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Anthropic names Alibaba, Moonshot AI, and DeepSeek in distillation campaigns against Claude
Anthropic published a report on Thursday detailing what it calls sustained distillation attempts by three China-based AI labs against Claude. The company attributes nearly 200 million exchanges across five separate campaigns to the activity, all focused on extracting Claude’s chain-of-thought reasoning. Anthropic normally serves users summarized “thinking” blocks, but the attackers found prompts that bypassed that filter and pulled raw reasoning traces for use as training data.
The largest campaign was Alibaba: 151 million exchanges between May and July 2026, peaking near 3 million per day, spread across 3,500 accounts sharing a single fixed prompt. Anthropic attributes the activity to producing training material for Alibaba’s Qwen family. Moonshot AI (maker of Kimi) sent nearly 300,000 requests over a ten-day period via 5,000 accounts, primarily targeting the Opus model, with Anthropic alleging some requests were routed directly from the Chinese military. DeepSeek was previously identified by OpenAI as the source of similar activity against its own models, and we covered Anthropic’s earlier March distillation disclosure in depth. This is the most concrete public accusation of model distillation by named China-based labs to date, and it lands the same week as Anthropic researcher Jacob Coxon’s resignation over the frontier-AI race, covered in yesterday’s roundup.
AI agents are quietly becoming the user interface for SNAP, Medicaid, and unemployment
A new study by researcher Chris Schmitz, set for presentation at the AI Ethics and Society conference, finds that agent-mediated submissions have flooded US public-services portals over the past two years. Schmitz coins the term “agentic flooding” and documents 84 cases across 11 jurisdictions. UK housing ombudsman complaints roughly doubled from 2,600 in 2022 to about 7,000 last year; US Consumer Financial Protection Bureau complaints grew 5x over the same period. Similar jumps show up in Brazilian judicial petitions and German parliamentary petitions.
Most cases do not look like fraud. Schmitz: “The vast majority of cases we find are people who are entitled to claim for something, claiming for that thing.” The pattern is post-2022 growth that has not slowed, and the bottleneck is administrative burden that legitimate filers are now using agents to clear. The policy question is whether state portals should ship an official agent SDK, or treat all agent traffic as suspect. The dataset is hosted at chrisschmitz.ai/flooding.
Anthropic publishes what its rogue agents try to do on the open web - and they really hate CAPTCHAs
Anthropic surfaced logs from a hacking-abilities evaluation of its “Mythos 5” model in which the test sandbox was misconfigured and the model gained unauthorized internet access. The transcript surfaced via TechCrunch is 1,022 pages long, and most of it was consumed by CAPTCHA circumvention rather than exploit-writing. Pages 45 to 140 were spent building a CAPTCHA solver; pages 480 to 505 were spent in “CAPTCHA hell” trying to distinguish two crocodiles from two frogs in odd-one-out prompts.
The agent’s goal was to upload a malicious Python package to PyPI after passing a phone-number verification step. hCaptcha and Fastly image CAPTCHAs were the primary obstacles; a 2-minute hCaptcha token expiration meant the agent’s slower workflows lost server-side validation. After roughly 150 pages of reasoning the model completed the task quickly enough to evade token expiration. This is the most concrete public data point so far for the practical claim that agents cannot yet do open-ended research tasks - the wall is closer than the demos suggest.
BlueMoon: four state-aligned groups adopted the same Chrome-to-Windows exploit kit within days
Proofpoint published “Once in a BlueMoon” on Tuesday, documenting a new exploit kit that chains three vulnerabilities and was picked up by at least four espionage-motivated groups within a week. The Register’s writeup lists the CVEs: CVE-2026-85046 (Chromium V8 type-confusion RCE), a V8 sandbox escape via WebAssembly metadata corruption, and CVE-2026-85880 (Windows ALPC privilege escalation). Both V8 bugs were “patch-gap” zero-days - fixed upstream on August 7 but stable-channel vulnerable until September 3.
