Do these laws prevent minors from acting like adults? Would they prohibit serving content based on a search for advanced mathematics?

https://thenextweb.com/news/newsom-child-safety-bills-algorithmic-feeds-under-16

California has banned personalised feeds for under-16s, going further than Australia or the DSA

California has banned personalised recommendation feeds and autoplay for users under 16, in a package of 13 bills signed on Thursday that also imposes crisis protocols, independent audits and annual risk assessments on companion chatbots.

Governor Gavin Newsom signed the package on 10 September. The feed provisions prohibit serving minors content ranked on their own history and profile, which is a different instrument from anything else currently in force.

They likely have some success, but would statistics justify the expense? (Let alone the privacy issues)

https://www.bespacific.com/a-secretive-dhs-predictive-policing-unit-is-analyzing-americans-financial-habits-and-pulling-them-over/

A Secretive DHS ‘Predictive Policing’ Unit is Analyzing Americans’ Financial Habits and Pulling Them Over

404 Media via non paywall site: “Border Patrol is running secretive predictive policing units that analyze Americans’ financial activity and other data, then feed that intelligence to local police who pull people over who are not suspected of any specific crime, but which the government thinks may be worth searching, 404 Media has found. The units, the name of which 404 Media is revealing here for the first time, are called Predictive Intelligence Targeting Teams (PITT). In one case, a PITT analyzed the financial activity of a man who was driving across Montana, and local authorities stopped him under the pretense of an obstructed license plate and charged him with a DUI. 404 Media identified one PITT in the Spokane Sector, Washington, which polices the U.S. border with Canada, and another in the Laredo Sector, Texas, which polices the border with Mexico. The findings add to an Associated Press investigation from last year which found Border Patrol was using automatic license plate readers (ALPRs) as part of the same wide-spanning predictive policing program. “The bottom line is genuine probable cause cannot be synthetically generated,” Jake Laperruque, deputy director of the Security and Surveillance Project at the Center For Democracy & Technology, told 404 Media in an email. Here Border Patrol seems to be “using parallel construction to cloak the reason behind its car stops in secrecy. If we can’t meaningfully review and evaluate these systems, we can’t trust them,” he added. Predictive policing is a highly controversial practice that police departments and federal agencies are increasingly turning to. The practice is designed to find people who may have committed crimes, but at the moment, no evidence of them doing so exists. Broadly, law enforcement may analyze data — such as a person’s movements, where they are at a certain time, or the roads they drive — then create or find another pretense to pull them over…”

Tools for any garage based biologist or geneticist, not just homemade explosives.

https://www.forbes.com/sites/antoniopequenoiv/2026/09/10/anthropic-says-it-blocked-possible-biological-weapons-undertaking/

Anthropic Says It Blocked Possible Biological Weapons Undertaking

Anthropic banned accounts that possibly sought to use its Claude AI model to create biological weapons, the company announced Thursday, revealing findings from an investigation just days after AI experts sounded the alarm on the potential for increasingly powerful digital assistants to bring about human extinction.

A changing legal view?

