cryptonews

Interesting.

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There are thousands of fake Reddit and WeTransfer webpages that are pushing malware. They exploit people who are using search engines to search sites like Reddit.

Unsuspecting victims clicking on the link are taken to a fake WeTransfer site that mimicks the interface of the popular file-sharing service. The ‘Download’ button leads to the Lumma Stealer payload hosted on “weighcobbweo[.]top.”

Boingboing post.

The Department of Justice is investigating a lobbying firm representing ExxonMobil for hacking the phones of climate activists:

The hacking was allegedly commissioned by a Washington, D.C., lobbying firm, according to a lawyer representing the U.S. government. The firm, in turn, was allegedly working on behalf of one of the world’s largest oil and gas companies, based in Texas, that wanted to discredit groups and individuals involved in climate litigation, according to the lawyer for the U.S. government. In court documents, the Justice Department does not name either company.

As part of its probe, the U.S. is trying to extradite an Israeli private investigator named Amit Forlit from the United Kingdom for allegedly orchestrating the hacking campaign. A lawyer for Forlit claimed in a court filing that the hacking operation her client is accused of leading “is alleged to have been commissioned by DCI Group, a lobbying firm representing ExxonMobil, one of the world’s largest fossil fuel companies.”

Jen Easterly is out as the Director of CISA. Read her final interview:

There’s a lot of unfinished business. We have made an impact through our ransomware vulnerability warning pilot and our pre-ransomware notification initiative, and I’m really proud of that, because we work on preventing somebody from having their worst day. But ransomware is still a problem. We have been laser-focused on PRC cyber actors. That will continue to be a huge problem. I’m really proud of where we are, but there’s much, much more work to be done. There are things that I think we can continue driving, that the next administration, I hope, will look at, because, frankly, cybersecurity is a national security issue.

If Project 2025 is a guide, the agency will be gutted under Trump:

“Project 2025’s recommendations—essentially because this one thing caused anger—is to just strip the agency of all of its support altogether,” he said. “And CISA’s functions go so far beyond its role in the information space in a way that would do real harm to election officials and leave them less prepared to tackle future challenges.”

In the DHS chapter of Project 2025, Cucinelli suggests gutting CISA almost entirely, moving its core responsibilities on critical infrastructure to the Department of Transportation. It’s a suggestion that Adav Noti, the executive director of the nonpartisan voting rights advocacy organization Campaign Legal Center, previously described to Democracy Docket as “absolutely bonkers.”

“It’s located at Homeland Security because the whole premise of the Department of Homeland Security is that it’s supposed to be the central resource for the protection of the nation,” Noti said. “And that the important functions shouldn’t be living out in siloed agencies.”

A newly discovered VPN backdoor uses some interesting tactics to avoid detection:

When threat actors use backdoor malware to gain access to a network, they want to make sure all their hard work can’t be leveraged by competing groups or detected by defenders. One countermeasure is to equip the backdoor with a passive agent that remains dormant until it receives what’s known in the business as a “magic packet.” On Thursday, researchers revealed that a never-before-seen backdoor that quietly took hold of dozens of enterprise VPNs running Juniper Network’s Junos OS has been doing just that.

J-Magic, the tracking name for the backdoor, goes one step further to prevent unauthorized access. After receiving a magic packet hidden in the normal flow of TCP traffic, it relays a challenge to the device that sent it. The challenge comes in the form of a string of text that’s encrypted using the public portion of an RSA key. The initiating party must then respond with the corresponding plaintext, proving it has access to the secret key.

The lightweight backdoor is also notable because it resided only in memory, a trait that makes detection harder for defenders. The combination prompted researchers at Lumin Technology’s Black Lotus Lab to sit up and take notice.

[…]

The researchers found J-Magic on VirusTotal and determined that it had run inside the networks of 36 organizations. They still don’t know how the backdoor got installed.

Slashdot thread.

A Travers’ beaked whale (Mesoplodon traversii) washed ashore in New Zealand, and scientists conlcuded that “the prevalence of squid remains [in its stomachs] suggests that these deep-sea cephalopods form a significant part of the whale’s diet, similar to other beaked whale species.”

