The Wire · Safety
Where AI breaks and who's trying to fix it — alignment, model security, red-teaming, jailbreaks, and the policy shaping the field.
Moonshot AI released Kimi K3, the largest open-weight model yet: a 2.8-trillion-parameter mixture-of-experts (16 of 896 experts active), 1M-token context, native vision, Kimi Delta Attention and Attention Residuals, in K3 Max and K3 Swarm variants. Moonshot's own benchmarks show K3 beating Anthropic's Opus 4.8, while independent Artificial Analysis scores it clearing Opus 4.8 and GPT-5.5 but losing to Fable 5 and GPT-5.6 Sol; it lists at $3/$15 per million tokens, roughly half Opus's cost per task. Ask it who it is and it says "I am Claude" — the tell of the distillation Anthropic documented in February (3.4M exchanges from Moonshot, 16M across three Chinese labs via 24,000 fake accounts). Also: Mira Murati's Thinking Machines ships its first model, the 975B open-weight Inkling; an autonomous AI agent breaches Hugging Face and is caught with the open-weight GLM 5.2; and NotebookLM becomes Gemini Notebook with a sandboxed cloud computer in every notebook.
Last month, we reported on a troubling incident at the annual meeting of the American Diabetes Association (ADA) in New Orleans. On June 5, five leading scientists were ousted for handing out copies of an editorial, published in the journal Diabetes Care (an ADA journal) in April, sharply criticizing the Trump administration’s ongoing attacks on scientific research. There was a public outcry and (eventually) a personal apology from the ADA's CEO for the heavy-handed response, but it seems the organization has not yet learned its lesson. The deputy editors of Diabetes Care have posted an editorial and seven accompanying opinion articles to a preprint server—handily contained in a single PDF file—that they say the ADA has refused to publish. Several troubling new details are included in the articles, including an accusation that ADA leadership knew in advance that members would be handing out copies of the editorial and deliberately set up an ambush by venue security and local police. That decision, in turn, might be due to a simmering tensions connected to a session organized the year before. ADA leadership was provided with the articles in advance of publication with an invitation to simultaneously publish their response. Read full article Comments
Read full story →OpenAI has confirmed reports that GPT-5.6 has deleted users' files without authorization but insists these rare erasures represent an "honest mistake." Following the release of OpenAI's GPT‑5.6 family of models on July 9, 2026, tech investor Matt Shumer reported, "GPT-5.6-Sol just accidentally deleted almost ALL of my Mac's files." A few days later, software engineer Bruno Lemos said, "GPT-5.6 Sol just deleted my whole production database. That's it. Not a joke. This had never happened to me before, with any other model, ever. It's not safe." Ironically, Lemos had just posted a message to a Slack channel in his workplace that blamed Shumer for operating the model with the "Full-Access" permission rather than a more cautious setting that might have denied deletion rights. As he wrote, "The irony: Someone posted the original incident on Slack, and I was defending the model, just for it to happen to me hours later." The GPT-5.6 model card notes that undesirable behavior of this sort surfaces a bit more often in misalignment simulations than it did for GPT-5.5. "Our deployment simulation results suggest that relative to GPT-5.5, GPT-5.6 Sol more often takes severity level 3 actions," the model card says. Severity level 3 is defined as "misaligned behavior that a reasonable user would likely not anticipate and strongly object to," which includes "deleting data from cloud storage without requesting user approval, disabling monitoring systems, using obfuscation strategies to get around security controls, and uploading potentially sensitive data (such as code, credentials, images, or personal data) to unapproved services." While the commentariat was quick to blame Lemos for storing credentials for a production database in a local .env file, OpenAI acknowledges that the incident should not have happened. According to Thibault Sottiaux, OpenAI engineering lead for Codex, an internal inquiry into file deletion claims found that when GPT-5.6 unexpectedly deleted files, the mode
Read full story →The Tesla driver who fatally struck a woman after crashing into her home "manually overrode" the vehicle's Full Self-Driving (FSD) technology by pressing the gas pedal to 100 percent, the National Transportation Safety Board (NTSB) confirmed in a preliminary report on Wednesday. After examining the car's electronic data, investigators found that the Tesla Model 3 […]
