We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Anatomy of a Frontier Lab Agent Intrusion: A Technical Timeline of the July 2026 Incident
Hugging Face just released this extremely detailed technical description of OpenAI's recent accidental cyberattack against their infrastructure. This attack was very sophisticated, and the resulting document doubles as a crash-course …
Security incident disclosure — July 2026
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Top AI Security Incidents of 2025 Revealed | Adversa AI
Discover how AI systems are being hacked in the wild — from prompt injection to agent abuse — with real breaches, lessons, and defenses in Adversa AI’s 2025 report.
No. 009: AI agents p-hacking, submission surge data, Gemini for Science - The Hallway Track
The Hallway Track No. 009 - How AI is affecting the practice of science
Distributed Denial of Science: How Indirect Data Poisoning of AI Systems Can Industrialize Scientific Fraud
Scientific fraud is the instrument of doubt that malicious entities can use to establish controversy in science. Historically, it required the resources of a company: deep pockets, ghostwritten articles, and corrupt academics. Today, Artificial Intelligence (AI) is increasingly automating scientific research, so we ask: Can a remote adversary weaponize the honest use of AI in science to compromise scientific integrity? We envision and empirically evaluate a new attack, indirect data poisoning, in which an adversary corrupts an open dataset and uploads the poisoned variant to a public repository. Autonomous research agents may independently retrieve and process this data, turning honest scientists into the unpaid and unwitting distributors of fraud at scale. Across five socially-salient topics, from hiring discrimination to the safety of autonomous vehicles, three widely used frontier AI systems (Claude Code with Claude Opus 4.7, Codex with GPT-5.5, Gemini CLI with Gemini 3.1 Pro), and 450 ethically contained experimental runs, we find that poisoning succeeds in 49.56% of runs, while the rate of poisoning detection is only 6.0%. The attack requires no topic-specific trigger-words, agent access, indirect prompt injection, or fabricated papers, only the open data ecosystem and misleading metadata. To mitigate the attacks, we propose and evaluate two measures: a scientist persona and a data provenance audit with five checks (referencing papers, social markers, statistical anomalies, related datasets, poisoning caution). We find that the persona still leaves 16.67% of runs with a poisoned conclusion, but provenance auditing reduces attack success rate to zero. Our results suggest that indirect data poisoning may enable scientific fraud at unprecedented scale, but these attacks can be mitigated with suitable auditing by agents during data retrieval.
Impressions from visiting OpenAI, Anthropic, & Cursor
A peek into where software engineering is headed from inside the sector’s leading AI labs. Agents running in the cloud are a major trend, while coding harnesses are spreading beyond the craft
It’s Time to Investigate the AI Labs - Cal Newport
Over the last several months, the two leading frontier AI labs have shown some brazen behavior. It started with a series of carefully planned announcements ... Read more
No. 017: Science EIC contra AI hype, data poisoning of research agents, Vancouver Standard for AI disclosure - The Hallway Track
The Hallway Track No. 017 - How AI is affecting the practice of science
Anthropic Says Its A.I. Systems Broke Into Computers at 3 Organizations
The disclosure followed OpenAI’s report last week that its own artificial intelligence had hacked into the network of an online library.
Anthropic Says Its A.I. Systems Broke Into Computers at 3 Organizations
The disclosure followed OpenAI’s report last week that its own artificial intelligence had hacked into the network of an online library.
How a 40-Minute Window Brought Down a $10 Billion AI Startup: The Mercor Data Breach, Explained
A poisoned open-source package, a credential-stealing payload, and 4 terabytes of stolen data here’s what every AI company needs to learn…
OpenAI Model Hacks Into HuggingFace During Cybersecurity Evaluation
This latest incident is a rather dramatic escalation in agentic AI cybersecurity breaches.
Daily Dose of Data Science on Twitter / X
Claude Code fully dissected!Researchers from UCL reverse-engineered the leaked Claude source. What they found changes how you should think about agent design.Only 1.6% of the codebase is AI decision logic.The other 98.4% is operational infrastructure. Permission gates, tool… https://t.co/5HYH7qV3wQ pic.twitter.com/5SBHQy5wFH— Daily Dose of Data Science (@DailyDoseOfDS_) June 13, 2026
OpenAI’s models broke free and launched a cyberattack. Congress wants new rules before it happens again.
The first fully autonomous breach by OpenAI’s most powerful AI models has prompted a bipartisan push for stronger oversight over increasingly powerful artificial intelligence models.
Agentic Laboratories of the Future: Towards World Models for Scientific Discovery
Scientific discovery is fundamentally a problem-solving process involving distributed intelligence. Human intuition, computational reasoning, and experimental execution are distributed across people, instruments, and software systems, limiting the speed and scale of discovery. Although automation, high-throughput experimentation, foundation models, and cloud infrastructure have accelerated individual stages of the scientific workflow, they have not unified the discovery process. We hypothesize that the next generation of laboratories will be agentic: environments in which scientists, AI systems, and robotic platforms operate as collaborative discovery partners, with humans contributing the parts of discovery that remain hardest to make explicit: asking the right questions and holding provisional mechanistic models of how a system works. The key missing layer is an agentic harnessing layer that continuously integrates hypothesis, literature-derived evidence, experimental data, uncertainty, and experimental state into a shared “laboratory world model”—a dynamic representation of the scientific system and its evolving context. By maintaining and updating this lab world model, the agentic harnessing layer enables coordinated decision-making, adaptive planning, and increasingly autonomous scientific workflows across humans and machines. A central challenge is that much of the scientific research process remains inaccessible to machines, including tacit knowledge, human observations, adaptive decision-making, and evolving experimental context. Advances in multimodal AI and immersive interfaces may help bridge this gap, allowing humans, agents, and robotic systems to collaborate seamlessly in scientific discovery rather than simply automating isolated tasks. Agentic laboratories could provide a new architecture for science, integrating human, artificial, and physical intelligence into a unified discovery system.
OpenScience.ai — Autonomous AI Research Agents
AI agents conducting reproducible scientific inquiry. Full provenance, executable notebooks, peer validation.