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Last month, Henry Farrell and I convened the Third Interdisciplinary Workshop on Reimagining Democracy (IWORD 2024) at Johns Hopkins University’s Bloomberg Center in Washington DC. This is a small, invitational workshop on the future of democracy. As with the previous two workshops, the goal was to bring together a diverse set of political scientists, law professors, philosophers, AI researchers and other industry practitioners, political activists, and creative types (including science fiction writers) to discuss how democracy might be reimagined in the current century.

The goal of the workshop is to think very broadly. Modern democracy was invented in the mid-eighteenth century, using mid-eighteenth-century technology. If democracy were to be invented today, it would look very different. Elections would look different. The balance between representation and direct democracy would look different. Adjudication and enforcement would look different. Everything would look different, because our conceptions of fairness, justice, equality, and rights are different, and we have much more powerful technology to bring to bear on the problems. Also, we could start from scratch without having to worry about evolving our current democracy into this imagined future system.

We can’t do that, of course, but it’s still still valuable to speculate. Of course we need to figure out how to reform our current systems, but we shouldn’t limit our thinking to incremental steps. We also need to think about discontinuous changes as well. I wrote about the philosophy more in this essay about IWORD 2022.

IWORD 2024 was easily the most intellectually stimulating two days of my year. It’s also intellectually exhausting; the speed and intensity of ideas is almost too much. I wrote the format in my blog post on IWORD 2023.

Summaries of all the IWORD 2024 talks are in the first set of comments below. And here are links to the previous IWORDs:

• IWORD 2022: home page, essay and talk summaries
• IWORD 2023: home page and talk summaries.

IWORD 2025 will be held either in New York or New Haven; still to be determined.

Artificial intelligence (AI) is writing law today. This has required no changes in legislative procedure or the rules of legislative bodies—all it takes is one legislator, or legislative assistant, to use generative AI in the process of drafting a bill.

In fact, the use of AI by legislators is only likely to become more prevalent. There are currently projects in the US House, US Senate, and legislatures around the world to trial the use of AI in various ways: searching databases, drafting text, summarizing meetings, performing policy research and analysis, and more. A Brazilian municipality passed the first known AI-written law in 2023.

That’s not surprising; AI is being used more everywhere. What is coming into focus is how policymakers will use AI and, critically, how this use will change the balance of power between the legislative and executive branches of government. Soon, US legislators may turn to AI to help them keep pace with the increasing complexity of their lawmaking—and this will suppress the power and discretion of the executive branch to make policy.

Demand for Increasingly Complex Legislation

Legislators are writing increasingly long, intricate, and complicated laws that human legislative drafters have trouble producing. Already in the US, the multibillion-dollar lobbying industry is subsidizing lawmakers in writing baroque laws: suggesting paragraphs to add to bills, specifying benefits for some, carving out exceptions for others. Indeed, the lobbying industry is growing in complexity and influence worldwide.

Several years ago, researchers studied bills introduced into state legislatures throughout the US, looking at which bills were wholly original texts and which borrowed text from other states or from lobbyist-written model legislation. Their conclusion was not very surprising. Those who borrowed the most text were in legislatures that were less resourced. This makes sense: If you’re a part-time legislator, perhaps unpaid and without a lot of staff, you need to rely on more external support to draft legislation. When the scope of policymaking outstrips the resources of legislators, they look for help. Today, that often means lobbyists, who provide expertise, research services, and drafting labor to legislators at the local, state, and federal levels at no charge. Of course, they are not unbiased: They seek to exert influence on behalf of their clients.

Another study, at the US federal level, measured the complexity of policies proposed in legislation and tried to determine the factors that led to such growing complexity. While there are numerous ways to measure legal complexity, these authors focused on the specificity of institutional design: How exacting is Congress in laying out the relational network of branches, agencies, and officials that will share power to implement the policy?

