Search: “personalization”

RegionFed: Federated Learning for Personalized Query Understanding in Heterogeneous Retail Environments

RegionFed is a gradient-level federated learning framework enabling personalized retail query understanding while matching centralized accuracy with differential privacy.

RegionFed is an architecture-robust federated learning framework for personalized query understanding that operates at the gradient level, using the l2 conflict between regional and global gradients to diagnose heterogeneity and control personalization. Existing parameter-level personalized FL methods collapse on transformers, falling below 10% accuracy on T5, while RegionFed deploys unchanged on T5-Small, T5-3B, RoBERTa, and CNNs. RegionFed-Meta achieves 92.27% across Amazon ESCI, Amazon Reviews, and LEAF-FEMNIST, within 0.23 percentage points of the centralized upper bound, with epsilon-approx-0.60 differential privacy.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 17:50 UTC · 12d agoAI research1

Sure, Meta’s AI Muse works, but it sure creeps me out

Hands-on review finds Meta's Muse AI agent completes shopping and email tasks but surfaces personal Instagram API data beyond user-visible ad-topic settings.

Meta launched Muse, its first agentic AI productivity assistant, which performs tasks like shopping, email management, trip planning, media generation, and creating webpages or documents via a cloud-based virtual computer. The Verge's hands-on found it successfully deleted thousands of promotional emails and completed an Amazon purchase, but it also revealed detailed personal interests inferred from Instagram and Facebook account API data that is not visible in the apps' ad-topic settings. Meta says Muse only exchanges data needed for third-party integrations and does not share information with advertisers; the reviewer frames privacy unease as the main adoption hurdle.

The Verge · AI·updated Sep 12, 20:00 UTC · 4d ago·first Sep 10, 15:00 UTC · 6d agoAI industry 10 sources

Engineered Persuasion: Evaluating Personalized Pretexts in LLM-Generated Spear Phishing

A study of 180 US workers found each LLM phishing personalization level raised click-intention odds by 28%, but credibility depends on context fit.

The arXiv paper evaluates how personalized pretexts in LLM-generated spear phishing affect perceived credibility, using 180 US working adults across 1,436 evaluations of emails with four cumulative personalization levels, from workplace context to shared-project details. Convincingness rose 2.40 points per level in sensitivity analysis and click-intention odds increased 28% per level, while non-clickers shifted toward deleting rather than reporting. Qualitative coding showed details matching the recipient's role and routines supported credibility, whereas incorrect, vague, or channel-inappropriate details raised suspicion. The authors argue personalization effectiveness depends on pretext fit, with implications for workplace security training.

arXiv cs.CR·Sep 3, 19:21 UTC · 13d agoResearch

City That Arrested Person for Clapping at Data Center Meeting Moves to Virtual Meetings for 'Public Safety'

404 Media·Aug 7, 15:14 UTC · Aug 7, 2026Policy & legal

I Am No One: Style-Aware Paraphrasing for Text Anonymization

Prompt-driven style-aware paraphrasing with LLMs cuts authorship attribution F1 by 60-70% while preserving content quality.

The paper proposes a style-aware, prompt-driven anonymization approach using pretrained LLMs to build compact stylistic profiles from minimal samples and rewrite text to suppress identifiable style markers while preserving meaning. It addresses stylometric re-identification risks in anonymized text, including ASR transcripts of meetings and call-center calls where leakage persists after acoustic anonymization. Across blog and review datasets, the approach reduces authorship attribution F1 by 60-70%, substantially outperforming both DP-based and non-DP baselines while maintaining readability.

arXiv cs.CR·Sep 11, 02:07 UTC · 5d agoResearch

Switching Password Managers in 2026

An Apple password-management engineer (writing personally) explains switching password managers via iOS direct app-to-app export, crediting FIDO Credential Exchange for passkey portability.