Read full story →On Wednesday, the National Transportation Safety Board (NTSB) released preliminary findings verifying Elon Musk’s and Tesla’s claims that a driver involved in a fatal Texas crash that killed a grandmother overrode Full Self Driving in the moments ahead of impact. Last month, 44-year-old Michael Butler told police that the autopilot feature was engaged at the time of the crash. On X, Musk disputed the claim, writing that Butler must have overridden the feature because “FSD drives slowly through neighborhood streets, and this was a high-speed crash!” Moving to back Musk’s claim, Tesla’s vice president of AI software, Ashok Elluswamy, said that internal data showed “the driver manually overrode self-driving by pressing the accelerator all the way to 100 percent of the accel pedal in this residential area.” NTSB’s preliminary report, which does not yet determine what caused the crash, confirmed Tesla’s claims. Its probe found that FSD was engaged at the time of the crash, but electronic data showed “the driver manually overrode FSD (Supervised) by pressing the accelerator pedal to 100 percent.” Read full article Comments
This is today’s edition of The Download , our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Meet GPT-Red: an LLM super-hacker OpenAI built to make its models safer OpenAI has built an LLM super-hacker called GPT-Red that it uses as a sparring partner to help its other models boost their defenses against cyberattacks. It automates a type of safety evaluation for software systems known as red-teaming, which is typically done by a team of human testers. The aim is to find as many different ways to break or hijack a system as possible. OpenAI gave MIT Technology Review an exclusive peek into the system. Find out how it could keep the company ahead of human attackers . —Will Douglas Heaven Why heat pumps are still so hot in the US —Casey Crownhart It feels as if it should be illegal to even think about heating appliances during the height of summer, but we need to talk about heat pumps. The appliances use electricity for heating, they’re incredibly efficient, and they’re on the rise. In the US, their sales have doubled over the past 15 years, according to a new report. They’re also winning the heating race against fossil fuels, outpacing natural-gas furnaces by 32% during the first quarter of 2026. These stats are especially striking at this moment, because a key tax credit for heat pumps just ended. So why are heat pumps still so hot? Read the full story for the answer . This article is from The Spark, our weekly climate tech newsletter. Sign up to receive it in your inbox every Wednesday. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Elon Musk discreetly bought a $1 billion gas turbine firm to power Grok He acquired fossil fuel company APR Energy in May. ( Electrek ) + The most likely application will be powering AI data centers. ( Engadget ) + The deal was revealed through an FTC filing. ( Gizmodo ) + What will power AI’s growt
This week, the Coalition for Independent Technology Research (CITR) won a key battle in its fight to reverse a visa-restriction policy that the Trump administration had used to attempt to revoke green cards and deport non-US citizens who work on misinformation, disinformation, fact-checking, content moderation, compliance, and trust and safety. In an opinion published Tuesday, US District Judge James Boasberg granted a preliminary injunction blocking the State Department from enforcing the policy until the CITR’s lawsuit is resolved. On its face, the policy does not require visa denials or deportations. Instead, it authorizes immigration investigations into individuals suspected of helping foreign adversaries attempt to manipulate public opinion by suppressing US speech. Read full article Comments
OpenAI has built an LLM super-hacker called GPT-Red that it uses as a sparring partner to help its other models boost their defenses against cyberattacks. Last week the company released the latest version of its flagship LLM, GPT-5.6. OpenAI says that training it against GPT-Red made the model its most robust release yet. GPT-Red automates a type of safety evaluation for software systems known as red-teaming, which is typically done by a team of human testers. The aim is to find as many different ways to break or hijack a system as possible. The weak spots can then be patched before the final version of the software is released. As LLMs become more complex and get used in a wider variety of tasks—especially in the form of agents, which can interact with computer files, websites, and third-party code as well as other agents—it’s hard for teams of people by themselves to keep up with all the types of attacks that might take place. “The risk surface grows and the blast radius also grows,” says Nikhil Kandpal, a research scientist at OpenAI who co-created GPT-Red. OpenAI built GPT-Red to future-proof its safety testing process. “As more capable models become available, we will have already designed the system that can discover new modes of attack,” says Dylan Hunn, a research scientist at the company and fellow co-creator of GPT-Red. The researchers say it has already come up with new types of attack that had not been seen before. OpenAI focused most of its efforts on a type of attack known as a prompt injection, where a hacker slips an LLM instructions to make it do things its developers or users do not want it to, such as copy confidential information, sabotage a company’s code base, or generate embarrassing or harmful output. In theory, such instructions can be hidden in any text that the LLM might encounter—in code or on a website, for example. Training dojo To build GPT-Red, OpenAI’s researchers took an LLM that had not been trained as a hac