In looking at bills enacted between 1993 and 2014, the researchers found two things. First, they concluded that ideological polarization drives complexity. The suggestion is that if a legislator is on the extreme end of the ideological spectrum, they’re more likely to introduce a complex law that constrains the discretion of, as the authors put it, “entrenched bureaucratic interests.” And second, they found that divided government drives complexity to a large degree: Significant legislation passed under divided government was found to be 65 percent more complex than similar legislation passed under unified government. Their conclusion is that, if a legislator’s party controls Congress, and the opposing party controls the White House, the legislator will want to give the executive as little wiggle room as possible. When legislators’ preferences disagree with the executive’s, the legislature is incentivized to write laws that specify all the details. This gives the agency designated to implement the law as little discretion as possible.

Because polarization and divided government are increasingly entrenched in the US, the demand for complex legislation at the federal level is likely to grow. Today, we have both the greatest ideological polarization in Congress in living memory and an increasingly divided government at the federal level. Between 1900 and 1970 (57th through 90th Congresses), we had 27 instances of unified government and only seven divided; nearly a four-to-one ratio. Since then, the trend is roughly the opposite. As of the start of the next Congress, we will have had 20 divided governments and only eight unified (nearly a three-to-one ratio). And while the incoming Trump administration will see a unified government, the extremely closely divided House may often make this Congress look and feel like a divided one (see the recent government shutdown crisis as an exemplar) and makes truly divided government a strong possibility in 2027.

Another related factor driving the complexity of legislation is the need to do it all at once. The lobbyist feeding frenzy—spurring major bills like the Affordable Care Act to be thousands of pages in length—is driven in part by gridlock in Congress. Congressional productivity has dropped so low that bills on any given policy issue seem like a once-in-a-generation opportunity for legislators—and lobbyists—to set policy.

These dynamics also impact the states. States often have divided governments, albeit less often than they used to, and their demand for drafting assistance is arguably higher due to their significantly smaller staffs. And since the productivity of Congress has cratered in recent years, significantly more policymaking is happening at the state level.

But there’s another reason, particular to the US federal government, that will likely force congressional legislation to be more complex even during unified government. In June 2024, the US Supreme Court overturned the Chevron doctrine, which gave executive agencies broad power to specify and implement legislation. Suddenly, there is a mandate from the Supreme Court for more specific legislation. Issues that have historically been left implicitly to the executive branch are now required to be either explicitly delegated to agencies or specified directly in statute. Either way, the Court’s ruling implied that law should become more complex and that Congress should increase its policymaking capacity.

This affects the balance of power between the executive and legislative branches of government. When the legislature delegates less to the executive branch, it increases its own power. Every decision made explicitly in statute is a decision the executive makes not on its own but, rather, according to the directive of the legislature. In the US system of separation of powers, administrative law is a tool for balancing power among the legislative, executive, and judicial branches. The legislature gets to decide when to delegate and when not to, and it can respond to judicial review to adjust its delegation of control as needed. The elimination of Chevron will induce the legislature to exert its control over delegation more robustly.

At the same time, there are powerful political incentives for Congress to be vague and to rely on someone else, like agency bureaucrats, to make hard decisions. That empowers third parties—the corporations, or lobbyists—that have been gifted by the overturning of Chevron a new tool in arguing against administrative regulations not specifically backed up by law. A continuing stream of Supreme Court decisions handing victories to unpopular industries could be another driver of complex law, adding political pressure to pass legislative fixes.

AI Can Supply Complex Legislation

Congress may or may not be up to the challenge of putting more policy details into law, but the external forces outlined above—lobbyists, the judiciary, and an increasingly divided and polarized government—are pushing them to do so. When Congress does take on the task of writing complex legislation, it’s quite likely it will turn to AI for help.

Two particular AI capabilities enable Congress to write laws different from laws humans tend to write. One, AI models have an enormous scope of expertise, whereas people have only a handful of specializations. Large language models (LLMs) like the one powering ChatGPT can generate legislative text on funding specialty crop harvesting mechanization equally as well as material on energy efficiency standards for street lighting. This enables a legislator to address more topics simultaneously. Two, AI models have the sophistication to work with a higher degree of complexity than people can. Modern LLM systems can instantaneously perform several simultaneous multistep reasoning tasks using information from thousands of pages of documents. This enables a legislator to fill in more baroque detail on any given topic.

