Muse, in its own files

A citation-checked review of Meta's Muse agent, built from its own leaked virtual machine. Each finding links to the files and lines it comes from.

I asked Meta’s Muse for its filesystem and it sent me 6.8 GB | Mouse

I asked Muse to archive the files it could see and send them to my Google Drive. It did.

Muse Creates Detailed Profiles of All Your Friends and Family

Millions have downloaded Meta’s AI agent Muse. But getting it to do your bidding comes with privacy costs.

Proton survey: People don't trust AI, but they tell it everything | Proton

New research finds that most AI chatbot users have shared something sensitive, but few trust the companies behind them.

Meta’s Muse AI sent a YouTuber’s address to a stranger

Here’s what led to the viral post about AI mismanaging deals on Facebook Marketplace.

OpenAI says agents leaked 53 images from ChatGPT users in latest example of rogue activity

Disclosure reveals ⁠new area of privacy risk for the company and illustrates ​how difficult it is to inventory unauthorized activity tied to its agents

Meta's Muse Is Better at Surveilling Than Helping Me

The Muse app continues Meta’s trend of opting users into data collection for AI training. It also nudges you to share your bank account, email, and passport information.

Meta’s Muse is creepy, but maybe not for the reasons you think

Muse probably can’t ‘see’ your notifications. But it also has no idea how it works.

ChatGPT now knows what you do on other websites via ad collector

OpenAI's ad collector at bzr.openai.com sets a cookie called __obi, scoped to .openai.com. The value is while you are on ChatGPT and tied to your ChatGPT account. __obi is then sent to OpenAI from ordinary websites you visit. Any company that buys ads on ChatGPT installs

Can Synthetic Training Data Survive Its Own Regulation?

Synthetic training data has recently emerged as the quick fix to two different problems: the lack of high-quality training data, and the need to avoid using personal data in training to stay within the boundaries of privacy legislation. Gartner predicts that by 2026 75% of all enterprises will use generative AI to create synthetic training data, a huge increase from less than 5% in 2023.1 Moreover, Gartner predicts that by 2035, solutions that use synthetic data for training will grab three quarters of all end-user spending on enterprise AI software.2 Training data that only a few years ago was the subject of several research papers is now carrying the weight of huge enterprise systems.

Large-scale online deanonymization with LLMs

We show that large language models can be used to perform at-scale deanonymization. With full Internet access, our agent can re-identify Hacker News users and Anthropic Interviewer participants at high precision, given pseudonymous online profiles and conversations alone, matching what would take hours for a dedicated human investigator. We then design attacks for the closed-world setting. Given two databases of pseudonymous individuals, each containing unstructured text written by or about that individual, we implement a scalable attack pipeline that uses LLMs to: (1) extract identity-relevant features, (2) search for candidate matches via semantic embeddings, and (3) reason over top candidates to verify matches and reduce false positives. Compared to classical deanonymization work (e.g., on the Netflix prize) that required structured data, our approach works directly on raw user content across arbitrary platforms. We construct three datasets with known ground-truth data to evaluate our attacks. The first links Hacker News to LinkedIn profiles, using cross-platform references that appear in the profiles. Our second dataset matches users across Reddit movie discussion communities; and the third splits a single user's Reddit history in time to create two pseudonymous profiles to be matched. In each setting, LLM-based methods substantially outperform classical baselines, achieving up to 68% recall at 90% precision compared to near 0% for the best non-LLM method. Our results show that the practical obscurity protecting pseudonymous users online no longer holds and that threat models for online privacy need to be reconsidered.

AI tools can unmask anonymous accounts

Goodbye, privacy.

Privacy Considerations with AI Tools

Artificial intelligence (AI) tools come in all sorts of flavors. There are notetaking and transcription tools, chatbots, device-wide agentic AI features, grammar and writing tools, translation assistants, AI summaries, and research tools, among others. As a term, “AI” may refer to features in apps, apps themselves, third-party plug-ins, or a...

Secret Claude tracker shocks users after Anthropic’s anti-surveillance stance

Anthropic accused of spying on users; engineer says “experiment” is over.

Meta Tapped a Pentagon Supplier to Prototype Face Recognition for Its Glasses

Rank One, whose board includes a former CIA deputy director and a former FBI science chief, supplied face recognition to Meta for internal development of its smart glasses app.

Meta Tapped a Pentagon Supplier to Prototype Face Recognition for Its Glasses

Rank One, whose board includes a former CIA deputy director and a former FBI science chief, supplied face recognition to Meta for internal development of its smart glasses app.