Parameter Labs chain exploits ChatGPT HTML previews for account

🚀 RELEASES & LAUNCHES

• Hugging Face releases FineDNA, the largest open dataset for teaching LLMs DNA
  Hugging Face's biology team released FineDNA, the full dataset used to turn Qwen3-14B into Carbon-X, the strongest open DNA language model.
  • Aimed at builders of genomics-focused LLMs: FineDNA covers midtraining, SFT, and RL stages, with code and recipe open under Apache-2.0.
  • Contents: 300B+ tokens of genomes and DNA-rich text for midtraining, ~200K SFT conversations, and ~98K verifiable RL prompts.
  • The dataset spans eukaryote genomic windows up to 100 kbp across six species groups, plus mRNA, splice/promoter context, and prokaryote sequences.
  • It was used to create Carbon-X from Qwen3-14B, described as the strongest open language model on DNA.
  • Dataset is on Hugging Face (HuggingFaceBio/FineDNA, ~955 GB) and code is on GitHub (huggingface/carbon), so the recipe can be applied to other LLMs.
  Sources 📄 Source: huggingface.co · 📎 Coverage: reddit.com · 👁 via r/LocalLLaMA

🔬 RESEARCH

• JKU's SITTA: 109M-parameter image-text alignment model rivals much larger vision-language models
  JKU Linz researchers released SITTA, a 109M-parameter image-to-text alignment model that reportedly outperforms models up to 72B parameters on image-text alignment using 42× less training data.
  • For local-LLM and multimodal researchers: SITTA is a lightweight mapping from image to text domain that lets pretrained language models be conditioned on visual input.
  • Built by Fabian Paischer, Thomas Adler, Markus Hofmarcher, and Sepp Hochreiter at LIT AI Lab, JKU Linz; code and pretrained mappings are open-sourced on GitHub.
  • Trains via least-squares on MS-COCO using CLIP RN50x64 embeddings, and works with Llama, T5-v1_1, FLAN-T5, GPT-J, and GPT-JT.
  • Supports image captioning on MS-COCO and Flickr30k plus retrieval evaluation; supports 8-bit quantization for GPUs under 48GB VRAM.
  • Matters because it shows a tiny alignment layer can match far larger vision-language systems, making image captioning feasible with existing LLMs and modest data.
  • The Reddit post claims it outperforms models up to 72B parameters at image-text alignment using 42× less training data.
  Sources 📄 Source: github.com · 📎 Coverage: reddit.com · 👁 via r/LocalLLaMA

📊 BENCHMARKS & EVALS

• Memdoor team-solve: five Qwen3.8-27B agents match Claude Fable 5 on LiveCodeBench Hard
  The Memdoor team-solve workflow ran five Qwen3.8-27B agents and scored 9.8/10 on the first ten LiveCodeBench Hard problems versus 8.0 for Claude Fable 5 alone.
  • Who it's for: developers running open-weights coding agents who want a documented multi-agent harness recipe.
  • What happened: Memdoor's team-solve example — planner, two ideators, solver, manager, plus a repair worker, each with a fresh context communicating only via files on disk — scored a mean 9.8/10 over five passes on the first ten LiveCodeBench Hard problems, vs 8.0 for Fable 5 alone and 9.6 for the GVS5H harness it reproduces.
  • Cost and speed: fifty runs cost $35.79 (15.3M output tokens); a median run took 40 minutes and $0.39 on Qwen3.8-27B with a 128K reply cap, run via OpenRouter.
  • Caveats: ten problems is a small sample, the workflow claims parity with GVS5H rather than a gain, and the spring-2025 contests may be contaminated in training data.
  • Why it matters: a $0.39-per-problem open-weights recipe matching a frontier closed model on hard coding problems pressures API-based coding tools, though it's a signal, not a swap-in.
  Sources 📄 Source: github.com · 📎 Coverage: aiweekly.co · 👁 via r/LocalLLaMA

⚠️ INCIDENTS & SAFETY

• Parameter Labs chain exploits ChatGPT HTML previews to take over accounts, earning $5,000 bounty
  Parameter Labs disclosed a complex exploit chain against ChatGPT's sandboxed HTML previews that allowed account takeover, which OpenAI fixed and paid a $5,000 bug bounty for.
  • Targets ChatGPT users: a malicious share URL could exfiltrate private data or act on behalf of any signed-in user who visited it.
  • The attack chain: MathLive's virtual keyboard accepted postMessage() from sandboxed iframes, its innerHTML handling allowed injecting an unsandboxed iframe, and the YouTube oEmbed endpoint bypassed CSP to achieve JavaScript execution.
  • With JavaScript running on ChatGPT, attackers could fetch private user data from OpenAI APIs and exfiltrate it via an image reference.
  • OpenAI closed the hole and paid a $5,000 bug bounty, which Simon Willison called cheap for the severity.
  • Highlights that sandboxed artifacts/HTML previews in AI products must stay tightly isolated from parent-page APIs and private data.
  Sources 📄 Source: parameter.ai · 📎 Coverage: simonwillison.net · 👁 via Simon Willison

🏛️ POLICY & REGULATION

• Judge allows NAACP lawsuit over xAI data center gas turbine pollution to proceed
  A federal judge ruled the NAACP's Clean Air Act lawsuit over xAI's unpermitted gas turbines can move forward.
  • The NAACP and NAACP Mississippi State Conference sued xAI and subsidiary MZX Tech in April 2026 for operating 27 gas turbines (495 MW) without an air permit in Southaven, Mississippi, powering the Colossus 2 data center that trains Grok.
  • The plant could emit over 1,700 tons of NOx per year — likely the largest industrial NOx source in greater Memphis — plus up to 180 tons of particulate matter, 500 tons of carbon monoxide, and 19 tons of formaldehyde annually.
  • The suit seeks to halt the turbines, require best available control technology, and impose civil penalties of nearly $124,500 per day of violation.
  • The DOJ intervened in June 2026 seeking dismissal, arguing the facility is critical to national security, citing Grok's use in the Iran strike where it enabled hitting 2,000 targets in 96 hours.
  • The ruling lets the citizen suit proceed despite federal efforts to shut it down, a test case for community enforcement against AI data center pollution.
  Sources 📎 Coverage: techcrunch.com · 👁 via TechCrunch AI