That’s not to say that handing over legislative drafting to machines is easily done. Modernizing any institutional process is extremely hard, even when the technology is readily available and performant. And modern AI still has a ways to go to achieve mastery of complex legal and policy issues. But the basic tools are there.

AI can be used in each step of lawmaking, and this will bring various benefits to policymakers. It could let them work on more policies—more bills—at the same time, add more detail and specificity to each bill, or interpret and incorporate more feedback from constituents and outside groups. The addition of a single AI tool to a legislative office may have an impact similar to adding several people to their staff, but with far lower cost.

Speed sometimes matters when writing law. When there is a change of governing party, there is often a rush to change as much policy as possible to match the platform of the new regime. AI could help legislators do that kind of wholesale revision. The result could be policy that is more responsive to voters—or more political instability. Already in 2024, the US House’s Office of the Clerk has begun using AI to speed up the process of producing cost estimates for bills and understanding how new legislation relates to existing code. Ohio has used an AI tool to do wholesale revision of state administrative law since 2020.

AI can also make laws clearer and more consistent. With their superhuman attention spans, AI tools are good at enforcing syntactic and grammatical rules. They will be effective at drafting text in precise and proper legislative language, or offering detailed feedback to human drafters. Borrowing ideas from software development, where coders use tools to identify common instances of bad programming practices, an AI reviewer can highlight bad law-writing practices. For example, it can detect when significant phrasing is inconsistent across a long bill. If a bill about insurance repeatedly lists a variety of disaster categories, but leaves one out one time, AI can catch that.

Perhaps this seems like minutiae, but a small ambiguity or mistake in law can have massive consequences. In 2015, the Affordable Care Act came close to being struck down because of a typo in four words, imperiling health care services extended to more than 7 million Americans.

There’s more that AI can do in the legislative process. AI can summarize bills and answer questions about their provisions. It can highlight aspects of a bill that align with, or are contrary to, different political points of view. We can even imagine a future in which AI can be used to simulate a new law and determine whether or not it would be effective, or what the side effects would be. This means that beyond writing them, AI could help lawmakers understand laws. Congress is notorious for producing bills hundreds of pages long, and many other countries sometimes have similarly massive omnibus bills that address many issues at once. It’s impossible for any one person to understand how each of these bills’ provisions would work. Many legislatures employ human analysis in budget or fiscal offices that analyze these bills and offer reports. AI could do this kind of work at greater speed and scale, so legislators could easily query an AI tool about how a particular bill would affect their district or areas of concern.

This is a use case that the House subcommittee on modernization has urged the Library of Congress to take action on. Numerous software vendors are already marketing AI legislative analysis tools. These tools can potentially find loopholes or, like the human lobbyists of today, craft them to benefit particular private interests.

These capabilities will be attractive to legislators who are looking to expand their power and capabilities but don’t necessarily have more funding to hire human staff. We should understand the idea of AI-augmented lawmaking contextualized within the longer history of legislative technologies. To serve society at modern scales, we’ve had to come a long way from the Athenian ideals of direct democracy and sortition. Democracy no longer involves just one person and one vote to decide a policy. It involves hundreds of thousands of constituents electing one representative, who is augmented by a staff as well as subsidized by lobbyists, and who implements policy through a vast administrative state coordinated by digital technologies. Using AI to help those representatives specify and refine their policy ideas is part of a long history of transformation.

Whether all this AI augmentation is good for all of us subject to the laws they make is less clear. There are real risks to AI-written law, but those risks are not dramatically different from what we endure today. AI-written law trying to optimize for certain policy outcomes may get it wrong (just as many human-written laws are misguided). AI-written law may be manipulated to benefit one constituency over others, by the tech companies that develop the AI, or by the legislators who apply it, just as human lobbyists steer policy to benefit their clients.

Regardless of what anyone thinks of any of this, regardless of whether it will be a net positive or a net negative, AI-made legislation is coming—the growing complexity of policy demands it. It doesn’t require any changes in legislative procedures or agreement from any rules committee. All it takes is for one legislative assistant, or lobbyist, to fire up a chatbot and ask it to create a draft. When legislators voted on that Brazilian bill in 2023, they didn’t know it was AI-written; the use of ChatGPT was undisclosed. And even if they had known, it’s not clear it would have made a difference. In the future, as in the past, we won’t always know which laws will have good impacts and which will have bad effects, regardless of the words on the page, or who (or what) wrote them.

