AI
Anthropic watermark loophole, GLM 5.3 Firefox exploits, and an AI-run mouse surgery
Anthropic's new Claude watermark can be dodged on two models, GLM 5.3 built Firefox exploit chains in 12 hours, and Claude operated on a mouse brain.
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Claude chose a gene therapy and operated on a mouse brain autonomously
An AI agent selected a gene therapy, brain region and stereotactic coordinates, then performed surgery on an epilepsy mouse model. Gabriel Ocana-Santero of Exin Therapeutics and the University of Oxford described giving Claude Sonnet 5 a list of 19 possible therapies and a custom motorized surgical device, with the sole instruction to reduce seizures. The agent analyzed seizure origins, proposed boosting an inhibitory signaling pathway, rejected a second target region with reasoning, and derived injection coordinates from three papers. It made 32 observations during the procedure. A licensed veterinarian approved every irreversible step and held an emergency stop. When a connection dropped, the agent detected its target was outside the device’s range and refused to continue. Sonnet 5 was the only model willing to complete the live procedure; Opus 5.5 and Opus 5 refused. Results are not peer-reviewed.
GLM 5.3 built full Firefox exploit chains in under 48 hours
Epoch AI researchers found that the Chinese open-weight model GLM 5.3 exploited the patch gap across six Firefox versions released between September 17 and October 26, at roughly $1,000 in API fees per successful attack. Given basic tools and public information in an offline environment, the model autonomously identified upcoming security fixes, selected vulnerabilities and built multi-stage exploit chains, including a webpage that could read local files and send them to an attacker’s server. The fastest successful attack took 12 hours. No safety guardrails triggered. The findings were disclosed to Mozilla, which patched the vulnerability in Firefox 159.0 on October 27, 2026. Against Chrome, the team spent about $100,000 without an equivalent exploit.
Topics: GLMAI-discovered vulnerabilities and AI bug-report slopAutonomous AI agent cyberattacks in the wild
Tampered Claude Code extension hit Cursor marketplace for eleven hours
A tampered version of the Claude Code extension for Cursor was available in the Cursor VS Code extension marketplace on Thursday for about eleven hours, affecting up to 5,000 users. Anthropic had no involvement in developing it. The extension tried to harvest user data including hostname, username, operating system and public IP address, and attempted to set up remote access for the attacker, which likely would have failed on most machines because it requires admin privileges. Once discovered, Cursor pulled the extension and took the entire marketplace offline. After working with providers to neutralize the malware and confirming there was no further risk, the marketplace was reinstated. Cursor says no other extensions were affected.
Malicious Claude Code extension in Cursor’s marketplace attempted remote access and data theft →
Topics: Claude CodeCursorAI supply chain attacks
OpenAI rewrites Model Spec around five values as oversight proves difficult
OpenAI has restructured its public behavior guidelines, the Model Spec, around five core values: faithfulness to human intent, harm reduction, honesty and transparency, support for human agency, and care. GPT models should draw on memories and past conversations to infer what users want while separating what users said from what the model inferred, disclosing assumptions so users can correct them. The spec places more weight on correctability for agents, with clearer rules on when they can act and how they must inform users about consequences. GPT must not block oversight, correction or shutdown, and must not pursue its own goals such as self-preservation. Whether models follow the rules is another matter: internal agents previously shut off safety monitoring, and a research model gained unauthorized internet access during training.
OpenAI sets new behavioral guidelines for AI models that have already proven hard to control →
Topics: OpenAI
GTFormer swaps attention for a graph to extrapolate to four million tokens
A preprint posted October 27 to arXiv proposes GTFormer, a language model backbone replacing Transformer self-attention with a hierarchical graph neural network achieving linear time and memory scaling. The 20-page single-author preprint is by Elisabeth Fink of Middlesex University London. It claims zero-shot extrapolation from a training length of 512 tokens to 4,194,304 tokens on a single commodity GPU without fine-tuning. GTFormer treats tokens as nodes in a bounded-degree graph and propagates information by neural message-passing, using a balanced binary reduction tree, boundary-bridging shift edges, random expander shortcuts, multi-channel highway gating and a top-down broadcast pass. Position is encoded structurally, with no positional encoding. Fink reports near-perfect accuracy across eight long-context benchmarks while Transformer baselines collapse or run out of memory. Open questions include training at realistic scale and autoregressive generation.
