16-year-old researcher found a Microsoft bug, got admin access to databases with 17.3 trillion rows

I Built a Visual System Design Reference — Switch Any Architecture Between AWS, Azure, GCP & Open Source

I Built a Visual System Design Reference — Switch Any Architecture Between AWS, Azure, GCP... Tagged with systemdesign, webdev, opensource, showdev.

sbom-cve-check 1.4.0 released

sbom-cve-check 1.4.0 has been released, adding an OpenVEX exporter and improving Linux kernel CVE analysis and version parsing. The new openvex export type, contributed by Andrey Zhizhikin, generates OpenVEX v0.2.0 documents with VEX assessments, configurable via a new --export-author option. Kernel CVE handling now distinguishes mainline versions like 6.12 from stable versions like 6.12.34 to reduce false positives and negatives. Version parsing also gained support for formats like 0.9~rc5 and Ubuntu-style suffixes, and build metadata like 1.25.0+4f85853f64 is now ignored per semver rules. Install or upgrade with pip install --upgrade "sbom-cve-check[extra]".

Red Flags to Watch for When Choosing Between AI Pentesting Vendors

Choosing between AI pentesting vendors requires looking past marketing labels like 'autonomous' or 'agentic' because the underlying systems vary widely. A Stanford-led study ran different AI agent frameworks against a live 8,000-host network and found that between 55% and 82% of reported findings were confirmed as real vulnerabilities. Swapping only the underlying model moved one framework's valid finding rate from 57% to 80%, yet that same framework still scored lowest overall since its findings stayed shallow. On a CTF-style benchmark two frameworks scored near-identically (48.6% vs 45.9%), but on the live network one scored nearly three times higher on severity and complexity. Buyers should demand proof of exploitation, real-network results, clear safety controls, data handling answers and clarity on what 'continuous' actually means.

N-able takes cloud platform offline after 30+ "zero-days" published

N-able has taken its hosted N-central environments offline and urged on-premises customers to shut down their servers after a security researcher publicly disclosed more than 30 alleged zero-day vulnerabilities, including exploits that could let attackers take over vulnerable systems.

Learn Real-World Penetration Testing: Hands-On Ethical Hacking Crash Course

Understanding how malicious actors infiltrate networks is the first step to building resilient infrastructure. To help developers and aspiring security analysts learn the fundamentals of offensive sec

Pwn.college

pwn.college is a free, hands-on education platform for learning core cybersecurity and hacking skills, maintained by a team at Arizona State University. Learners progress through belt ranks, starting with a 'white belt' and earning belts through practice. Hackers continue beyond the platform to 'brown belt' and eventually 'black belt' status, which must happen outside the dojo. The site powers much of ASU's cybersecurity curriculum and asks users not to post writeups or walkthrough videos of challenge solutions. It points learners toward CTFtime.org for Capture The Flag events and a GitHub list of wargames for further practice. The underlying infrastructure is open source and accepts pull requests, though some modules remain closed-source.

Who Gives A Crap? Hackers, apparently, after toilet paper maker's customer data exposed

UK shoppers caught up in third-party security mess, and nobody knows whether the data was flushed away

EP230: RAG vs Agentic RAG

RAG (Retrieval-Augmented Generation) và Agentic RAG là hai phương pháp xử lý thông tin khác nhau trong lĩnh vực AI. RAG hoạt động bằng cách truy vấn vector database để tìm thông tin liên quan trước khi tạo câu trả lời, ví dụ như với vector database có thể chứa hàng triệu document embeddings. Agentic RAG thêm vào một agent có khả năng tự suy luận, cho phép hệ thống tự động xác định cần tìm thêm thông tin hay không, như trường hợp của OpenAI's GPT-4 với function calling. Sự khác biệt chính là Agentic RAG có khả năng tự chủ trong việc quyết định cần làm gì tiếp theo thay vì chỉ phụ thuộc vào kết quả truy vấn ban đầu. Đây là kiến thức quan trọng để hiểu sự tiến hóa từ RAG cơ bản đến các hệ thống thông minh hơn có khả năng tự điều chỉnh.

Bài viết này giúp bạn hiểu rõ sự khác biệt cơ bản giữa RAG và Agentic RAG để áp dụng hiệu quả vào các dự án AI.