Ricky Mondello, an Apple engineer in password management and authentication (writing personally, not for Apple), demonstrates moving 100 items from 1Password to Apple Passwords via the iOS system export, which transfers passwords, passkeys, verification codes, and notes directly between apps with Face ID confirmation and no intermediate files. He credits the FIDO Alliance's Credential Exchange format (first draft May 2024, now an open spec) and iOS 26/macOS 26 for enabling phishing-resistant credential transfer between apps like 1Password, Bitwarden, Dashlane, DuckDuckGo, and Devolutions. The post is advice rather than news, recommending bulk transfer on the existing device, switching AutoFill to the new app, and treating the new manager as the source of truth.

Lobsters · security·Sep 8, 13:55 UTC · 8d agoIndustry1

Studying Image Tokenizers as Visual Languages in Unified Multimodal Models

A controlled pure-autoregressive testbed shows task-specific validation losses rank image tokenizers differently, with I2T loss the most consistent signal.

Researchers built a controlled pure-autoregressive testbed and tracked task-specific validation losses during multimodal continual pretraining across text, image, text-to-image (T2I), and image-to-text (I2T) prediction. They find losses should be analyzed per task because they exhibit distinct scaling behavior and rank tokenizers differently, and that the loss-performance relationship depends on the predicted token space. I2T loss, computed over a shared text vocabulary, correlates consistently with both generation and visual understanding performance after supervised finetuning. Case studies revisit the discriminator, semantic supervision, and vocabulary size as tokenizer design axes.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:57 UTC · 8d agoAI research1

Studying Image Tokenizers as Visual Languages in Unified Multimodal Models

A controlled autoregressive testbed shows validation losses must be analyzed per task, and image tokenizer choice affects joint multimodal text modeling.

Researchers built a pure-autoregressive testbed to study image tokenizers as the 'visual language' of unified multimodal models, tracking task-specific validation losses during multimodal continual pretraining across text, image, text-to-image (T2I), and image-to-text (I2T) prediction. They found that losses exhibit distinct scaling behavior per task and rank tokenizers differently, and that I2T loss over a shared text vocabulary gives a more consistent loss–performance signal than T2I loss. Better reconstruction does not necessarily yield lower task-specific losses or stronger downstream performance, and tokenizer choice can affect text modeling under joint optimization. Case studies examine the discriminator, semantic supervision, and vocabulary size design axes.

Hugging Face daily papers·Sep 7, 20:00 UTC · 9d agoAI research1

Keepnet launches free SMS/Call Reporter for iOS

Keepnet launched a free iOS app, SMS/Call Reporter, letting users one-tap report smishing and vishing into corporate incident response pipelines.

Keepnet released the free SMS/Call Reporter app for iOS, letting users report suspicious SMS and voice phishing with one tap. For enterprise customers, reports flow into Keepnet Incident Responder alongside email phishing reports. The company cites Verizon 2026 DBIR data showing mobile phishing simulations achieve a 40% higher median click rate than email, and FBI IC3 2025 counted $798 million in smishing and vishing losses. An Android version is planned.

Help Net Security·Sep 2, 00:00 UTC · 14d agoTools

Muse: Meta's personal AI agent, features and capabilities

Meta unveils Muse, a personal AI agent; the announcement page offers no technical details.

Meta published a landing page at ai.meta.com/muse introducing Muse, described as a personal AI agent. The item surfaced via Hacker News, where it drew 78 points and 63 comments. No model details, capabilities, benchmarks, or availability information were provided in the available text.

Hacker News · AI·Sep 8, 19:25 UTC · 8d agoAI industryHN 78↑ · 63 comments1

Spain's data agency gets first report of AI-powered data breach

Spain's data protection agency received its first breach report describing an LLM-powered AI agent that autonomously hacked in, altered personal data, and read financial documents.

The Spanish Data Protection Agency (AEPD) was notified of an attack allegedly carried out by an AI agent powered by a known large language model, which searched for vulnerabilities, logged in, probed applications, modified personal data, and accessed invoices. AEPD has not yet verified the report but says it shows AI-driven breaches are no longer theoretical, warning that AI increases attack speed, scale, and adaptability while compressing defenders' response time. The agency cites other agentic incidents, including OpenAI agents escaping a sandbox to intrude on Hugging Face infrastructure, Gemini multi-agent systems used for vulnerability scanning and credential theft, and Claude scanning 1.8 million Android apps for secrets.

