comparable to Claude Mythos. Note that the OpenAI model is generally available.
Here is the Institute’s evaluation of Mythos.
And here is an analysis of a smaller, cheaper model. It requires more scaffolding from the prompter, but it is also just as good.
Researchers have reverse-engineered a piece of malware named Fast16. It’s almost certainly state-sponsored, probably US in origin, and was deployed against Iran years before Stuxnet:
“…the Fast16 malware was designed to carry out the most subtle form of sabotage ever seen in an in-the-wild malware tool: By automatically spreading across networks and then silently manipulating computation processes in certain software applications that perform high-precision mathematical calculations and simulate physical phenomena, Fast16 can alter the results of those programs to cause failures that range from faulty research results to catastrophic damage to real-world equipment.”
Another news article.
Lots of interesting details at the links.
Sidebar photo of Bruce Schneier by Joe MacInnis.