SShortSingh.
Back to feed

curl to Make Low and Medium Severity Vulnerability Reports Public from October 2026

0
·12 views

Starting 26 October 2026, the curl project will route vulnerability reports rated LOW or MEDIUM to its public GitHub issue tracker, while HIGH and CRITICAL reports will remain private on HackerOne. Maintainer Daniel Stenberg says over 90 percent of submissions in recent years have been rated below HIGH, creating a heavy workload for a small team. Many of these reports come from AI-assisted discovery of unusual input combinations that Stenberg believes rarely occur in real applications. The project has said its maintainers are overloaded and that it must change its approach to survive, and it expects the volume not to decline in the coming months. The change conflicts with the project's published disclosure policy page, which still describes a fully private process.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Log in to join the discussion and vote.

Log in

Related stories

0
ProgrammingDEV Community ·

Build a Typed AI Support Triage Feature in .NET 10 Using IChatClient

The article shows how to add a basic AI feature to a .NET 10 app without a vector database or agent framework, relying on the IChatClient interface. It walks through creating a minimal API project and installing the Microsoft.Extensions.AI packages along with the OpenAI adapter. The author recommends storing API keys in user secrets and registering the chat client once through dependency injection. A support ticket triage endpoint is built that returns a typed C# result containing a category, priority enum, and summary. The piece also covers streaming reply drafts and swapping models with a single change.

0
ProgrammingDEV Community ·

How to Fix Core Web Vitals on a Next.js and Sanity Site, Starting With Mobile

A Next.js and Sanity site showing Poor or Needs Improvement URLs in Search Console can be triaged by focusing on one failing metric at a time. The guidance stresses that Search Console reflects real-user field data over a rolling 28-day window, so a strong Lighthouse score does not offset it. Mobile is usually the first place failures appear and should be addressed first. Pages are grouped by template, so fixing one shared template can clear many URLs at once. The article outlines the 75th-percentile thresholds for LCP, INP and CLS, and covers common causes such as lazy-loaded hero images, missing reserved image dimensions, font swaps and heavy client-side JavaScript.

0
ProgrammingDEV Community ·

Bot-Generated Articles Skew Wikipedia Word Frequencies in Low-Resource Languages

An analysis of word frequency lists from several Wikipedia editions suggests that some minority-language versions are unreliable as linguistic datasets. In balanced human-written text, function words like prepositions and conjunctions dominate, but Tatar and Bashkir Wikipedias are topped by content words such as "river" and "water" because many articles were auto-generated from templates using state water registry data. Erzya and Moksha Wikipedias show a similar distortion, with frequent terms tied to bot-created orchid stubs, a pattern the author calls the Orchid Phenomenon. Average article lengths in these editions are also much shorter than in Russian Wikipedia, raising the risk of repetitive template-driven content. The author advises developers to profile corpora with simple frequency counts before training language models on Wikipedia dumps.

0
ProgrammingDEV Community ·

Quackula: A Haunted Rubber Duck That Hunts Code Smells and Asks the Hard Questions

Quackula is a developer tool described as a haunted rubber duck that lives in the terminal and browser. Its exorcise mode scans a repository for problematic code patterns such as swallowed exceptions, oversized functions, and leftover debug logging, then explains in plain English how to fix each one. A separate debug mode follows the classic rubber-duck approach, prompting the user with pointed questions instead of giving direct answers. The author presents the project as a submission to a Hacktoberfest AI challenge and frames it as a nod to the long-standing rubber-duck debugging tradition. A demo on a deliberately flawed sample project showed thirteen detected issues, each accompanied by a stylized verdict.