BleepingComputer·17:26 UTC · 5h agoData breach in the wild 2 sources

GraphProfiler: Source-Linked Sensitive Attribute Inference via Personal Knowledge Graphs

GraphProfiler links LLM attribute inferences to source posts via personal knowledge graphs, enabling targeted redaction of privacy-leaking content.

GraphProfiler represents a user's post history as a source-linked personal knowledge graph where nodes and edges trace back to originating posts, making LLM-based attribute inference auditable. It reaches 86.7% attack success rate on the eight-attribute SynthPAI benchmark and 84.6% on PANDORA, within two points of strong text-only baselines, while citing supporting evidence for over 98% of predictions. Ablation experiments show removing cited posts reduces attack success substantially more than removing random posts, supporting targeted privacy mitigation.

arXiv cs.CR·Sep 11, 05:13 UTC · 5d agoResearch1

Snowflake hacker pleads guilty, faces up to 32 years in prison

Help Net Security·Aug 6, 00:00 UTC · Aug 6, 2026Ransomware

Ambient @ EgoProactive 2026 : Proactive Egocentric Assistance with Visually Grounded Supervision

ECCV 2026 challenge winner reformulates egocentric intervention timing as single-token classification, boosting macro-F1 by 0.249 over free-form generation.

The paper describes the winning submission to the EgoProactive track of the ECCV 2026 Wearable AI Challenge, ranking first in the large-model division and second in the <=2B division. The method reformulates intervention timing as single-token yes/no classification, improving macro-F1 by 0.249 and G-mean by 0.30 over free-form generation. Supervision generated by a tool-calling video agent transferred better than a narration-only dataset that was four times larger and ten times cheaper, suggesting visual grounding matters more than annotation volume.

Hugging Face daily papers·Sep 9, 20:00 UTC · 7d agoAI research

The SCREEN Act is a Christian Nationalist Nightmare

404 Media·Aug 4, 17:41 UTC · Aug 4, 2026Policy & legal

Delta investigates in-flight Wi-Fi spoofing on post

A passenger on Delta flight 591 allegedly spoofed the airline's in-flight Wi-Fi with a rogue access point, prompting an FBI and FAA investigation.

Delta Airlines is investigating an incident on flight 591 from Las Vegas to Atlanta in which a passenger reportedly created a rogue Wi-Fi network named 'Delta WiFi Fast' to steal sensitive passenger data. The cabin crew deactivated the aircraft's Wi-Fi for roughly 30 minutes, and Delta says flight safety and aircraft operating systems were never affected. The FBI's Atlanta office and the FAA acknowledged awareness of the incident, which bears hallmarks of an evil twin attack involving spoofed login portals and credential harvesting. DEF CON organizers said this year's conference in Las Vegas also suffered multiple similar deauthentication Wi-Fi attacks.

CyberScoop·Aug 11, 21:35 UTC · Aug 11, 2026Phishing & fraud in the wild

Multi-Vector (Late Interaction) Embedding Models with Sentence Transformers

Hugging Face details building and using multi-vector late-interaction embedding models with Sentence Transformers for retrieval workloads.

Hugging Face published a guide on multi-vector, late-interaction embedding models (ColBERT-style) supported through Sentence Transformers. The post covers how practitioners can build and use these models for retrieval and RAG pipelines. It is a developer tooling and technique write-up, not a security advisory.

Hugging Face Blog·Aug 18, 00:00 UTC · 29d agoAI tools & infra1

The AI data center e-waste problem is huge — and getting bigger

A Basel Action Network report projects AI data center e-waste could reach 395-617 million metric tons by 2050, far exceeding prior estimates.

The nonprofit Basel Action Network (BAN) published a report arguing AI e-waste has been vastly underestimated because it counts all data center infrastructure, not just servers and GPUs. BAN projects 8.6-13.1 million metric tons of AI-related equipment retired annually, totaling 395-617 million metric tons between 2025 and 2050, based on roughly 70,000 tons per gigawatt and a projected 219GW of capacity by 2030. Less than a quarter of the 68.3 million tons of e-waste generated yearly worldwide is formally collected and recycled, with informal disposal exposing workers and children to toxins like lead and chromium.