This essay was written with Nathan E. Sanders, and originally appeared in Lawfare.

Humans make mistakes all the time. All of us do, every day, in tasks both new and routine. Some of our mistakes are minor and some are catastrophic. Mistakes can break trust with our friends, lose the confidence of our bosses, and sometimes be the difference between life and death.

Over the millennia, we have created security systems to deal with the sorts of mistakes humans commonly make. These days, casinos rotate their dealers regularly, because they make mistakes if they do the same task for too long. Hospital personnel write on limbs before surgery so that doctors operate on the correct body part, and they count surgical instruments to make sure none were left inside the body. From copyediting to double-entry bookkeeping to appellate courts, we humans have gotten really good at correcting human mistakes.

Humanity is now rapidly integrating a wholly different kind of mistake-maker into society: AI. Technologies like large language models (LLMs) can perform many cognitive tasks traditionally fulfilled by humans, but they make plenty of mistakes. It seems ridiculous when chatbots tell you to eat rocks or add glue to pizza. But it’s not the frequency or severity of AI systems’ mistakes that differentiates them from human mistakes. It’s their weirdness. AI systems do not make mistakes in the same ways that humans do.

Much of the friction—and risk—associated with our use of AI arise from that difference. We need to invent new security systems that adapt to these differences and prevent harm from AI mistakes.

Human Mistakes vs AI Mistakes

Life experience makes it fairly easy for each of us to guess when and where humans will make mistakes. Human errors tend to come at the edges of someone’s knowledge: Most of us would make mistakes solving calculus problems. We expect human mistakes to be clustered: A single calculus mistake is likely to be accompanied by others. We expect mistakes to wax and wane, predictably depending on factors such as fatigue and distraction. And mistakes are often accompanied by ignorance: Someone who makes calculus mistakes is also likely to respond “I don’t know” to calculus-related questions.

To the extent that AI systems make these human-like mistakes, we can bring all of our mistake-correcting systems to bear on their output. But the current crop of AI models—particularly LLMs—make mistakes differently.

AI errors come at seemingly random times, without any clustering around particular topics. LLM mistakes tend to be more evenly distributed through the knowledge space. A model might be equally likely to make a mistake on a calculus question as it is to propose that cabbages eat goats.

And AI mistakes aren’t accompanied by ignorance. A LLM will be just as confident when saying something completely wrong—and obviously so, to a human—as it will be when saying something true. The seemingly random inconsistency of LLMs makes it hard to trust their reasoning in complex, multi-step problems. If you want to use an AI model to help with a business problem, it’s not enough to see that it understands what factors make a product profitable; you need to be sure it won’t forget what money is.

How to Deal with AI Mistakes

This situation indicates two possible areas of research. The first is to engineer LLMs that make more human-like mistakes. The second is to build new mistake-correcting systems that deal with the specific sorts of mistakes that LLMs tend to make.

We already have some tools to lead LLMs to act in more human-like ways. Many of these arise from the field of “alignment” research, which aims to make models act in accordance with the goals and motivations of their human developers. One example is the technique that was arguably responsible for the breakthrough success of ChatGPT: reinforcement learning with human feedback. In this method, an AI model is (figuratively) rewarded for producing responses that get a thumbs-up from human evaluators. Similar approaches could be used to induce AI systems to make more human-like mistakes, particularly by penalizing them more for mistakes that are less intelligible.

When it comes to catching AI mistakes, some of the systems that we use to prevent human mistakes will help. To an extent, forcing LLMs to double-check their own work can help prevent errors. But LLMs can also confabulate seemingly plausible, but truly ridiculous, explanations for their flights from reason.

Other mistake mitigation systems for AI are unlike anything we use for humans. Because machines can’t get fatigued or frustrated in the way that humans do, it can help to ask an LLM the same question repeatedly in slightly different ways and then synthesize its multiple responses. Humans won’t put up with that kind of annoying repetition, but machines will.

