A walkthrough of prompt injection attacks against OopsSec Store's AI assistant, bypassing its input filters to extract a flag from the system prompt. OopsSec Store has an AI support assistant with a secret embedded in its system prompt. The only thing standing between us and the flag is a regex blocklist. Spoiler: four regexes are not enough. Initialize the OopsSec Store application: npx create-os
I have a confession. For years, when a developer proudly showed me their Python app — gray square buttons, a Listbox straight out of 1998 — I would politely nod. I've stopped doing that. Not because I turned mean. Because PyQt6 exists, and there's no excuse anymore. This article is my attempt to convince you — yes, you, the one still typing import tkinter out of habit — that something radically be
TL;DR — One API call subscribes a customer endpoint. Centrali signs each delivery with HMAC-SHA256, retries 5 times over ~40 minutes on failure, logs every attempt, and exposes a one-line replay endpoint. No queue. No retry logic. No Svix. The whole subscribe call is right below — scroll to it if you just want the shape. Your customers want webhooks. You know the checklist: A queue so user request
J'ai un aveu à faire : pendant longtemps, quand un dev me montrait fièrement son app Python avec un bouton gris carré et une Listbox qui sentait Windows 95, je hochais la tête poliment. Aujourd'hui, j'ai arrêté. Pas parce que je suis devenu méchant. Parce que PyQt6 existe, et qu'il n'y a plus aucune excuse. Cet article, c'est ma tentative de te convaincre — toi qui ouvres encore tkinter par réflex
In my last article, I mentioned that my SAST tool uses regex-based pattern matching instead of AST parsing, and that this was a deliberate tradeoff. A few people asked me to go deeper on that decision — because on the surface, it sounds like I took a shortcut. I didn't. Or rather — I did, but it was an informed shortcut, and there's a meaningful difference. Let me explain what AST parsing actually
You don’t notice the problem right away. Everything runs smoothly in MySQL… until a new report shows up. Then queries slow down, dashboards lag, and you start realizing you’re stretching the database beyond what it’s good at. That’s usually when BigQuery enters the picture. So the real question becomes: How do you actually move data between them without turning it into a side project? Let’s w
Generate a CycloneDX SBOM and deterministic, audit-ready risk report from your package-lock.json. You run npm audit. It says “47 vulnerabilities.” Cool. Which ones actually matter? The one in your production bundle? You don’t know. So you either: Ignore everything → ship anyway Either way, you lose signal. The real problem isn’t vulnerabilities — it’s decision-making Most tools answer: “What is wr
The Challenge: Beyond the "Lift and Shift" Fatigue The real fear isn’t migration itself—it’s operational fragmentation: different tools, different processes, and different failure modes between the data center and the cloud. After deep-diving into the Nutanix ecosystem, I realized that the goal shouldn't be just moving VMs, but achieving operational symmetry. This is where Nutanix Cloud Clusters