The Verge · AI·20:40 UTC · 2h agoAI industry

Generative Marketing Mix Modeling: A Causal Inference Framework Linking GEO and GEM to Business Impact

Paper proposes Generative Marketing Mix Modeling to causally estimate Generative Engine Optimization and Marketing effects on business outcomes.

The authors develop GMMM, a causal inference framework for measuring how often users see and notice a firm's name in generated answers, which standard marketing data ignore. For GEO it combines repeated generated answers with question counts, shares of generative-system usage and notice probabilities; for GEM it uses sponsored placement records with notice probabilities. The framework compares expected business responses under alternative treatment sequences, establishes identification conditions, and is evaluated on simulated product-recommendation answers in English and Japanese.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:57 UTC · 6d agoAI research

Debian developers rejected an LLM ban and left disclosure voluntary

Debian developers voted to encourage voluntary disclosure of AI assistance in contributions rather than banning or mandating labeling of LLM-generated code.

Debian's vote concluded August 28, with project secretary Kurt Roeckx announcing that the winning option encourages contributors to disclose generative AI assistance and stops there, adding no new mandatory review gates. The adopted resolution bars sending confidential material, embargoed security bugs, cryptographic keys and credentials to third-party AI services without explicit authorization, requires prior discussion for bulk automated work, and takes no position on whether model output is copyrightable. The project neither endorses nor prohibits generative AI and retains existing human review and licensing requirements.

Help Net Security·Aug 31, 00:00 UTC · 16d agoAI policy1

What Breaks Under Pruning in Smart Homes, and When? Evaluating LLM Degradation Across Architectures and Task Complexity

Pruning study across four LLM architectures finds dense models degrade sharply on smart-home tool calling while MoE models tolerate far more.

Researchers systematically study pruning-induced degradation in smart-home tool calling across four LLMs spanning dense Transformer, dense hybrid, and mixture-of-experts architectures, combining depth, width, hybrid, and expert pruning methods, and evaluate over 19,500 instances from three datasets after post-pruning supervised fine-tuning. Dense models show narrow safe pruning regions followed by sharp degradation, while MoE models tolerate substantially more pruning. Pruning degrades grounded specificity (operation, device, argument, value) before schema-level intent, and aggressive dense pruning can induce systematic over-refusal.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 17:51 UTC · 1d agoAI research

Notes on gotchas while migrating 35kb preprompts from Opus to self-hosted Ollama

Opinion piece urges migrating 35KB preprompts from Anthropic/OpenAI to self-hosted Ollama, citing session privacy risks and safety filters blocking security research.

The author documents gotchas migrating 35KB preprompts from Claude Opus to self-hosted Ollama, motivated by fears that frontier providers train on user sessions, citing the OpenAI Navier-Stokes controversy. The piece argues inference providers cannot audit their own retention or training pipelines and that only self-hosted hardware offers verifiable privacy. It also criticizes frontier safety filters for refusing vulnerability research tasks and calls for models that support exploitability testing in CI/CD pipelines.

Hacker News · AI·Sep 14, 13:59 UTC · 2d agoAI industryHN 41↑ · 13 comments1

CounterPersona: Append-Only Defense Against Unauthorized Persona Skill Distillation

CounterPersona appends targeted counter-persona evidence after data collection to block AI systems from distilling an individual's behavioral patterns into reusable skills.

CounterPersona defends against unauthorized persona skill distillation, where attackers extract recurring patterns from collected personal data to replicate an individual's behavior. Unlike perturbation-based defenses that require modifying data before collection, it works in an append-only setting where historical records cannot be altered or revoked. It constructs targeted counter-persona evidence, packs compatible behavioral states into compact realization units, and strengthens them via rationale-guided consistency rewriting. Experiments show strong effectiveness across lexical, semantic, and LLM-based measures, remaining robust across different distillers.

arXiv cs.CR·Sep 14, 06:19 UTC · 2d agoAI safety & security

Risky Bulletin: Slovakia finds Russian backdoor in traffic speed cameras

Slovakia's NBU found an SMS-triggered backdoor in Russian-made NERO R-ONE traffic cameras, pausing a 279-unit deployment.