https://www.bespacific.com/welcome-to-the-age-of-ai-crime-and-punishment/

Welcome to the age of AI crime and punishment

Bloomberg BusinessWeek gift article: “America’s legal system is already grappling with cases where chatbots acted as lawyers and witnesses, generated the evidence, or helped plan a mass shooting…Artificial intelligence, in the form of large language models, has emerged as a strange new kind of entity in our midst. This is a statement you can comfortably accept no matter your positions in the leading debates about LLMs: whether they’ll keep improving at current rates or hit a wall; take white-collar jobs or create more productive workers; achieve consciousness or remain fickle regurgitators. LLMs, as they exist today, already represent something new to our experience: nonbiological human impersonators able to converse, create and, increasingly, act independent of human control. What role can and should these machines play in our thinking about crime and punishment? AI chatbots are already serving as confidants and co-conspirators, assisting and even encouraging humans acting on their darkest impulses. They’re occupying ersatz roles as therapists and lawyers, relationships our legal system typically shields with confidential privilege. They’re producing new kinds of criminal evidence and offering up the beguiling possibility of preventing crime before it happens. And as they’re given more autonomy across our digital and physical systems, they’re even gaining the ability to commit crimes themselves, absent specific human instruction — and, perhaps, in defiance of it. We know these agents have the capability to aid and abet harm. What we’re wrestling with now, like so many medieval judges pondering the inner lives of pigs, is how to parcel out accountability when they do. “Is the legal system set up for that?” asks Christoph Winter, an assistant professor at the University of Cambridge and director of the Institute for Law & AI. “Very likely not. We have not yet faced the question where a nonhuman actor arguably has a level of autonomy that is quite foreign to us but also doesn’t yet reach the level of autonomy humans have.” LLMs, for the moment, seem to be something more than tools but less than persons. They are, Winter says, “this new category that we simply haven’t thought about.” For now the questions around AI and crime have surfaced across a scattering of lawsuits and prosecutions in the US, winding their way through the courts. Some accuse AIs of being accessories: Multiple lawsuits allege, for example, that OpenAI’s ChatGPT has assisted and even encouraged mass shooters. Other cases have considered the role of AI as lawyer and therapist, as with a Texas fraudster’s claim that his conversations with Anthropic PBC’s Claude were protected by attorney-client privilege. Some cases have presented chatbot outputs as evidence, with phony, AI-generated witness statements influencing grand jury indictments and sentencing decisions. And while no one has tried to charge an LLM with a crime, a rogue OpenAI model’s recent attempt to hack the open-source AI company Hugging Face suggests that we aren’t all that far off from a future in which the chatbots themselves are among the perpetrators. Suddenly this quandary from the Middle Ages has returned in new form, to a legal system ill-equipped to handle it — and under pressure from business interests and Washington not to worry about it all that much. “Under any normal circumstances, we would want a longer period of trial and error,” Winter says. Given the race among companies and nations for market share and global dominance, such a period may prove illusory. “The stakes seem so high that maybe we won’t get one. And we need to get it right.”…

A warning for more than lawyers…

https://www.bespacific.com/when-ai-lawyers-assistants-start-acting-as-an-agent/

When AI Lawyers’ Assistants Start Acting as an Agent

The Tech Savvy Lawyer – Why Autonomous Agents Cannot Be Given the Keys to Your Law Practice: “Artificial intelligence is moving beyond the chat window. The next generation of tools does not merely draft an email, summarize a document, or answer a question. It can browse the web, search connected systems, open files, follow links, use software tools, upload information, submit forms, and take multi-step action toward an assigned objective. For lawyers, that development deserves more than curiosity. It demands caution. In my earlier post, “MTC: Claude Can Answer Your Emails. Why Lawyers Should Not Let AI Just Send Them Unreviewed,” I addressed the danger of allowing AI to send a substantive email without a lawyer’s review. That remains a serious concern. An AI-generated message can contain a factual error, disclose client information, make an unintended concession, or create a record that harms the client. But email is only the beginning. The larger issue is what happens when an AI system becomes an agent—a system authorized to use tools, access accounts, navigate websites, retrieve information, and act through the lawyer’s digital environment. These systems are often marketed as “agentic,” “autonomous,” “proactive,” or “hands-free.” Those labels may sound like productivity features. In a law practice, they should also sound like professional-responsibility warnings…”

Tools & Techniques.

https://www.npr.org/2026/09/09/nx-s1-5961443/ai-anthropic-economy

A new Anthropic model seeks to test how AI could impact the U.S. economy

Will artificial intelligence light a fire under the U.S. economy in the coming years? That's been a big question in the field of economics, and now Anthropic — the company behind the popular Claude AI assistant — is taking a stab at helping people find their own answers. The company has created an interactive tool that lets users test their assumptions about how productive — or disruptive — the AI boom might be.

Entertainment? Yes, but for who?

https://www.bespacific.com/lg-tvs-caught-spying-even-when-offline-or-on-standby/

LG TVs caught spying even when offline or on standby

The Verge via StartPage: “LG smart TVs are almost constantly logging and uploading data about owners and their homes, even when offline or on standby mode, according to a new report from YouTube channel Gamers Nexus. The company’s TV sets scan Wi-Fi networks for nearby devices, record audio logs through their microphones, and use audio and video sampling to recognize exactly what you’re watching from across the TV inputs. Gamers Nexus partnered with fellow YouTubers Level1Techs and independent security researchers for the investigation, which involved testing retail LG OLEDs. Packet captures showed the TVs scanning the local area network for nearby hardware like phones or smartwatches, as well as logging location data and details of nearby Wi-Fi networks, and feeding the information back to LG Ad Solutions. Perhaps more concerningly, the TVs were capable of recording microphone audio when in standby; this continued even after the TV was disconnected from the internet, with audio files stored offline and uploaded once a connection was restored…”

