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🔥 bia-pain-bache / BPB-Worker-Panel - A GUI Panel providing Worker subscriptions for VLESS, Trojan

GitHub热门项目 | A GUI Panel providing Worker subscriptions for VLESS, Trojan and Warp configs alongside a private DoH server and chain proxies, offering full DNS, clean IP, Fragment, Warp, Warp pro and routing settings for cross-platform clients using Amnezia, Wireguard, Sing-box, Clash/Mihomo and Xray cores. | Stars: 10,613 | 44 stars today | 语言: TypeScript

2026-05-29 原文 →
AI 资讯

Renovate & Dependabot: The New Malware Delivery System

Supply chain attacks every other morning Unless you've lived under a rock for the last few months, you probably noticed that software supply chain attacks are getting trendy among threat actor groups. Over the last 12 months, we've seen more of those than ever before, to name only a few of them: tj-actions/changed-files : In March 2025, a popular reusable GitHub application workflow was compromised to dump secrets from CI/CD pipelines. Salesloft Drift : In August 2025, threat actors stole OAuth credentials from the compromised Drift chatbot application. Shai-Hulud : In September and November 2025, a wormed attack propagated through npm packages and collected secrets. The common thread among those incidents is that they all revolved around secrets, one way or another. Some used secrets as an initial access vector, and others were focused on collecting secrets from victim environments. March 2026 did not change the state of things, with two new severe attacks added to our dreadful collection: trivy-action & LiteLLM campaign by Team PCP. The most popular Axios package compromise. Both those attacks followed a now-classical pattern, spreading through compromised open-source dependencies to maximise the impact in the shortest possible time. Your all-time classic, now with added internal threats Open-source supply chain attacks are not new. Ever since we started using centralized open-source package registries, the risk has existed. Threat actors understood this and started exploiting it. What has changed since 2015 is how we have improved software development productivity through automation. And now, this very same automation that lets you test and build your projects without typing a single command is amplifying the supply-chain threat and the velocity of attacks. Let's see how. Keeping your malware up to date A very concerning pattern we've observed in the trivy-action and Axios campaigns is that automation can become the source of your compromise. One thing no develop

2026-05-29 原文 →
AI 资讯

Auto-Convert Every WordPress Upload to WebP (Free, No Cloud Service)

If you have ever run a Lighthouse audit on a content-heavy WordPress site, you know the usual verdict: "Serve images in next-gen formats." Translation: your JPEGs and PNGs are too heavy, and WebP would cut them down a lot. The problem is that nobody wants to convert images by hand, and most automated options either run as a bulk job you have to remember to trigger, or push your images to a cloud service with a monthly quota. I wanted something simpler: upload an image, get a WebP, done. So I built a free plugin called Pixellize Image Optimizer , and this post walks through the problem, how the plugin solves it, and how it works under the hood. Why WebP is worth it WebP files are usually 25 to 35 percent smaller than the equivalent JPG or PNG at the same visual quality. On an image-heavy page that is a real difference: Faster Largest Contentful Paint, which is one of the Core Web Vitals Google uses for ranking. Less bandwidth, which matters if you pay for CDN traffic. No visible quality loss at sensible quality settings. Browser support is no longer a concern. Every current browser handles WebP. The approach: convert on your own server, on upload Instead of a cloud API, the plugin converts images locally with the PHP GD or Imagick extension (almost every host has one). There is no account, no API key, and no paid tier. The core flow: You upload an image to the Media Library as usual. The plugin generates a WebP version of the full image and every thumbnail size. Your front end serves the WebP automatically. How it works under the hood For WordPress developers, here is the interesting part. The plugin hooks into the standard upload and rendering pipeline rather than fighting it: On upload, it taps into the attachment metadata generation so it can convert the full image and every registered sub-size, not just the original. That matters because responsive images use srcset , and a half-converted set defeats the purpose. On the front end, it rewrites image URLs to the We

2026-05-29 原文 →
AI 资讯

Sidemark: Active Telemetry Comments for C#

OpenTelemetry has quietly become table stakes. That's a good thing, but if you've instrumented a real codebase, you know the tax. A method that does one obvious thing slowly fills up with StartActivity , SetTag , AddEvent , SetStatus . The bookkeeping of telemetry starts to drown out the intent of the code, and in review you spend half your time mentally separating "what this code does" from "what we report about what it does." It's easy to think "oh, but the framework takes care of this with auto-instrumentation", but if you talk to the experts in OTel, they'll go to great lengths to explain that auto-instrumentation is a floor, not a ceiling. Most of the value in telemetry comes from the custom instrumentation you add to your code that adds business context to your traces. And that custom instrumentation is the stuff that clutters up your code. Here's the kind of thing I mean: // before - ugly, obtuse, who put that there var orderId = order . Id ; Activity . Current ?. SetTag ( "orderId" , orderId ); Sidemark is my answer to that code-obfuscation problem: non-invasive instrumentation via what I'm calling Active Comments . // after - glorious, beautiful, basking in the light of the sun, closer to god, happy, satiated var orderId = order . Id ; //? The idea A small set of comment syntaxes - //? , //! , //?! - become ride-along annotations . They travel next to the code, get read at build time, and turn into the equivalent Activity calls in the compiled output. The code you read stays the code that does the work. The telemetry rides along instead of competing with your logic for attention. The framing is loosely inspired by Wallaby.js's Live Annotations , which project runtime values inline next to the code that produced them. Sidemark takes the same instinct in the other direction: comments as a write surface for instrumentation, rather than a read surface for debug values. Comments are an under-used channel for information about code that isn't itself code - and su

2026-05-29 原文 →
AI 资讯

How to investigate suspicious SSH logins without giving AI a shell

A lot of Linux incident response starts with a login question, not a malware sample. Someone sees a spike of failed SSH attempts. A root login appears in the wrong time window. A service account logs in from an address nobody recognizes. A helpdesk ticket says "the server looks weird" and the only concrete clue is a username or IP address. At that point, the useful question is not "is this host compromised?" It is more boring and more important: Did anyone actually authenticate? Which account was involved? Was it password, key, sudo, su, or a scheduled task? Was the same IP seen in web logs, current sockets, process context, or command history? Did persistence, services, packages, or recent files change near the same time? Can another responder review exactly what evidence was collected? That last point matters. If you let an AI assistant freely run shell commands during the first pass, you can get speed, but you also create a new risk: the model may over-collect, mutate the host, or produce a confident answer that nobody can audit later. For a login anomaly, I prefer a read-only evidence loop. A practical first pass Start with the narrow clue if you have one. If the alert names a user: oi login --user root -s 7d If the alert names an IP address: oi login --ip 203.0.113.44 -s 7d If the alert is vague, start wider: oi login -s 7d oi scan -s 7d The goal of the first pass is not to prove every detail. The goal is to build a timeline that a human responder can challenge. For a suspicious SSH login, I want the initial report to answer five things. 1. Authentication pattern Look for the difference between noise and access. A server can receive thousands of failed SSH attempts from the internet. That is useful background, but it is not the same as a successful session. The first split should be: failed attempts only successful login after many failures accepted key from an unusual source login by an account that normally should not be interactive root login where root SSH

2026-05-29 原文 →
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