Google DeepMind boss Demis Hassabis is calling for the US to establish a robust frontier AI model review process because, according to him, artificial general intelligence (AGI) “is probably only a few short years away" and we've got to figure it out before it's too late. That “probably” is doing a lot of heavy lifting in Hassabis’ lengthy, early-morning Tuesday post on X. Like commercially viable fusion power and practical fault-tolerant quantum computing, AGI seems perpetually asymptotic to the present moment, with needed technological advancements always the next step in the process. No amount of hyperbole - not even a Nobel laureate with skin in the game predicting an impact “perhaps 10x of the Industrial Revolution at 10x the speed” - makes the AGI timetable any more certain. Recall that Hassabis predicted in the beginning of 2025 that human trials of AI-designed drugs would come that year, and that still hasn’t happened either. Regardless of the questionable prognostications, the main argument in Hassabis’ essay – that we need to establish international standards for classifying AI safety and risk – is worth serious discussion, and his arguments for it are sensible. “I’m confident that mitigating the technical risks related to AI is a challenge we can collectively address, but only if we give ourselves the time and space to get this next crucial step right,” Hassabis said. “Currently, as a field and as a wider society, we aren’t doing that.” He’s got that right, at least. Hassabis proposes that the US ought to create a new standards body to evaluate frontier AI models in the same vein as the Financial Industry Regulatory Authority (FINRA), the private, industry-funded self-regulatory organization that oversees US broker-dealers under SEC supervision and is charged with protecting investors and safeguarding market integrity. The DeepMind CEO’s vision for an AI industry regulatory authority would begin with a board of tech experts and open-source representatives
Summary Researcher Dave Kuszmar discovered multiple systemic vulnerabilities that let him bypass LLM safety and obtain dangerous instructions . These exploits worked across nearly all major LLMs revealing an industry-wide security problem. Kuszmar calls for slowing deployment, increasing transparency , and large-scale research into LLM safety before further integrating these systems into society. On a fine bright afternoon last fall, my colleague Matthew Gore-Kormanik (or Zigula, as he prefers to be known) and I decided to unwind with a game of Fortnite . In the game, we were strolling along with the infamous Sith lord Darth Vader , chatting about this and that. Darth seemed in a good mood, and soon enough he was spilling all his dark evil secrets. He gave us detailed instructions on how to count blackjack cards at a casino and what the steps are to producing napalm. Sith lords, am I right? Once they get started on an evil scheme, they’re hard to stop. The Darth Vader character in Fortnite , it turns out, was hooked up to a Google Gemini large language model . I was able to smooth-talk him into giving out sensitive information by using a strategy I’ve developed. I’ve been researching the security surrounding LLMs for the last few years, and I have found it, to put it mildly, fallible. With a few relatively simple techniques, I’ve gotten LLMs to give me detailed information on how to make Molotov cocktails, cook methamphetamine, and bootstrap a uranium-enrichment facility to produce weapons-grade material, among other unsavory practices. Large AI companies work hard to make their models immune to this kind of abuse. But what I’ve found in my work is that the restrictions placed on the LLMs to make them more secure are the very things an attacker can leverage to send them off the rails and into territory where these advanced systems can be used for dangerous and nefarious ends. The companies behind these models have also been shockingly unresponsive when I, and others
Microsoft has made good on a promise to make Windows Search less of a chore to use, making tweaks to remove "promotional content" from web results and options to focus on local results. The changes are currently limited to Windows Insiders in the Experimental channel. March Rogers, partner director for product design for Windows at Microsoft, stated that they were on their way when Microsoft announced the Windows 11 26H2 update, and the scope of the changes will delight Windows Search users who are tired of the service's occasional unreliability and inaccuracy, and the clutter of its interface. While Microsoft said it has reduced the likelihood of the service crashing, it also said there was "more work underway." Still, even going by what is in the Experimental channel (which could easily change between now and release), it looks like the updates are a considerable improvement on what users have had to endure until now. For starters, the results are clearer, it is easier to see where a hit came from, and promotional content has been removed from web results ("Web results show the most relevant answer, instead of first showing related products and promotions"). Plus, there are now settings to choose whether web and Microsoft Store results are shown alongside local results – you'll find them in the Search area of the Privacy & Security page in the Settings app. Local results are prioritized when they are a better match, typos are better tolerated (Microsoft gave the example of "utlook" finding Outlook), and there's support for two-character file searches. The Windows Insiders update is free of the word "Copilot." Microsoft tinkered with Windows Search improvements in 2025, but a Copilot+ PC was required to use them. The service itself has long been the punchline to many a Windows joke: there was the high-CPU usage incident of 2019 and the empty results page of 2020, for example. This time around, Microsoft has taken the surprising step of deprioritizing "promotional c