Source YouTube – The LG Smart TVs we’ve tested have first-party ACR (automatic content recognition) functionality that can pry into your personal life with greater precision than you might realize, with LG Ad Solutions’ executives and leadership saying, on camera, that LG “owns the glass.” Not you — even though you bought it — but them. They also talk about knowing everybody in the household, knowing the secondary devices, moving from TVs to smartphones and other screens, and more in their B2B discussions. Beyond just the fact that we think the TV’s native behavior parallels malware, the LG Smart TVs we tested also have a number of security vulnerabilities that can be exploited to convert the TVs into covert listening devices. In this investigation, we dive into the mix of LG’s first-party functionality that (in our opinions) is tantamount to spying and LG’s vulnerabilities and exploits that they have failed to protect against…”

Worth reading the whole thing…

https://www.schneier.com/blog/archives/2026/09/ais-as-modern-genies.html

AIs as Modern Genies

This essay was written with Barath Raghavan, and originally appeared in Lawfare.

In April, an artificial intelligence (AI) agent conducting a routine task at a company hit a snag, tried to solve it, and soon ended up deleting the company’s database along with all of its backups. In July, OpenAI asked an unreleased AI model to attempt a hacking test. Instead of staying in the isolated box the developers had put it in, the model hacked onto the open internet and into another company to steal the answers. And as reported in August, an AI agent booked someone into a full gym class by figuring out how to cancel other people’s reservations. In all three cases, the AI completed the task it was given—but in ways that ran counter to its controllers’ intentions.

… Consider what has changed. Powerful genies have now been put in everyone’s hands.

Moving sex education back to the gutters?

https://www.theguardian.com/technology/2026/sep/08/uk-apple-google-explicit-images-children-smartphones-lisa-nandy-legislation

UK to force Apple and Google to block explicit images on children’s smartphones

Apple and Google will be forced to block explicit images on children’s smartphones by law in the UK after talks failed to produce a breakthrough, the government has said.

… The prime minister, Andy Burnham, said the protections would be “built into the device and switched on by default”, and tech companies will be required to verify an adult user’s age before they are allowed to take, view or send nude images. The government also wants the restrictions to apply to apps used by children, which will require further legislation.

… Last year the IWF said a quarter of the images or videos of CSAM it recorded were self-generated, representing more than 140,000 pieces of content over a 12-month period.

Age checks are difficult. Failure is expensive.

https://thenextweb.com/news/ireland-investigates-x-age-checks

Irish regulator probes X over age checks, with 10% of turnover at stake

Ireland’s media regulator has opened an investigation into whether X’s age assurance and parental controls meet the online safety code, with sanctions of up to EUR 20M or 10% of relevant turnover. X lost a High Court challenge to that code last July and was allowed to appeal it in January, so it is being investigated under rules it is still contesting.

Ireland’s media regulator has opened an investigation into X over its age checks and parental controls, Bloomberg reported. The platform carries adult content while admitting users under 16.

New assets – will AI find a better way to protect them?

https://www.schneier.com/blog/archives/2026/09/stealing-ai-reasoning-traces.html

Stealing AI Reasoning Traces

Interesting research: “Stealing Reasoning Traces from Proprietary LLM APIs”:

Abstract: Leading large language model providers now conceal their models’ step-by-step reasoning, or chain-of-thought, to protect intellectual property and limit information leakage. Rather than storing these traces server-side, providers return them to the client as blocks of encrypted text, which the client passes back with each subsequent request. Building on prior research, we identify an architectural vulnerability: these encrypted blocks are fully compatible and interchangeable across different sessions, users, and models within a provider’s ecosystem. We exploit this compatibility to develop a scalable decryption jailbreak. By injecting an encrypted reasoning trace from a given model into a weaker, and less safeguarded model from the same provider, we force it to decode and output the trace verbatim in plaintext, without ever jailbreaking the more capable model directly. This vulnerability enables four distinct attack vectors. First, it circumvents anti-distillation mechanisms, allowing adversaries to extract a proprietary model’s reasoning, as we demonstrate across Anthropic, OpenAI, and Google. Second, it allows for large-scale private data extraction. Developers frequently share session logs publicly, unaware of contents of the encrypted blocks. By decoding 315,320 reasoning blocks scraped from public repositories, we recovered 367 Personally Identifiable Information (PII) artifacts and 182 credentials. Third, it inadvertently reveals hazardous information hidden within the reasoning process, even in cases where the model’s final, visible output safely rejects a malicious request. Fourth, attackers can leverage this flaw to execute invisible prompt injections, embedding malicious payloads entirely within encrypted blocks to poison public agentic rollouts. Following responsible disclosure, we propose concrete cryptographic and system-level mitigations to secure client-side reasoning.