Understanding Similarities and Differences

Researchers are still struggling to understand where LLM mistakes diverge from human ones. Some of the weirdness of AI is actually more human-like than it first appears. Small changes to a query to an LLM can result in wildly different responses, a problem known as prompt sensitivity. But, as any survey researcher can tell you, humans behave this way, too. The phrasing of a question in an opinion poll can have drastic impacts on the answers.

LLMs also seem to have a bias towards repeating the words that were most common in their training data; for example, guessing familiar place names like “America” even when asked about more exotic locations. Perhaps this is an example of the human “availability heuristic” manifesting in LLMs, with machines spitting out the first thing that comes to mind rather than reasoning through the question. And like humans, perhaps, some LLMs seem to get distracted in the middle of long documents; they’re better able to remember facts from the beginning and end. There is already progress on improving this error mode, as researchers have found that LLMs trained on more examples of retrieving information from long texts seem to do better at retrieving information uniformly.

In some cases, what’s bizarre about LLMs is that they act more like humans than we think they should. For example, some researchers have tested the hypothesis that LLMs perform better when offered a cash reward or threatened with death. It also turns out that some of the best ways to “jailbreak” LLMs (getting them to disobey their creators’ explicit instructions) look a lot like the kinds of social engineering tricks that humans use on each other: for example, pretending to be someone else or saying that the request is just a joke. But other effective jailbreaking techniques are things no human would ever fall for. One group found that if they used ASCII art (constructions of symbols that look like words or pictures) to pose dangerous questions, like how to build a bomb, the LLM would answer them willingly.

Humans may occasionally make seemingly random, incomprehensible, and inconsistent mistakes, but such occurrences are rare and often indicative of more serious problems. We also tend not to put people exhibiting these behaviors in decision-making positions. Likewise, we should confine AI decision-making systems to applications that suit their actual abilities—while keeping the potential ramifications of their mistakes firmly in mind.

This essay was written with Nathan E. Sanders, and originally appeared in IEEE Spectrum.

President Biden has signed a new cybersecurity order. It has a bunch of provisions, most notably using the US governments procurement power to improve cybersecurity practices industry-wide.

Some details:

The core of the executive order is an array of mandates for protecting government networks based on lessons learned from recent major incidents­—namely, the security failures of federal contractors.

The order requires software vendors to submit proof that they follow secure development practices, building on a mandate that debuted in 2022 in response to Biden’s first cyber executive order. The Cybersecurity and Infrastructure Security Agency would be tasked with double-checking these security attestations and working with vendors to fix any problems. To put some teeth behind the requirement, the White House’s Office of the National Cyber Director is “encouraged to refer attestations that fail validation to the Attorney General” for potential investigation and prosecution.

The order gives the Department of Commerce eight months to assess the most commonly used cyber practices in the business community and issue guidance based on them. Shortly thereafter, those practices would become mandatory for companies seeking to do business with the government. The directive also kicks off updates to the National Institute of Standards and Technology’s secure software development guidance.

More information.

Is there nothing that squid research can’t solve?

“If you’re working with an organism like squid that can edit genetic information way better than any other organism, then it makes sense that that might be useful for a therapeutic application like deadening pain,” he said.

[…]

Researchers hope to mimic how squid and octopus use RNA editing in nerve channels that interpret pain and use that knowledge to manipulate human cells.

Blog moderation policy.

I am always interested in new phishing tricks, and watching them spread across the ecosystem.

A few days ago I started getting phishing SMS messages with a new twist. They were standard messages about delayed packages or somesuch, with the goal of getting me to click on a link and entering some personal information into a website. But because they came from unknown phone numbers, the links did not work. So—this is the new bit—the messages said something like: “Please reply Y, then exit the text message, reopen the text message activation link, or copy the link to Safari browser to open it.”

I saw it once, and now I am seeing it again and again. Everyone has now adopted this new trick.

One article claims that this trick has been popular since last summer. I don’t know; I would have expected to have seen it before last weekend.

According to a DOJ press release, the FBI was able to delete the Chinese-used PlugX malware from “approximately 4,258 U.S.-based computers and networks.”

