2O1 @Netdriver_Mk_1

Tools like Snaffler are great, but crawling SMB shares creates a telemetry nightmare. You instantly light up the SIEM with : - 5140 / 5145 (Network Share Access) - 4656 / 4663 (Object & File Access) So I built Invoke-WindowsSearch to query the native Windows Search DB (OLE DB) directly via WinRM/RPC, It extracts the targets without touching the actual files, completely bypassing the 4663 and 5145 detection footprint. Trade-offs: Requires the WSearch service (disabled by default on Server OS) and lacks complex regex capabilities. Know your environment before execution. #RedTeam #ActiveDirectory #OPSEC #ThreatHunting #PowerShell

Updates for the Linux kernel exploitation collection 😋 github.com/xairy/linux-ke…

x.com/i/article/2060…

Solved the challenge with - InFoFinder_Pro The tool is available on my GitHub: github.com/techenthusiast… #OSINT #Cybersecurity

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The OSINT Newsletter @osintnewsletter

3 weeks ago

🕵️ Our new OSINT CTF challenge is live! This week’s challenge involves conducting an investigation on a phone number linked to a suspected scammer. Play here: ctf.osintnewsletter.com

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10 backend projects that teach you scalability: 0. Notification system 1. Video processing queue 2. Search autocomplete 3. Real-time leaderboard 4. Payment retry engine 5. Email delivery service 6. Analytics event pipeline 7. Collaborative text editor 8. Distributed job runner 9. Social media feed

Hugging Face security analysis: ~70,000 live secrets and API keys, private repos, and leaky pics! 🤖🤗💦🔑😈 More details on: LinkedIn: linkedin.com/posts/dlaskov_… Substack: it4sec.substack.com/p/hugging-face…

Anti-Cheat vs EDR Bypass — Understanding the Techniques Behind Detection Evasion 💀🔥 A deep technical comparison between game anti-cheat bypasses and Endpoint Detection & Response (EDR) evasion techniques. Key Topics Covered • Anti-Cheat vs EDR Architecture • Code Injection & DLL Loading • Function Hooking Techniques • Packet Manipulation Concepts • Process & Memory Abuse • Rootkit Fundamentals • Anti-Debugging Methods • String & Code Obfuscation • Polymorphism & Detection Evasion • Windows API Usage Analysis Why It Matters Although anti-cheat systems and EDR solutions protect different environments, many underlying detection and evasion concepts overlap. Understanding these similarities helps security professionals improve detection engineering, malware analysis, threat hunting, and defensive research. Ideal For • Threat Hunters • Malware Analysts • Detection Engineers • Reverse Engineers • SOC Analysts • Blue Teamers • Security Researchers A valuable resource for understanding how modern detection systems work—and how defenders can build stronger security controls. 🔗 whiteknightlabs.com/2024/02/09/a-t… #CyberSecurity #ThreatHunting #MalwareAnalysis #DetectionEngineering #BlueTeam #SOC #ReverseEngineering #WindowsInternals #EDR #ThreatResearch

Found an 8 year old RCE in MongoDB. Sadly, MongoDB had already found it internally. Technical writeup after the patch is released. Sad Friday... #bugbounty #0day #rce

Who knew a really long string could make an Entra ID login disappear from the logs entirely? In our #blog, @nyxgeek breaks down how overflowing #Azure's sign-in logging mechanism allowed access tokens to be issued without a single log entry. Read it now! hubs.la/Q047xTVc0

我写了一份 Obsidian 的同步教程合集 你可以勾选你想要同步的设备、偏好的同步方式、以及对同步的特别要求，然后网站会给你推荐适配的（我写的） Obsidian 同步教程。 网址：21obsidian.com/sync 这应该是全网最全 每个步骤都带图的 Obsidian 同步教程了

How did he create Linux without OPUS 4.8 ??

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"signalling logs, packet captures, routing data, and other telecommunications sources to trace the methods and origins of advanced surveillance activity. This analysis identified 4G infrastructure associated with operator networks based in Israel, the United Kingdom, and the Channel Islands." citizenlab.ca/research/uncov…

CVE-2026-40369: Arbitrary Kernel Address Increment via NtQuerySystemInformation TL;dR: One syscall from any unprivileged process, even inside Chrome’s renderer sandbox, can increment arbitrary kernel memory addresses, giving SYSTEM privilege escalation pwn2nimron.com/blog

Found a cool bug at Meta. From misconfigured Grafana instance to R/W access on 507 private Meta repositories. Wrote up the full chain here: sectricity.com/blog/misconfig… $157k bounty awarded by @metabugbounty

Bug Bounty Tip - One way I use Google dorks to discover additional assets owned or managed by a target is by identifying unique text that appears across their public-facing systems. This is especially useful when the program scope states that all public-facing assets or IT infrastructure are in scope. For example, if the target’s main domain is example.com, I’ll first browse the site and look for wording, banners, error messages, or any text that uniquely identifies the organization. Once I find something distinctive, I use it in a Google dork to search for other assets that may belong to the same target. A simple example would be ( "Hello world" -".example.com" ). If multiple known subdomains display the phrase “Hello world,” and you’ve confirmed it’s specific to the target, searching that phrase while excluding the main domain can help uncover other related systems, forgotten subdomains, third-party hosted assets, or exposed services tied to the organization. Also, don’t forget to validate that the domain or subdomain actually belongs to the target before testing it. A good way to confirm ownership is by checking where the CNAME record points to, reviewing DNS records, or looking into the domain/subdomain registrar and hosting information.