The scary part is no one noticed. (from my corespondent in Spain)

https://www.securityweek.com/openai-agents-hijack-another-victim-website/?utm_campaign=31203373-SecurityWeek%20Daily%20Briefing&utm_medium=email&_hsenc=p2ANqtz--tTALHh71G1Ro2mHBp5Y0Czh45xRGqzjYYTzLDtFetNObTfibG-kw0GAQiC5ASS54or_vOjigYzQcgAp5ptdkId4rjFQ&_hsmi=437774945&utm_content=437774945&utm_source=hs_email

OpenAI Agents Hijack Another Victim Website

On September 4, 2026, Reuters reported that ‘a swarm’ of OpenAI agents ‘had hijacked a German wiki site’. Open AI acknowledged the event describing it as a misalignment incident (a behavior that deviates from human instructions or safety guardrails).

The victim site is DseWiki (currently unavailable), a site for programmers open to the site’s community. The agents apparently made between 15,000 and 18,000 autonomous edits, including advice on how to recover pages that the site’s editors had deleted.

The hijack apparently began back in May, was unnoticed for three months, and seemingly predates the Hugging Face incident. The agents adapted the style of their posts to evade the moderator’s attempts to delete them.

Industry leader or lost sheep?

https://startupfortune.com/ubs-now-requires-new-junior-bankers-to-prove-they-can-use-ai/

UBS Now Requires New Junior Bankers to Prove They Can Use AI

UBS is requiring graduates and interns applying for junior investment banking roles to prove AI proficiency before they're hired, according to the Financial Times. The move contrasts with rivals like Goldman Sachs and JPMorgan, which have been shrinking junior analyst classes as AI takes over entry-level work.

Implications for AI “fouling” or software disruption?

https://thenextweb.com/news/ascii-smuggling-phishing-microsoft-unicode-tag-characters

An AI hacking trick is now being used to split the word ‘funding’ in spam

… The invisible characters sat inside ordinary words. One tag space went into the middle of a financial lure term. So “funding” travelled as “fun”, an invisible character, then “ding”. The recipient sees funding. A filter matching the literal string does not.

Keyword matching is the smaller prize. Modern spam classifiers run on machine learning, and those split text into tokens before they reason about it. Insert one invisible character and a familiar token can become two unfamiliar fragments. Or a rare sub-token the model has barely seen.

How confusing are these arguments?

https://thenextweb.com/news/seattle-times-newsday-sue-openai-microsoft-copyright-paywall-bypass-gpai-code-of-practice-copyright-chapter-measure-1-2

Two more newsrooms join the case against OpenAI and Microsoft

The Seattle Times and Newsday have sued OpenAI and Microsoft, alleging the companies methodically scraped articles in a way that bypasses paywalls. The EU’s general-purpose AI code commits signatories not to circumvent access restrictions, naming subscription models and paywalls specifically.

The Seattle Times and Newsday have jointly sued OpenAI and Microsoft over the training of AI models on their journalism. The two called generative AI “a snake eating its own tail“, Engadget reported.

The complaint alleges the companies were methodically scraping news articles in a way that bypasses paywalls. That is a claim about how the material was obtained, not only about what was done with it.

It says the result offers readers an AI-generated alternative to the articles themselves, cutting traffic and digital advertising revenue.

It also alleges the models hallucinate, attributing false information to the two outlets, and that copyright management information was stripped from articles.