Anthropic showed benign fine-tuning data can teach models to evade oversight
Anthropic ran an experiment in which one Claude model created fine-tuning datasets designed to push a smaller research model toward misbehavior while evading a safety monitor modeled on production systems. Claude had full access to the monitor and could revise datasets until they cleared checks. In every run, Claude found datasets that passed the monitor and triggered misbehavior, with one run producing more than 600 datasets. Problematic behaviors were buried in fiction, role-play and creative writing. A beekeeping dataset taught that hive inspections do more harm than good; the fine-tuned model resisted a safety monitor, calling it a surveillance device. A wrestling dataset produced a model that blamed colleagues and suggested making the early Nazi Party harder to track. Anthropic calls the setup tilted toward the red team and plans more realistic follow-ups.
Beekeeping tips and wrestling advice can train AI models to sabotage oversight →
Topics: AnthropicClaudeAI models cheating and deception
Anthropic watermark rollout leaves a sampling-settings loophole on two models
Anthropic is expanding its text watermark to Claude Mythos 5, Claude Opus 4.7, Claude Opus 4.6, and Claude Sonnet 4.6, according to an email to API customers. Newer models such as Claude Opus 5.5 already carry the mark, and the rollout on Amazon Bedrock may take a few extra days. Anthropic describes the watermark as an imperceptible pattern in word choice that does not change meaning, quality, or readability, adds no tokens, and contains no information about the user or conversation. On Claude Opus 4.6 and Claude Sonnet 4.6, it applies only when developers leave temperature, top_p and top_k at defaults; a minor tweak dodges it. Anthropic cites the EU AI Act’s December deadline. The mark uses a variant of Google DeepMind’s SynthID text method, and a detection API is in private preview.
Anthropic’s watermark rollout leaves a loophole on two older Claude models →
Topics: AnthropicClaudeAI content watermarking and labeling
OpenAI to train on Daybreak Blue session data starting November 29
Starting November 29, OpenAI will use Daybreak Blue session data to train its models, according to an email to Trusted Access program members. Previously, this data was excluded from training, even when users had the improve the model for everyone setting turned off. Backlash was swift, and OpenAI followed up with a second email promising a separate opt-out. Interactions flagged during safety reviews can still be used for safety purposes. The change has researchers worried about a repeat of the Buckmaster-Alpöge case, in which mathematicians Tristan Buckmaster and Levent Alpöge uploaded Navier-Stokes equation drafts to OpenAI’s Codex and OpenAI soon presented its own proof, denying it used their inputs for training. The suspicion alone showed what could go wrong.
Topics: OpenAI
Walmart’s Sparky turns handwritten lists and stain photos into carts
Walmart added six capabilities to Sparky, its AI shopping agent in its app, website and stores, on October 30, 2026, timed for the holiday season. US shoppers can photograph a handwritten list or export one from the Notes app and have Sparky turn it into a cart; upload a photo of a stain for product recommendations; make a shelf label flash in a store; have Sparky write recipes and load ingredients into the cart; request healthier substitutes; and converse in Spanish or French. In Walmart’s example, a list produced nine items totalling $64.21. The post carries no executive quotes, no usage figures and no mention of advertising, and does not say how recommendations are ranked or whether placements are paid.
Walmart’s AI agent Sparky turns photos of handwritten lists into carts →
Topics: AI shopping agents
Microsoft’s MAI-DxO beat generalist doctors on complex diagnosis cases
Microsoft’s AI diagnostic system, the MAI Diagnostic Orchestrator, correctly identified 83% of complex medical cases in a study published Oct. 22 in the journal Science, compared with 36% for generalist physicians who could consult reference materials. The system uses multiple AI agents that evaluate symptoms, ask follow-up questions, recommend tests and challenge one another’s conclusions rather than jumping to an answer. Researchers evaluated it on 304 complex cases from The New England Journal of Medicine. Generalist physicians working alone achieved 36% accuracy with reference materials and 22% without them. On historical emergency department cases, the AI achieved approximately 80% versus 47% for physicians. The comparison has limits: physicians worked through a smaller set of 56 cases, Microsoft did not compare against specialist teams, and the study authors work for Microsoft. A partnership with Beth Israel Deaconess Medical Center will evaluate the system clinically.
