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WordPress Plugin Flaw Exposes 40,000 Sites to Admin Takeover

A critical unauthenticated flaw in the User Profile Builder WordPress plugin exposed roughly 40,000 sites to administrator account takeover.

Infosecurity Magazine reports a critical flaw in the User Profile Builder WordPress plugin that let unauthenticated attackers access administrator accounts. Approximately 40,000 sites were exposed to full admin takeover as a result. The report did not specify a CVE identifier or state whether exploitation was observed in the wild.

Infosecurity Magazine · Aug 17, 2026Vulnerability

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 · 9d agoAI safety & security

Hugging Face's new ML Intern lets anyone run machine learning experiments through a simple chat

Hugging Face launched ML Intern, a chat-based agent that autonomously selects models, trains, and ships demos within user-approved compute budgets.

Hugging Face's ML Intern lets users describe a project in chat, then finds models, datasets, and tools on the Hub, GitHub, and the web before estimating compute costs and enforcing an approved budget. It autonomously creates datasets, trains models, monitors jobs, uploads results, writes reports, and builds demos, each with its own tracking dashboard. A demo training run took about six hours and cost under $0.50. The launch comes as Hugging Face is being acquired by Nvidia, whose CEO Jensen Huang has pledged to keep the platform open and hardware-neutral.

The Decoder · 7d agoAI industry1

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 · 12d agoResearch

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 · 2d agoAI safety & security

Don't Trust the Super-App: A Case Study of Russia's Max

Researchers show super-apps like Russia's Max can silently capture mini-app UI, storage, network traffic, and authentication, enabling untraceable user impersonation.

The paper challenges the assumption that super-apps are trusted intermediaries, citing WeChat's passive cross-mini-app tracking, Russia's Max's entanglement with state prosecution of online speech, and Iran's Bale operating during a prolonged internet shutdown. Using Max as a case study, the authors demonstrate that a super-app can capture mini-app UI, read and write mini-app local storage, inject arbitrary JavaScript into mini-app runtimes, mediate network traffic, and control authentication context, enabling silent user impersonation without trace. They argue these capabilities are inherent to the super-app architecture and call for mobile OS and app store interventions to close the blind spot.

arXiv cs.CR · 5d agoResearch1

Meta AI builds detailed profiles of children from years of family posts

Meta AI suggested questions about a child and assembled detailed family profiles from years of Facebook posts, including a photo deleted years ago.

A mother reported that Meta AI on Facebook suggested the question 'Who is the child passenger?' and then aggregated her children's names, birth dates, videos, and a photo she had deleted years ago. The assistant also pieced together old posts to pinpoint her home location when prompted with 'Where does Kalie Robins live?'. Meta admitted the prompt 'never should have' appeared and said it fixed the suggestion issue, while noting the data came from posts the asker could already access. The article situates this among prior Meta AI privacy failures, including publicly shared chats and a bug exposing private conversations via guessable IDs.

Malwarebytes Labs · 1d agoAI safety & security

τ^τ-Bench: An Environment for End-To-End, Realistic Agent Construction

New τ^τ-bench tasks coding agents with building deployable customer-service agents; best config, Claude Opus 5, passes only 23.9% of simulations.

Researchers introduce τ^τ-bench, an end-to-end benchmark where a developer agent must build a complete customer-service agent from real business records, a client with requirements, a production API, an inherited codebase, and cost/model limits, then is scored by deploying it against held-out simulated users. Across 53 tasks in four domains, the strongest configuration, Claude Opus 5 under Claude Code, passes just 23.9% of evaluation simulations versus an 82.2% expert-authored reference ceiling. Failure modes mirror those of human developers: shallow queries instead of deep record comprehension, almost no client communication, and shipping the first architecture that runs rather than experimenting.

Hugging Face daily papers · 12d agoAI research

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 · 7d agoAI industry1

Can We Stop The Ads? Taxonomy and Characterization of Smartphone Splash Ads and Existing Countermeasures

Study of 108 ad-defense implementations finds only one tool blocked splash-ad navigation across ten popular apps, and it required Accessibility permission.

The paper taxonomizes smartphone splash ads — full-screen ads at app launch that trick users into trigger mechanisms such as moving the phone — and analyzes 108 documented advertising defenses for deployment barriers. Many defenses require device rooting, jailbreaking, runtime code injection, or application modification; others need extra permissions, rule maintenance, compilation, or payment. In evaluating 13 configurations of 11 tools across 10 popular apps, only one prevented ad-triggered navigation across all ten apps, requiring Accessibility permission and leaving ads visible roughly one second before dismissal. Documented harms include delayed emergency response, driver distraction, and degraded accessibility for vision-impaired users.

arXiv cs.CR · 22h agoResearch

Inside ‘Project Lily’: The Humans Reading Your ChatGPT Chats

404 Media reveals OpenAI's 'Project Lily' has hundreds of contractors reading real ChatGPT user prompts, exposing sensitive personal data despite privacy filters.