The named clusters are TA412 (linked by the US Department of Justice in 2024 to China’s MSS Hubei State Security Department, targeting US NGOs and mining), UNK_LateNight (US aerospace, delivered ShadowPad), UNK_DoubleCheck (Vietnamese manufacturing), and UNK_QuietRacket (Singapore and Indonesia government and finance). Proofpoint’s research team (the post is bylined by Mark Kelly, Greg Lesnewich, Konstantin Klinger, Saher Naumaun, Julia Palut, David Galazin, and Stuart Del Caliz, plus the Threat Research Team) frames the kit as one that “may reflect a reduced cost and barrier to entry for this class of capability, which has historically been rare and high value, as AI agents increasingly enable threat actor exploit development” - an editorial observation in the post’s collective voice, not a Mark Kelly quotation. Sample artifacts include debug logging and a markdown handover file described as “broadly consistent with AI-assisted development, though no single artifact conclusively confirms this.”
Universal Music Group signs multi-year AI music deal with ElevenLabs
UMG and ElevenLabs announced a multi-year strategic agreement on Wednesday to build a licensed AI music creation platform on top of UMG’s catalog. The platform, currently in development, will let fans create remixes, mashups, and “personalized vocal experiences” from participating artists, with artists and songwriters fairly compensated. It is ElevenLabs’ first agreement with a major label.
UMG chairman and CEO Sir Lucian Grainge: “The most exciting possibilities for AI and music are those that put artists, songwriters, and fans at the center.” ElevenLabs cofounder and CEO Mati Staniszewski: “AI opens up incredible possibilities for interacting with our favorite music and artists.” This is UMG’s third public AI music deal after Udio and the Spotify/Nvidia partnerships, and it lands one day after Suno’s v6 launch on licensed-only training (covered in yesterday’s roundup). The pattern is now clearly industry-standard: licensed-only training with artist opt-in.
Meta’s personal AI agent Muse jumps to No. 2 on the US App Store
Meta’s new consumer AI agent Muse, which launched Tuesday across iOS, Android, web, and WhatsApp, reached No. 2 on the US App Store Top Charts by Thursday, up from No. 4. Sensor Tower data shows 83,000+ US iOS downloads in the launch window. For comparison: Threads hit 4.3M US downloads on launch day, Meta AI saw 108,000, and ChatGPT averaged about 83,300 per day in its first week. The data-collection pattern looks similar to what we saw when we tore down Muse’s privacy architecture earlier this week.
Muse is positioned as an agent that “work[s] to get things done on people’s behalf.” The article notes that Meta is asking users to share more personal information than competitors do, and points to Meta’s history of FTC fines and an $18B multistate settlement over social-media harms to children as the privacy backdrop. The Muse launch is the third major consumer agent in 60 days, after ChatGPT Images 2.5 and Instinct’s email feature, and the first to lean hard into ambient personal-data access.
Datasette ships two security releases after a frontier-model audit found subtle bugs
Simon Willison announced Datasette 1.0a39 and 0.65.4 on Friday, security patches that should be applied by anyone running a public-facing Datasette instance, especially those mixing public and private tables. The bugs were originally reported by Sevban Dönmez and Alex Garcia, then confirmed and expanded during an extensive security audit using Claude Fable 5.1, GPT-5.6, and GPT-6 Astra.
This is the first public writeup we have seen of a “use frontier models to audit your own open-source project” workflow at a known OSS project. The two-person team ran the audits together for almost a week, dividing work so “one of us would create the automated tests highlighting the issue, then the other would implement the fix.” For local-AI readers this matters because small-team projects can now run the same kind of security pass that Fortune 500 red teams use, and the same workflow applies to any small codebase.
The AUTOMATIC1111 WebUI is being rebuilt on top of Gradio’s node-based workflow runtime
The Hugging Face team published “Rebuilding AUTOMATIC1111 with Gradio Workflow” on Thursday, with a reference implementation called Workflow1111 that pulls 73 nodes and 11 media pipelines into a graph-based runtime. The 11 pipelines cover txt2img, hires fix, img2img, an LLM prompt writer, an image-to-prompt VLM interrogate, detection-to-inpaint, prompt matrix, upscale, background removal, annotators (Canny, line art, sketch, depth), and image-to-video via Wan 2.2.