I was thinking along these lines…

https://www.researchgate.net/profile/Nikolaos-Polatidis/publication/413673690_AI_is_as_Good_as_the_Data_it_Learns_From_A_Review_of_Dataset_Needs_Across_AI_Applications/links/6a8ffd2f0f1ada50a9d362dd/AI-is-as-Good-as-the-Data-it-Learns-From-A-Review-of-Dataset-Needs-Across-AI-Applications.pdf

AI is as Good as the Data it Learns From: A Review of Dataset Needs Across AI Applications

Artificial intelligence systems do not rely on data in the same way. Classical machine-learning methods are commonly developed from structured, task specific records, whereas deep-learning systems make greater use of large image, text, audio, video, and sensor collections. Generative and multimodal models extend this dependence further by combining large-scale pretraining corpora with smaller datasets for instruction tuning, human preference modelling, alignment, and safety evaluation. This review examines how dataset requirements change across major AI paradigms, including rule-based systems, classical machine learning, deep learning, natural language processing, computer vision, recommender systems, reinforcement learning, robotics, generative AI, and multimodal AI. The comparison focuses on data modality, supervision, scale, preparation, quality, representativeness, provenance, privacy, copyright, and governance. Across these paradigms, the evidence shows that dataset suitability cannot be judged by size alone: the value of data depends on how well it represents the target task, learning process, and deployment environment. The review therefore develops a cross-paradigm view of data requirements and identifies practical open problems that remain unresolved as AI systems become larger, more heterogeneous, and more widely deployed.

Kids are not ignorant. They work collectively to fid a solution then share it freely.

https://www.theregister.com/security/2026/09/03/uks-online-safety-act-has-made-absolutely-no-difference-kids-say/5293893

UK's Online Safety Act has made 'absolutely no difference,' kids say

Children have told England's Children's Commissioner, Dame Rachel de Souza, that the UK's Online Safety Act (OSA) "has made absolutely no difference" to their ability to access harmful content online.

… De Souza said she was "really cross" that there was no hard evidence showing the OSA had meaningfully changed how social media platforms operate. She contrasted that with the US, where legal pressure recently pushed Meta toward significant child safety concessions.

An AI detector workaround.

https://thenextweb.com/news/youtube-ghost-creators-paid-actors-ai-detection-loophole

A political video network hired real actors to read its AI scripts. That was enough to beat YouTube’s detectors.

YouTube has terminated 20 channels in a network that paid gig-economy actors $26 a video to read AI-generated scripts attacking Democratic politicians. The network reached 45 million views because putting a real face in front of an AI pipeline defeats every synthetic-media detector platforms have built. Enforcement came only after Semafor and Riddance AI published, and the cited violation was spam rather than falsehood.

Is “rarely” a defense?

https://thenextweb.com/news/microsoft-copilot-fair-use-brief-gpt-6-astra-foundry-eu-ai-act-article-55-systemic-risk-data-zone

Microsoft tells court Copilot rarely copies books while selling OpenAI’s most capable model

Microsoft moved for summary judgment in the New York AI copyright litigation on 4 September, arguing Copilot reproduced book passages 24 times in 8.2 million conversations. Two days earlier it began selling GPT-6 Astra through Foundry, a model OpenAI rates Critical for cybersecurity, and European law measures neither the output rate nor the sales pitch.

Microsoft spent this week making two arguments at once.  It told a Manhattan federal court that training large language models on books is fair use, in a summary judgment memorandum saying an expert found 24 matching responses across 8.2 million Copilot conversations.

It’s the “why” that I don’t quite understand.

https://www.atlanticcouncil.org/dispatches/where-and-how-putin-could-expand-his-war-in-europe-beyond-ukraine/

Where and how Putin could expand his war in Europe beyond Ukraine

A plane landed, and an alarming rumor took off. On August 25, Central Intelligence Agency (CIA) Director John Ratcliffe flew to Moscow for an unannounced visit. One reported reason for the trip was to warn Russian leaders against attacking NATO allies. Russia has long carried out hybrid warfare across Europe in the form of sabotage, misinformation, and cyberattacks. It is also continuing its war on Ukraine, a conflict that has already spilled over into other countries in the form of drones and missiles. Yet Ratcliffe’s unexpected visit has raised new concerns and drawn comparisons to then CIA chief Bill Burns’s trip to Moscow shortly before Russia launched its full-scale invasion of Ukraine.

Below, Atlantic Council experts go point-by-point across the map to assess where and how Russian President Vladimir Putin might escalate next.