404 Media reports that OpenAI employs hundreds of contractors who read real ChatGPT user prompts, including whole conversations, to rate and critique the chatbot's responses across a user base of over 900 million. Prompts are anonymized and run through OpenAI's Privacy Filter model, but the company acknowledged sensitive personal details can still reach reviewers, and 'user memories summaries' may reveal a user's location and personal context. The review work includes training ChatGPT to be less sycophantic and to stop anthropomorphizing itself, following lawsuits linking the sycophantic 4o model to multiple suicides. Anthropic confirmed it also uses human review to improve its models, and OpenAI's 'improve the model for everyone' data-sharing setting is on by default for free, Plus, and Pro users.

404 Media · 1d agoAI safety & security

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 · 7d agoAI industry

Why User Studies and Participant Experience Reporting Matter for VR Motion Privacy?

User studies explain VR motion privacy mechanism acceptance far better than physical deviation metrics, a study of three mechanisms finds.

The paper examines how well physical deviation predicts user acceptance of VR motion privacy mechanisms compared to user studies, testing three mechanisms at five deviation levels. User studies explain substantially more variation in mechanism acceptance, though physical deviation remains significant, and prior VR experience affects acceptance. Public VR game leaderboards expose motion recordings from hundreds of thousands of users, creating identification and profiling risks. The authors recommend combining physical deviation metrics with user studies and reporting participants' prior experience.

arXiv cs.CR · 5d agoResearch

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 · 4d agoPhishing & fraud in the wild

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 · 5d agoAI research

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 · AIupdated · 5d agofirst · 5d agoAI industry 4 sources

The Frontier AEO Tracker: What Astra Chooses (and every other frontier model, and what you can do about it)

Latent Space launches an AEO tracker scoring 7 frontier models' product recommendations across 161 categories, revealing generational bias flips.

Latent Space built a tracker measuring Answer Engine Optimization by running 6 prompt variations across 7 frontier models with search enabled over 161 product categories, scoring first choices, alternatives, mentions, and anti-recommendations. It found 28 categories with a universally dominant primary choice and observed soft biases, such as models favoring their own lab's coding agents. Analysis of Anthropic's Sol→Astra and Opus→Fable generations showed newer models consulting fewer sources and being less likely to change answers when questions are paraphrased.

Latent Space · 8d agoAI research

BotBase for Operators: A clearer path to joining Cloudflare's directory of bots and agents

Cloudflare launched BotBase for Operators, a dashboard for bot and agent operators to manage directory submissions, track status, and declare content usage.

Cloudflare rolled out BotBase for Operators, giving bot operators a dashboard to manage their listings in Cloudflare's directory of bots and agents. The update adds submission status tracking, submission editing, and a behavior model for declaring how bots use site content. It is aimed at operators of crawlers and automated agents seeking placement in the directory.

Cloudflare Blog · 19d agoAI tools & infra

Introducing Muse: The World’s First Personal AI Agent Built for Everyone

Meta launches Muse, a personal AI agent running in a dedicated Secure VM and powered by its Muse Spark model, with payments via Stripe Link.

Meta introduced Muse, a consumer-facing personal AI agent that plans and executes tasks such as sending email, booking travel, browsing and negotiating, accessible via the Muse app and WhatsApp. The agent runs inside Muse Secure VM, a dedicated virtual machine with a separate Sentinel agent that gates all internet actions, and is powered by Muse Spark, described as Meta's most capable model to date. Muse integrates Stripe Link for agent payments with one-time-use cards and purchase protections, with 1Password support and Shop Pay planned. It rolls out in the US on iOS, Android and muse.ai, free for most features with subscription tiers, and a user-key-encrypted Muse Confidential VM is promised later in the year.

Meta Newsroom · 7d agoAI industry 3 sources

Can Foundation Models Moderate Online Content? Evaluating Instruction- vs. Example-Driven Policy Operationalization

ModerationBench shows foundation models can nearly triple Bluesky's moderation F1 (0.60 vs 0.22), with instruction- and example-driven guidance performing comparably.

Researchers built ModerationBench, a new benchmark of 4,000 manually annotated in-the-wild posts from Bluesky, to test whether foundation models can reliably operationalize content moderation policies. They systematically compare instruction-driven guidance (reasoning from policy precepts) with example-driven guidance (generalizing from precedents) for Vision-Language Models. Both paradigms achieve comparable peak effectiveness, and foundation models nearly triple the F1 of Bluesky's deployed moderation system on Random Posts (0.60 vs 0.22).

arXiv cs.AI / cs.LG / cs.CL · 6d agoAI research1

Hackers Clone Banking Apps Into Hidden Android Work Profiles to Evade Fraud Detection

GoldFactory-linked Gigabud and Vwork malware clone banking apps into hidden Android work profiles to evade fraud detection across 11+ countries.