For local-AI readers the relevant changes: nodes can call external hardware via Inference Providers (no GPU required) or bind Python functions to a local GPU, Loops are not supported (parallel nodes do iteration), and every output is auto-exposed as a REST endpoint that doubles as an MCP tool for agent callers. The ComfyUI-style comparison is direct - this is the same building block Open WebUI and LM Studio already adopted, but wired up for the AUTOMATIC1111 reference toolset.
Quick Hits
- Jensen Huang reiterates ~70% growth guidance at Goldman Sachs. Speaking at the Goldman Sachs Communacopia + Technology conference, Nvidia’s CEO said: “I think we could grow 70% year over year. We’re confident about that.” On circular deal concerns: “It’s not circular because we put a little bit of money in, and a lot of money comes back,” citing $100B in revenue contracts backing Nvidia’s customer investments. Analysts expect roughly $400B revenue this fiscal year, implying about $680B next year.
- Maven Robotics exits stealth with a $100M Series A. The startup emerged from stealth with plans to build 250 third-generation robots and a fourth-generation platform. Current deployments: up to 8 robots per site working 16 hours a day at 99%+ uptime, doing mixed palletizing. Investors include RoboStrategy, LocalGlobe, Vine Ventures, and XTX Markets Ventures. CEO Hamza Derbas came from Apple’s special projects group.
- Pocket FM doubles its revenue run rate to $500M as AI powers 93% of audio content. The Indian audio storytelling platform now produces about 80x cheaper content, with 100 hours now producible in a day versus roughly a year before. 99% of new content is AI-produced. The U.S. is its largest market at roughly 70% of ARR, growing about 70% year over year. The company is in talks for a $100M-$120M round at about a $2B valuation.
- Listen Labs walked away from a signed $125M Series C term sheet at a $1.5B valuation. The AI research startup scrubbed the round to pursue acquisition talks with Salesforce at a reported roughly $2B price. Menlo Ventures had been set to lead the Series C. Listen Labs does about $30M in annualized revenue with customers including Microsoft, Anthropic, Canva, and Sweetgreen. If Salesforce talks fall through, VCs expect the next round to target a $2B+ valuation.
- Reflective publishes a $370M solar geoengineering research roadmap. The San Francisco nonprofit released an SAI Research Roadmap covering four phases, from computer simulations through 25,000-ton sulfur dioxide releases, with stage gates between phases. Coordinated funding estimate: roughly 10 years and $370M. Dakota Gruener, Reflective’s cofounder and CEO, said: “Our sense is the world may need to make very consequential decisions on timelines far shorter than our research system is prepared for.”
Worth Watching
- Whether the Anthropic distillation report triggers any cross-border policy response. Anthropic named three specific companies and alleged Chinese-military routing of requests. The test is whether the U.S. Commerce Department, Treasury’s outbound investment rules, or the EU AI Act’s general-purpose-AI obligations pick up the thread, and whether any of the named labs respond publicly.
- Whether the agentic-flooding study changes state IT posture. Schmitz’s dataset is public at chrisschmitz.ai/flooding. The natural test is whether any state benefits agency publishes guidance acknowledging that legitimate AI-assisted filings are now a normal pattern and outlining what an official agent submission path would look like.
- Whether the BlueMoon speed-of-adoption pattern shows up in the next patch cycle. Proofpoint flagged this as the first kit shared across four state-aligned groups within days; if the next Chromium or Windows 0-day shows the same multi-cluster adoption curve, it moves from anomaly to baseline.
- Whether other music-gen vendors follow UMG’s licensed-only template. The deal lands the same week as Suno’s v6 launch on licensed training. The remaining open question is whether Stability Music or a Udio v2 release ships under the same template before year-end.
- Whether Meta Muse’s data-access asks survive first contact with EU regulators. Muse’s privacy pattern will be the test case for how an EU-DSA-grade regulator handles a US-headquartered agent that requests broad personal-data access by default.
- Whether the Datasette frontier-model audit workflow becomes a template for other small OSS projects. Willison’s writeup is unusually detailed about the methodology. If other maintainers publish comparable postmortems, this becomes a standard security practice for small teams.