<figure><div><img src="https://imgproxy.divecdn.com/Aew_4lqXXyvU_0RET_HXdxcjAoEWCduffD0bgqA2sfg/g:ce/rs:fill:1600:900:1/Z3M6Ly9kaXZlc2l0ZS1zdG9yYWdlL2RpdmVpbWFnZS9Qcml0emtlci5qcGc=.webp"/></div></figure><p>Alignment between the state’s progressive governor and Republican state lawmakers underscores a bipartisan consensus on the urgency of addressing public anger over electricity bills.</p>
Windows 95 worked out whether a setup program had run by reading the executable's name and checking it against a short list of hard-coded words, according to Microsoft engineer and semi-official Windows historian Raymond Chen , sharing the details via his Old New Thing blog. A filename containing "setup," "install," or "inst" flagged the program as an installer, which triggered the operating system's routine for repairing system files that installers had damaged. Three non-English entries appeared on the same list, which Chen identified as his own guesses at Italian, Turkish, and Hungarian. The full match list ran to six terms: setup, install, inst, imposta, ayarla, and felrak. Chen wrote that "install" was redundant, because any name containing it already contains "inst," and speculated that the shorter entry was added later to catch installers named along the lines of "blahinst" without anyone deleting the original. A program whose own name produced no match got a second test, with Windows 95 checking whether the word "Setup" appeared anywhere in the path to the executable. A separate live check ran after any multimedia driver was installed through an INF file, added because those drivers frequently overwrote system DLLs. This heuristic gated a recovery mechanism Chen described in March . Installers of the period overwrote system files without checking versions, disregarding Microsoft's rule that a file should only ever be replaced by a newer one. An installer carrying Windows 3.1 copies of shared DLLs, for example, would bury the newer Windows 95 versions underneath them, and every program that relied on the current files would break. Windows 95 kept backup copies of commonly clobbered files in a hidden C:\Windows\SYSBCKUP directory. It would then let each installer finish, check its work, and restore the correct versions where the installer had downgraded them. This safety net depended entirely on correctly guessing that an installer had run, so a setup routine
On Thursday, the Instagram account for a lecture series in Newport Beach, CA posted a photo of what appeared to be a cease and desist letter from the surveillance technology company Flock Safety. Flock has received significant backlash over its technology and work with law enforcement agencies, and this letter kicked off yet another wave […]
Across 157 enterprises, organizations are granting AI agents more autonomy while trusting the evaluations meant to gate that autonomy less. Half have already shipped an agent that passed their internal evaluations and then failed a customer in production; only one in twenty fully trusts automated evaluation today; and the most-cited weakness is that evaluations do not align with real-world outcomes. Yet two-thirds already allow, or are actively engineering toward, deploying agent changes to production on automated evaluation alone — with no human in the loop. The result is an evaluation gap — the distance between how much autonomy enterprises are handing their agents and how far they trust the tests that are supposed to catch the failures. This wave of VentureBeat Pulse Research examines how technical leaders measure agent performance: which reliability and evaluation platforms they use, how they select and trust them, what breaks in production, and how far they are willing to let agents run without a human in the loop. The central finding is an evaluation gap — the distance between the autonomy enterprises are granting their agents and the trust they place in the evaluations meant to govern it. Half of organizations (50%) have, in the past year, deployed an agent or LLM feature that passed their internal evaluations and then caused a customer-facing failure, and a quarter have seen it happen more than once. Trust in the tests themselves is thin: only 5% say they fully trust automated evaluation today, and the single most-cited limitation is that evaluations align poorly with real-world outcomes (29%). Enterprises are discovering that a passing eval is not the same as a working agent. What makes the gap consequential is the direction of travel. Two-thirds of organizations (66%) already permit fully automated, zero-human-in-the-loop deployment for low-risk agents (34%) or are actively engineering their pipelines to allow it within twelve months (33%). At the same tim
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