Free Book, Machine Learning Systems, CC License An open-source curriculum that grew into a global movement for AI engineering education. github.com/harvard-edge/c… AI engineering is the discipline of building efficient, reliable, safe, and robust intelligent systems that operate in the real world, not just models in isolation. The mission is to establish AI engineering as a foundational discipline alongside software engineering and computer engineering, by teaching how to design, build, and evaluate end-to-end intelligent systems. The physics of AI engineering. A rigorous, principles-first treatment of how ML systems are built, optimized, and deployed — from a single machine to fleet-scale infrastructure. A complete curriculum for AI engineering. Volume I: Introduction to Machine Learning Systems Volume II: Machine Learning Systems at Scale mlsysbook.ai

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📉 El fin de "leer el código": La herramienta Open Source que destruye los SaaS de documentación? Llegas a un nuevo equipo. El código tiene 200,000 líneas. Tu Tech Lead te dice: "ve leyéndolo para entenderlo". Abres 400 archivos, te abrumas y cierras la laptop. Pierdes tres días leyendo código que no te enseña nada sobre el sistema real. Understand-Anything (44.4k estrellas) acaba de solucionar esto para siempre. Es un pipeline multi-agente que analiza todo tu repositorio y lo transforma en un mapa de conocimiento interactivo donde puedes clicar, hacer zoom y preguntar lo que quieras. Lo que cambia las reglas del juego: 🧠 Pipeline Multi-Agente: Lee cada archivo, función y dependencia para entregarte un dashboard visual donde cada nodo te da un resumen en texto plano. 🎯 Domain View: No te muestra carpetas aburridas; mapea tu código según la lógica de negocio y los flujos reales del producto. 💬 Preguntas con Contexto: Puedes escribir directamente: "¿Dónde ocurre la autenticación?" o "¿Qué llama a esta función?" y te lleva al punto exacto. 🔌 Integración Total: Funciona de forma nativa como plugin para Claude Code, Cursor, Copilot y Gemini CLI. La gran diferencia con el resto: La mayoría de las herramientas generan diagramas complejos que solo sirven para impresionar en una presentación, pero no te ayudan a resolver un bug el lunes por la mañana. Este mapa está diseñado exclusivamente para enseñarte cómo encaja cada pieza. SaaS como Swimm o Mintlify te cobran cientos de dólares al mes y obligan a subir tu código a sus servidores. Understand-Anything: $0. Cualquier tamaño. En tu máquina local. Privado y libre para siempre (Licencia MIT). El Siguiente Nivel (Idea para Builders): CI/CD Auto-Docs: Un GitHub Action que regenere el mapa visual en cada Pull Request para ver el impacto del cambio antes de mergear. Onboarding Vectorial: Conectar el grafo a un bot de Slack para que los nuevos devs pregunten dudas de la arquitectura y el bot les devuelva la ruta visual exacta. Enlace al repositorio en los comentarios. Guarda este post en marcadores antes de que se pierda en el feed 🔖

How aware are modern compilers of exact microarchitectural layouts? Quite a lot…in one very specific way. Intel x86 is *not* the same as AMD x86. Sure…it’s the “same ISA” in the broadest sense, but the individual instructions often take different numbers of cycles. You want to stall as little as possible, so the order your instructions are arranged is somewhat important. Technically, something like “AMD Zen 3” ordering on an Intel Skylake is sub-optimal. If you look at LLVM, you’ll notice these X86Sched*.td (TableGen) files. Just about every x86 CPU generation has their own version. It defines things like ROB Size, issue width, and misprediction penalties (in terms of cycles). What’s fascinating is how much of this is “guessed” (reverse-engineered) vs “revealed” by the hardware vendors. From what I understand, Intel/AMD/etc will *sometimes* lend a helping hand / give some hints…but less than you’d expect. It’s a very weird situation when you think about it. I assume vendors are tight-lipped about exact latencies for competitive secrecy…yet those are the exact things you’d need to know to extract the most performance out of your compiler! If anyone knows other reasons for the secrecy, I’d love to hear it!

そういえば気づいたらリチェルカが発表していたLLMと難読化されたファイルのRevに関するペーパーが公開されていた。 ndss-symposium.org/wp-content/upl…

Five-bug chain in Samsung Galaxy Store leading to arbitrary local APK install (@bugscale) bugscale.ch/blog/here-we-g… #infosec