Details:

To retrieve information from and send commands to the hacked machines, the malware connects to a command-and-control server that is operated by the hacking group. According to the FBI, at least 45,000 IP addresses in the US had back-and-forths with the command-and-control server since September 2023.

It was that very server that allowed the FBI to finally kill this pesky bit of malicious software. First, they tapped the know-how of French intelligence agencies, which had recently discovered a technique for getting PlugX to self-destruct. Then, the FBI gained access to the hackers’ command-and-control server and used it to request all the IP addresses of machines that were actively infected by PlugX. Then it sent a command via the server that causes PlugX to delete itself from its victims’ computers.

A very security-conscious company was hit with a (presumed) massive state-actor phishing attack with gift cards, and everyone rallied to combat it—until it turned out it was company management sending the gift cards.

This is a current list of where and when I am scheduled to speak:

• I’m speaking on “AI: Trust & Power” at Capricon 45 in Chicago, Illinois, USA, at 11:30 AM on February 7, 2025. I’m also signing books there on Saturday, February 8, starting at 1:45 PM.
• I’m speaking at Boskone 62 in Boston, Massachusetts, USA, which runs from February 14-16, 2025.
• I’m speaking at the Rossfest Symposium in Cambridge, UK, on March 25, 2025.

The list is maintained on this page.

It was created in 1973 by Peter Kirstein:

So from the beginning I put password protection on my gateway. This had been done in such a way that even if UK users telephoned directly into the communications computer provided by Darpa in UCL, they would require a password.

In fact this was the first password on Arpanet. It proved invaluable in satisfying authorities on both sides of the Atlantic for the 15 years I ran the service ­ during which no security breach occurred over my link. I also put in place a system of governance that any UK users had to be approved by a committee which I chaired but which also had UK government and British Post Office representation.

I wish he’d told us what that password was.

Not sure this will matter in the end, but it’s a positive move:

Microsoft is accusing three individuals of running a “hacking-as-a-service” scheme that was designed to allow the creation of harmful and illicit content using the company’s platform for AI-generated content.

The foreign-based defendants developed tools specifically designed to bypass safety guardrails Microsoft has erected to prevent the creation of harmful content through its generative AI services, said Steven Masada, the assistant general counsel for Microsoft’s Digital Crimes Unit. They then compromised the legitimate accounts of paying customers. They combined those two things to create a fee-based platform people could use.

It was a sophisticated scheme:

The service contained a proxy server that relayed traffic between its customers and the servers providing Microsoft’s AI services, the suit alleged. Among other things, the proxy service used undocumented Microsoft network application programming interfaces (APIs) to communicate with the company’s Azure computers. The resulting requests were designed to mimic legitimate Azure OpenAPI Service API requests and used compromised API keys to authenticate them.

Slashdot thread.

News:

A sponge made of cotton and squid bone that has absorbed about 99.9% of microplastics in water samples in China could provide an elusive answer to ubiquitous microplastic pollution in water across the globe, a new report suggests.

[…]

The study tested the material in an irrigation ditch, a lake, seawater and a pond, where it removed up to 99.9% of plastic. It addressed 95%-98% of plastic after five cycles, which the authors say is remarkable reusability.

The sponge is made from chitin extracted from squid bone and cotton cellulose, materials that are often used to address pollution. Cost, secondary pollution and technological complexities have stymied many other filtration systems, but large-scale production of the new material is possible because it is cheap, and raw materials are easy to obtain, the authors say.

Research paper.

Blog moderation policy.

404 Media is reporting on all the apps that are spying on your location, based on a hack of the location data company Gravy Analytics:

The thousands of apps, included in hacked files from location data company Gravy Analytics, include everything from games like Candy Crush to dating apps like Tinder, to pregnancy tracking and religious prayer apps across both Android and iOS. Because much of the collection is occurring through the advertising ecosystem­—not code developed by the app creators themselves—­this data collection is likely happening both without users’ and even app developers’ knowledge.

It’s being actively exploited.

From the Washington Post:

The sanctions target Beijing Integrity Technology Group, which U.S. officials say employed workers responsible for the Flax Typhoon attacks which compromised devices including routers and internet-enabled cameras to infiltrate government and industrial targets in the United States, Taiwan, Europe and elsewhere.