Significant Raise of Reports
This LWN.net article highlights a dramatic surge in kernel security vulnerability reports, increasing from a few per week to several per day, with many attributed to "AI slop." This influx raises questions about AI's dual role in both generating and identifying software bugs. The discussion further delves into the evolving standards of software quality and security practices in the age of rapid development and easy updates, contrasting it with the perceived rigor of pre-2000 software.
The Lowdown
This LWN.net article, stemming from a comment on an earlier piece, highlights a dramatic surge in vulnerability reports, particularly within the kernel security list. The piece brings into focus the evolving landscape of software quality and security in an era increasingly influenced by AI.
- Kernel security reports have escalated significantly, from an average of 2-3 per week two years ago to 5-10 per day recently.
- Many of these new reports are attributed to "AI slop," yet most are found to be correct, necessitating an increase in maintainer staff to handle the volume.
- The article suggests this rapid increase might represent the "purging of a long backlog" of existing bugs rather than an entirely new wave of flaws.
- There's a hopeful outlook that AI tools could eventually aid in improving code quality before it's merged, thus catching security bugs earlier.
- The article concludes with a provocative thought, positing that overall software quality might eventually revert to pre-2000 levels, when the constraints of physical distribution (CDs, floppies) mandated more thorough testing and stability.
The report underscores a critical juncture in software development, where AI influences both the creation and detection of bugs, challenging established security and quality assurance practices and prompting a re-evaluation of how software is built and maintained.
The Gossip
AI's Ambidextrous Augmentation
The discussion revolves around the complex role of AI in the current software security landscape. Commenters acknowledge the anecdotal increase in CVEs tracked by tools like dependabot, linking it to the idea that Large Language Models (LLMs) might both increase the volume of bugs written and accelerate their discovery. While some express hope that AI tools will eventually help catch security bugs at the point of creation, others voice skepticism, questioning the motives behind "breathless and predictive" positions on AI's benefits, suggesting a lack of data and potential market-driven narratives.
Bygone Bug-Free Backlogs
A significant part of the conversation centers on a nostalgic reflection of software quality before 2000. Many argue that software released in that era was inherently better at the time of release because the constraints of physical distribution (CDs, floppies) forced developers to ensure the software worked thoroughly before shipment. This is contrasted with modern practices where easy updates allow for the release of buggier initial versions, with users effectively serving as testers. A former Microsoft developer from the 90s provides insight into the lower-level considerations and stringent performance demands that shaped development practices back then.
Memory's Malignancy and Modern Medics
One commenter highlights that a substantial majority (around 70%) of security vulnerabilities are attributed to memory safety issues, primarily stemming from software written in C and C++. The discussion points to modern languages like Rust as a promising solution, noting that Google has observed a drastic reduction in new vulnerabilities simply by writing new code in Rust, rather than attempting to rewrite existing C/C++ codebases.