… AI could help create a dramatically better future, but that outcome is not guaranteed. The world’s leading AI companies believe they could be close to automating AI research. It is hard to predict exactly how much this will accelerate AI progress, but there is a real risk that capability development rapidly accelerates beyond our ability to understand or control the resulting systems.
To realize AI’s potential, industry, government, and society at large may need the option to buy time to address emerging risks, develop security measures, and strengthen oversight. But each company—and country—is under intense competitive pressure not to unilaterally slow that acceleration. And today, the world lacks the technical and governance tools to deliberately pace frontier-wide progress. — Read More
Daily Archives: July 30, 2026
Comparing Open-Source AI Code Security Harnesses
There’s a huge new wave of open-source AI security projects that point a large language model at a codebase to find vulnerabilities. We looked at some popular (or less popular, but from interesting companies) OSS tools and compared how they work, and when you’d reach for each.
We find three complementary categories in the new world of harnesses: LLM-led exploitgen, LLM-skill-boosting, and SAST+LLM hybrids. — Read More
The Arguments Against Open Source AI are Very Bad
The release of Kimi K3 has opened a fresh round of angst and confused discourse. There’s a loud cohort of journalists, business leaders, and politicians arguing that open source AI is a dangerous threat.
Freely available AI for anyone? The horror!
Frontier labs’ case against open source AI is essentially: Open source models1 are dangerous (and un-American!). We should open the AI Pandora’s Box, but only with responsible gatekeepers (toll collectors, preferably us!). Only trusted users (our most profitable customers) should be able to use it. — Read More
How ChatGPT Optimizes its Agent Loop: Harness, API, and Inference
AI labs are moving faster than ever and releasing the most capable models we have ever seen. … But capability is only half of the picture. The other half is how much it costs these models to complete tasks, that is, the cost per successful task. Lower cost makes the model more affordable for users and less costly for the provider. A huge amount of effort inside the labs goes into making every component and layer of their AI applications optimized and more efficient, to reduce the overall cost. For example, GPT 5.6 Sol with max reasoning scores higher than Fable 5 on the Artificial Analysis Coding Agent Index while costing less than half as much.
To understand what techniques are adopted in frontier labs to make AI applications more efficient, we met with the OpenAI engineers who developed and shipped various efficiency techniques into the systems behind Codex and ChatGPT Work. — Read More
Anatomy of a Frontier Lab Agent Intrusion: A Technical Timeline of the July 2026 Incident
A companion technical writeup to our incident disclosure. This post walks through how the intrusion actually worked: the two initial-access vectors, how the agent pivoted and moved laterally, representative examples of the commands that were run and how we investigated with GLM 5.2 (an open-source model). Live credentials, internal hostnames, and specific indicators have been redacted or genericized, while the techniques are described exactly as observed by Hugging Face.
We are publishing this level of detail because the technique matters more than the incident, as it reveals the emerging attack capabilities of the frontier agents, how they could be used by rogue actors, and how everyone should be prepared as defenders. — Read More
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