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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.
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Hackers Leverage Claude to Exfiltrate Secrets from 1.8M Android apps
ShinyHunters-linked operators used Claude to scan 1.8M Android apps for hardcoded secrets, fueling intrusions across 40+ tenants.
Anthropic's September 2026 threat intelligence report describes a French-speaking operator (aliases MeowSHA, frkoo, blazespider) tied to ShinyHunters who ran 10 AWS EC2 workers and used Claude to decompile and scan 1.8 million Android APKs for hardcoded secrets with TruffleHog. Verified credentials were sorted into 100+ Telegram channels and paired with GitHub PAT harvesting, providing initial access for confirmed intrusions. In one supply-chain incident the actors extracted data from roughly 200 downstream customer organizations and dumped 2,100+ Azure AD token sets across 40+ corporate tenants in about 34 hours using AI agents. Anthropic banned tied accounts and stressed its own systems were not compromised.
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Shuffling is Not Enough: Breaking Permutation-Based Model Confidentiality in Hybrid FHE Inference
Attack breaks permutation-based model confidentiality in hybrid FHE inference, recovering all ResNet-20 linear layers exactly with d+1 queries per layer.
The paper shows output-permutation plus noise fails to protect model confidentiality in hybrid FHE inference: d+1 admissible queries recover an exact permutation-invariant summary of a d-input linear layer, and shuffle-model DP amplification premises cannot hold under correctness-bounded noise. The authors recovered all linear layers of a Safhire-style ResNet-20 end-to-end from TFHE transcripts with zero error, using 5,712 total queries. Exact per-layer recovery was also confirmed on pretrained ImageNet-scale CNNs and ViT-B/16. Leaked layer spectra enable model fingerprinting, lineage attribution, and improved logit-based extraction, while suppressing them destroys inference utility.
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One resignation turned the embers of AI fear into a wildfire
Interconnects argues a frontier-lab researcher's safety resignation went viral via media coordination, reigniting AI existential-risk discourse.
The essay analyzes why researcher Jacob Coxon's resignation over AI safety risks went viral, aided by a Wall Street Journal exclusive, advocacy-group amplification, and Daniel Kokotajlo's same-day Joe Rogan appearance. It notes Evan Hubinger's >10% extinction-risk figure and criticizes existential-risk discourse for conflating very different meanings of the term. The author assigns near-zero probability to complete extinction but argues concrete risks such as cyberattacks on critical infrastructure and bio-risks merit debate. It also rejects recursive self-improvement forecasts, proposing 'lossy self-improvement' where models excel at math and code but remain limited elsewhere.
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Semigroup-JEPA: Latent Dynamics Consistency for Zero-Shot Physics Generalization
SG-JEPA world model conditions latent prediction on physical parameters, halving open-loop prediction error versus DINO-WM and boosting robotic control success.
SG-JEPA extends the LeWorldModel JEPA framework by supplying the governing physics parameter to the temporal model via action-conditioning and jointly training an encoder and predictor through autoregressive latent rollout. On out-of-distribution gravitational-field tasks it reduces open-loop prediction error by up to 2x versus DINO-WM on 2D datasets and increases 3D robotic control success rate up to 2.5x using independently trained diffusion policies. A linear feature analysis attributes most of the gain to the encoder learning features that the predictor can carry forward through rollout.
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