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first_img Google disclosed the AI security agent PageBreak, which has identified over 500 vulnerabilities

The Google Product Security Team has disclosed an internal AI agent called PageBreak, used to test the security of its first-party web applications. This agent is built on Google's Gemini model and began a pilot program in November 2025, transitioning to a formal project in January 2026, with the goal of autonomously scaling vulnerability discovery and reducing manual input.Unlike common AI scanning tools, PageBreak hands over hypotheses to specialized validators after discovering suspicious defects, attempting actual exploitation in a real-time running copy of the application, and only reports once confirmed exploitable, with a false positive rate close to zero. Google claims that PageBreak has identified over 500 XSS vulnerabilities in its first-party web applications, which can be used to hijack login sessions, steal data, or impersonate users.Google stated that the security team has been overwhelmed in recent years by a large number of AI-generated vulnerability reports that appear reasonable but are not valid, making it a major challenge to distinguish real defects from hallucinations. When testing applications built using the next-generation high-assurance framework, PageBreak found only two vulnerabilities. The next step for Google is to integrate PageBreak with the automated remediation agent CodeMender, providing confirmed vulnerabilities with accompanying fixes.

first_img Chamath: The open-source weighted model is about four months away from the best closed-source frontier model

Social Capital founder Chamath Palihapitiya released an in-depth research report stating that open-source weight models are about four months away from matching the best closed-source frontier models in public evaluations, with increasing fluctuations in the gap. If open-source models allow companies more control over data, infrastructure, and customization while approaching frontier performance, the value corresponding to companies still paying for frontier laboratories becomes a business issue. Openness exists on a spectrum, from fully open-source models that can be downloaded and freely modified to open-source weight models with various restrictions, while closed-source models keep weights proprietary.Palantir CEO Alex Karp warned that companies might hand over differentiated proprietary knowledge and processes to frontier model providers. Microsoft CEO Satya Nadella stated that companies are effectively paying for intelligence twice: once in money and again in the more valuable proprietary knowledge that must be disclosed to make the intelligence useful. Despite concerns, companies are still willing to pay for frontier performance, even if the best open-source weight models are only months behind, with frontier laboratory revenues continuing to accelerate.Leading companies use both types of models, leveraging open-source models for control and customization while utilizing closed-source frontier models for maximum capability, with some cases reporting up to 12 times engineering efficiency and over 20 times cost savings. Some vendors adopt a dual-track approach, with Google offering both Gemini and Gemma, and Meta providing both Muse Spark and Llama. The 99-page report also discusses the costs of maintaining a lead for frontier laboratories, five factors of model competition, model operating locations, and investments in open-source weights by NVIDIA and Samsung.
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