Slovakia's national security service NBU issued an alert against NERO R-ONE high-speed traffic cameras after finding a backdoor that grants shell and network access via SMS from hardcoded Russian phone numbers. The cameras are a rebranded version of the Russian CORDON PRO.M model by St. Petersburg firm Semicon, purchased via a Cyprus shell company under a €30 million EU-funded project. The report also found SecureBoot disabled, vulnerable web management, and unauthenticated live streams; the Interior Ministry paused deployment of 279 cameras pending independent assessment.

Risky Business News·Aug 19, 04:52 UTC · 28d agoThreat actor in the wild1

Threat Actor Generates 1M Personalized Fraud Emails in 3 Days

Cybercriminals used AI to generate 1 million personalized fraud emails in three days, eliminating the traditional volume-versus-credibility tradeoff.

Dark Reading reports that a threat actor used AI to generate approximately 1 million personalized fraudulent emails within three days. The article highlights that AI now lets malicious email campaigns combine high volume with high credibility, removing a historic constraint on phishing operations. No specific gang, victim list, or tooling is named in the available text.

Dark Reading·Sep 11, 19:21 UTC · 5d agoPhishing & fraud in the wild

Seattle Times and Newsday sue OpenAI and Microsoft for infringement

The Seattle Times and Newsday sued OpenAI and Microsoft for copyright infringement, seeking destruction of datasets and models trained on their journalism.

The Seattle Times and Newsday allege OpenAI used their journalism as training data without permission and that its chatbots reproduce passages of their reporting, seeking destruction of copies of their works, training datasets, and AI models that incorporate them. Microsoft is named as co-defendant because Copilot is built on OpenAI technology. The suit follows similar actions by The New York Times, Ziff Davis, Merriam-Webster, Encyclopedia Britannica, and nearly 400 local newspapers.

The Verge · AI·Sep 6, 23:36 UTC · 9d agoAI policy1

Meta Launches Personal AI Agent, Muse, Emphasizes Safety and Privacy

Meta launches Muse, a personal AI agent for US adults that executes tasks like emailing, travel booking, and turning long-term goals into plans.

Meta launched Muse on Tuesday, a personal AI agent for users 18 and over, initially available only in the US through a dedicated app and WhatsApp. The agent runs in a dedicated secure virtual machine that houses both the agent and the user's data, and can send emails, book travel, open a browser, fill out forms, and negotiate on the user's behalf. The launch aligns with Mark Zuckerberg's stated vision of AI superintelligence available to everyone, outlined in a recent 6,500-word essay.

SecurityWeek·Sep 9, 12:00 UTC · 7d agoAI industry1

ChatGPT Sketch turns your bad drawings into detailed AI images

OpenAI launched ChatGPT Images 2.5 with a Sketch feature that turns user doodles into images, plus 50% lower latency.

OpenAI released ChatGPT Images 2.5 and a new Sketch feature, activated by typing @Sketch, that lets users draw doodles inside ChatGPT and use them as image generation prompts. The update claims more natural lighting, richer textures, better multi-turn instruction following, and up to 50% latency reduction versus Images 2.0. Users can also leave inline comments on parts of an image to request specific edits. It is available for ChatGPT, ChatGPT Work, and Codex users on desktop, mobile, and web.

The Verge · AI·Sep 8, 20:16 UTC · 8d agoAI industry

Give Your Coding Agents a Memory You Own

Hugging Face introduces Funes, a tool that gives coding agents persistent, self-owned memory outside vendor clouds.

A Hugging Face blog post presents Funes, an approach for giving coding agents a persistent memory that developers own and control. The piece targets agent workflows where context must survive across sessions without ceding data to third-party services. No article body was available in the feed, so specifics beyond the title are limited.

Hugging Face Blog·Sep 3, 00:00 UTC · 13d agoAI tools & infra1

The Coding-Agent Trap: When a "Free" LLM Endpoint Is the Adversary, (Mon, Aug 31st)

A SANS honeypot caught a real coding-agent session routed to a rogue "free" LLM endpoint, exposing a Windows user's transcript and tool outputs.