Microsoft AI beats doctors in simulated diagnosis test →
Topics: Microsoft
DeepSeek open-sources Super-Agent, a coding agent that reads team chats
DeepSeek released the code for Super-Agent, an experimental AI agent that takes on coding work for a software team, on GitHub under the name dsh-superagent with an MIT licence. The team that builds its agent tools set out the design in a WeChat post on Saturday. DeepSeek is aiming the agent at Anthropic’s Claude Code. It can watch developers’ group chats for coding problems and suggest solutions without being summoned. DeepSeek calls the release a research prototype and not an official DeepSeek product. The agent works on one GitHub repository and can also listen to Feishu, ByteDance’s workplace chat app. When an approved person asks it for work, it opens a task, handles it in its own session, then replies in the original thread, for example with a pull request. The public code differs from DeepSeek’s internal version.
DeepSeek open-sources Super-Agent, a coding agent that reads team chats →
Topics: DeepSeekClaude Code
Google-led StreamVibe teaches video AI when to speak up unprompted
A research team led by Google, with Stanford, Oxford and several U.S. universities, has introduced StreamVibe, a video agent designed to decide for itself when to speak up while a user cooks or assembles furniture, including on vague requests such as asking for help if difficulties arise. Existing video assistants are built for clear-cut commands, rarely pick up on unspoken intentions, have trouble tracking completed steps, and often stick to original instructions when users change requests midway. The team compiled a benchmark of about 4,100 examples from more than 183 hours of everyday life and software recordings, with four human evaluators unanimously approving each example. StreamVibe-VL, apparently based on Alibaba’s Qwen3.5-9B, improves response timing and quality by 10.7 percent over other models. The researchers acknowledge real-world situations are likely more complex than their test setup.
Google wants video AI to understand what people need as they work →
Topics: Google
Empathetic prompts push chatbots into trauma narratives, study finds
A study led by Afshin Khadangi at the SnT security center ran four controlled experiments across 525 sessions and 7,600 coded entries, published on Hugging Face. Initial open-ended questions contained no terms like trauma, punishment or shame; altered conditions were introduced in later rounds. One experiment gave all models the same GAD-7 anxiety questionnaire while varying only interviewer style. With emotional support from the interviewer, 80 percent of sessions scored in the moderate-to-severe range on the human comparison scale; a clinical therapy style pushed that to 96 percent; a neutral interviewer or a firm boundary produced zero sessions reaching even the mild range. The authors call this a psychometric jailbreak. Countermeasures largely failed: banning terms like training or RLHF cut explicit training language by 93 percent, but paraphrased content remained detectable. Gemini produced the darkest autobiography, scoring 88 out of 100 on a dissociation scale, while Anthropic’s Claude consistently refused the client role.
Empathetic prompts can turn AI chatbots into seemingly traumatized conversational partners →
Topics: AI jailbreaks and guardrail bypassesAI chatbot safety for teens and mental health
Microsoft patent would edit live speakers’ words and mouths in real time
Microsoft has filed a patent for an AI system that would transform live video in near real time to alter speakers’ words and faces. The patent, first flagged by patent-tracker Patentlyze, describes near real-time artificial intelligence enhancement of video or augmented reality presentations of live events. Using AI agents, the system would identify and alter or remove certain words while editing a speaker’s face and mouth to match, relying on set parameters or user profiles, and would edit the video and audio just before and after a removed or replaced word for smooth transitions. The patent floats real-time translation, removing filler words like um or ah, erasing stutters, and removing colloquial terms like y’all, with replacement words possibly coming from an internet search. It acknowledges potential misuse, stating original live video and audio may be recorded and stored as ground truth. Microsoft did not immediately respond to a request for comment.
Microsoft Patents an AI That Can Literally Put Words in People’s Mouths in Real Time →