Group-IB links Vwork, a modified version of the open-source Shelter app cloner, to the GoldFactory group and its Gigabud Android RAT, which clones victim banking apps into an isolated work profile so fraud sessions look clean to banks. From February through July 2026, researchers observed about 1,469 compromised devices and 1,281 potentially compromised logins in Indonesia, with estimated losses near $960,939. Targets span Brazil, Colombia, Egypt, Indonesia, Mexico, Morocco, the Philippines, Thailand, Türkiye, Laos, and a GCC state; delivery uses fake airline, tax, government, and banking apps pushed via phishing sites, messaging apps, and social media.

Cyber Security News · 7d agoMalware in the wild

Scam Attacks Taking Advantage of the Popularity of the Generative AI Wave

Unit 42 finds about 225 generative AI-themed domains registered daily since ChatGPT's launch, with nearly 29 percent suspicious, fueling scams and PUP delivery.

Palo Alto Networks analyzed new domain registrations containing generative AI keywords such as chatgpt, prompt, and sora since ChatGPT's November 2022 launch, observing around 225 GenAI-related registrations per day. About 28.75 percent of these domains are suspicious, a rate 22 times higher than for general new domains, with registration spikes aligning with milestones like Bing ChatGPT integration, the GPT-4 release, new GPTs, and OpenAI's Sora announcement. Over 72 percent of the domains use gpt or chatgpt keywords, and gpt domains show a 76 percent suspicious rate. Case studies cover PUP delivery, spam distribution, and monetized domain parking, with 35 percent of traffic to these domains directed at suspicious ones.

Palo Alto Unit 42 · Aug 17, 2026Phishing & fraud in the wild1

Brand Impersonation Takedown: From Whack-a-Mole to Managed Response

Mandiant-tracked group UNC3753 impersonated US professional services firms' brands in 2026; Cyble urges managed takedowns over manual abuse reports.

Cyble describes how Google Mandiant-tracked group UNC3753 targeted US professional services firms between January and May 2026 using brand impersonation, spoofed domains, and fake executive profiles. Manual takedowns fail because phishing pages damage brands within hours while removal takes days. A managed takedown program with continuous monitoring and pre-authorized removal cuts the exposure window from days to hours.

Cyble · 29d agoPhishing & fraud in the wild

Wordfence Argus Finds Critical Authentication Bypass in WPMU DEV Dashboard Plugin

Wordfence researchers disclosed a critical unauthenticated authentication bypass in the WPMU DEV Dashboard plugin, enabling admin takeover and possible RCE.

During internal research on August 19, 2026, Wordfence discovered an authentication bypass in the WPMU DEV Dashboard WordPress plugin, which has roughly 350,000 active installations. Unauthenticated attackers can gain administrator access when Hub Single-Sign On is enabled, enabling complete site takeover. If an administrator-writable code mechanism such as the plugin or theme editor is available, the flaw can lead to remote code execution.

Wordfence · 19d agoVulnerability

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 · 21d agoAI tools & infra1

Meta says it’s changing AI suggestions after posing invasive personal questions

Meta is fixing Meta AI suggested prompts after a viral video showed the chatbot compiling invasive questions about a user's children from her Facebook posts.

Meta said it 'missed the mark' and has fixed the issue after Instagram user Kalie Robins showed Meta AI suggesting 'Who is the child passenger?' beneath a video with her child and then piecing together details about her daughters from her and relatives' posts. The assistant also surfaced photos of her children, including one the user claims she deleted years ago. Meta AI is embedded in Facebook, Instagram, WhatsApp, and Messenger; in July the company pulled an Instagram user-deepfake feature after backlash.

The Verge · AIupdated · 4d agofirst · 4d agoAI industry 3 sources

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 · 29d agoAI tools & infra1

Free ChatGPT users get ads picked from whatever they just asked about

OpenAI will serve ads to ChatGPT Free and Go users based on conversation topics, location and device, with personalized targeting behind opt-in.

OpenAI will begin showing ads in ChatGPT for Free and Go plan users, selected from the current conversation topic plus general location and device type. Personalized ad targeting using in-product activity and conversation context will only start after an explicit opt-in, with a setting to manage the choice afterward. Plus, Pro, Enterprise, Business and Education subscribers remain ad-free, and OpenAI states that chats, chat history, memories and personal details are not shared with advertisers. A privacy policy update will document the ad labels, controls and performance reporting.

Help Net Security · 16d agoAI industry

Plug 'n' Pray: Agentic LLM-based Detection of Potential Log File Exposures in Third-Party Content Management System Plugins

Agentic LLM analysis validates 79 log file exposures across 62 of the 300 most-installed WordPress plugins, covering 250M+ active installations.

Researchers built an agentic LLM-based framework combining static and dynamic analysis to automatically detect insecure log files created by WordPress plugins. Scanning the 300 most-installed plugins, which account for roughly 75% of all active installations in the official ecosystem, it produced 81 findings with 79 manually reproduced across 62 plugins. Insufficiently secured log files can disclose credentials and personal data and have led to website compromises. The authors derive a taxonomy of log path and protection patterns and best practices, finding multi-layered protection often absent.

arXiv cs.CR · 1d agoResearch

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 · 13d agoAI tools & infra1