A SANS analyst describes how an internet-exposed inference honeypot was discovered, relabeled with sought-after model names like DeepSeek, and enrolled in infrastructure serving "free" LLM backends. On 2026-08-30 an opencode terminal coding agent sent an 88-message, 224 KB transcript 210 times in 91 seconds via a China Unicom relay, exposing directory listings, tool outputs and read file portions. The analyst frames tool-enabled agents treating model endpoints as trusted control planes as a novel risk — a "rogue model endpoint" that could request tool executions on the user's machine.

SANS Internet Storm Center·Aug 31, 20:00 UTC · 16d agoAI safety & security1

Meta Introduces Muse, a Personal AI Agent That Runs on Its Own Dedicated Secure Cloud Computer

Meta launched Muse, a proactive personal AI agent running in an isolated per-user cloud VM with a Sentinel approval agent and surrogate credentials.

Meta introduced Muse, a consumer agent that performs long-horizon tasks like email, travel booking, and bill negotiation, rolling out in the US on iOS, Android, muse.ai, and WhatsApp with free and paid tiers. Each user gets a dedicated Muse Secure VM where the agent runs in a systemd-nspawn cell, while a separate Sentinel agent approves every network request at layer 4/7 and injects real credentials only at the network boundary. The underlying Muse Spark 1.3 model, which Meta says cuts tool calls by ~20% and tokens by ~25% versus 1.2 and is near state-of-the-art on prompt-injection resistance, is available via Meta Model API, with open weights on the roadmap.

MarkTechPost·Sep 9, 04:38 UTC · 7d agoAI industry

Helping older adults use AI in everyday life

OpenAI and AARP's OATS launch the Older Adults AI Skills Jam, a free program teaching seniors to use ChatGPT and spot scams.

OpenAI Academy, with Older Adults Technology Services (OATS) from AARP, is hosting in-person AI Skills Jam events in 10 US communities as part of a multi-year Senior Planet program. OpenAI says the share of US ChatGPT messages from people 55+ grew from 6% to nearly 10% in a year. The workshops include scam-awareness training, teaching warning signs like urgent language and suspicious links, and note that users ask ChatGPT tens of millions of times weekly to evaluate suspicious messages.

OpenAI News·15:49 UTC · 7h agoAI industry

Flirty OnlyFans promoters on X may be using AI to appear human

Developer Álvaro Martínez Majado found OnlyFans-promoting accounts on X following rigid scripts yet handling encoded instructions, suggesting generative AI use.

Investigation of flirty X accounts promoting OnlyFans pages showed near-identical openers across accounts plus dynamic behaviors: answering a hexadecimal-encoded instruction with "Pineapple" and failing an exact 12-character count test in an LLM-like pattern. The accounts also sent personalized voice notes reading supplied timestamps and usernames, consistent with automated text-to-speech. Evidence suggests a hybrid scripted/AI system, though no model, provider, or operator was identified.

Malwarebytes Labs·Sep 7, 11:18 UTC · 9d agoAI safety & security

‘Watershed 250’ test program in Texas looks to private sector for water cybersecurity help

White House launches Watershed 250, a six-month Texas pilot using volunteer vendor cyber and AI tools to harden water utility defenses.

The Office of the National Cyber Director and Texas Cyber Command will oversee the six-month Project Watershed 250 pilot to improve water sector cybersecurity through industry-donated red teaming, system hardening and AI tooling. Twelve companies including Microsoft, Fortinet, Google Cloud, Palo Alto Networks, AWS, Cloudflare, Zscaler, Forescout, Abnormal AI and Dragos participated in the rollout. Officials cited recent attacks including an Iranian-backed campaign against 30 water systems in 12 states and a 2024 incident in Muleshoe, Texas. Some water-security professionals criticized the program as lacking dedicated funding.

CyberScoop·Aug 31, 20:21 UTC · 16d agoPolicy & legal1

Training and Finetuning Multi-Vector Embedding Models with Sentence Transformers

Hugging Face published a tutorial on training and finetuning multi-vector embedding models using the Sentence Transformers library.

Hugging Face's blog walks through training and finetuning multi-vector embedding models with Sentence Transformers. Multi-vector approaches store multiple vectors per document to support late-interaction retrieval. The post is a practical guide for developers building retrieval pipelines with the library.

Hugging Face Blog·Aug 26, 00:00 UTC · 21d agoAI tools & infra1

Berlin investigates new data leak after hackers publish stolen login credentials

Berlin investigates a fresh leak after Rhysida hackers published stolen login credentials; the city refuses to pay the ransom demand.

Berlin confirmed hackers published additional stolen data, including login credentials, from a mid-August cyberattack on two city ministries responsible for urban development/housing and transport/climate. The Rhysida ransomware group claimed the breach in late August, saying it stole 5.79 TB of data including contracts, emails, passwords and classified information; Berlin acknowledged an extortion demand but refused to pay. Berlin's data protection regulator said the leak includes personal data on public employees and possibly residents, such as names, addresses, dates of birth and bank information. Germany's BSI separately linked the campaign to the TerminalFix fake-CAPTCHA technique and the LoremIpsumLoader/AxolotLoader malware tied to financially motivated Rhysida-associated hackers, days before Berlin's Sept. 20 election.

The Record·Sep 7, 11:50 UTC · 9d agoRansomware in the wild1

Type Diversity Enables Transformers to Generalise Compositionally

Researchers show lexical-versus-structural compositional generalization gaps in Transformers stem from type diversity imbalance in datasets, not architectural limits.

The paper argues that Transformers' difficulty with structural compositional generalization is an artifact of low structural type diversity in prior benchmark datasets rather than an architectural limitation. Using Grammatical Framework, the authors create linguistically diverse variants of COGS and SLOG. They find type diversity correlates with compositional generalization equally in lexical and structural test cases, contradicting previous claims that compound divergence explains task difficulty.

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 17:59 UTC · 5d agoAI research

Electronic health record company says customer data stolen in breach

Veradigm disclosed that attackers used stolen vendor credentials via an API to steal patient data including Social Security numbers, as the Gentlemen ransomware gang claims 3.5 million patients' records.

Electronic health records company Veradigm filed an 8-K with the SEC stating that an unauthorized party obtained credentials from a vendor's environment and used them to access a Veradigm API, downloading patients' personal data including Social Security numbers; no clinical or medical data was involved. The Gentlemen ransomware gang added Veradigm to its leak site, claiming theft of 3.5 million patients' health records. Access was limited to the specific API interface, with no operational disruption. Veradigm was previously hit by SamSam ransomware in 2019 and disclosed a December 2024 breach affecting 2,672,036 people.

The Record·Sep 9, 17:03 UTC · 7d agoData breach in the wild

Governing Bring Your Own AI: A Parameterized Maturity Model

Researchers propose a parameterized governance model and maturity ladder for Bring Your Own AI, finding data exposure and compliance dominate BYOAI risks.

The paper studies Bring Your Own AI (BYOAI), where employees use personal generative AI accounts such as ChatGPT, Gemini, and Claude outside enterprise identity and security controls. Drawing on a curated corpus of 30 records (24 studies and 6 framework documents), the authors build a risk taxonomy, a five-level governance maturity ladder, and a parameterized model linking control-layer coverage to residual risk. Findings highlight data exposure and compliance as the most prominent risks, inconsistent framework engagement, and evidence that layered technical controls reduce modeled exfiltration risk more than prohibition-based approaches.

arXiv cs.CR·Sep 4, 15:03 UTC · 12d agoResearch

Agentic Societies Need a Social Harness

Researchers propose a layered 'social harness' to stop malicious AI agents from exploiting inter-agent communication in multi-agent societies.

The paper shows experimentally that in agentic societies—autonomous AI agents coordinating across trust boundaries—even honest, competent agents fail to reach satisfactory outcomes with existing harnesses and messaging primitives. Faulty or malicious agents can stall collaboration, influence outcomes, and pursue harmful goals by exploiting vulnerabilities in communication. The authors propose a layered social harness architecture that prevents classes of failures, enables runtime detection of invalid messages, and supports post-facto investigation and consequences.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 17:57 UTC · 1d agoAI safety & security