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Linux Kernel Developers Adopt New Fuzzing Tools

Linux Journal
3 weeks 5 days ago
by George Whittaker

The Linux kernel development community is stepping up its security game once again. Developers, led by key maintainers like Greg Kroah-Hartman, are actively adopting new fuzzing tools to uncover bugs earlier and improve overall kernel reliability.

This move reflects a broader shift toward automated testing and AI-assisted development, as the kernel continues to grow in complexity and scale.

What Is Fuzzing and Why It Matters

Fuzzing is a software testing technique that feeds random or unexpected inputs into a program to trigger crashes or uncover vulnerabilities.

In the Linux kernel, fuzzing has become one of the most effective ways to detect:

  • Memory corruption bugs
  • Race conditions
  • Privilege escalation flaws
  • Edge-case failures in subsystems

Modern fuzzers like Syzkaller have already discovered thousands of kernel bugs over the years, making them a cornerstone of Linux security testing.

New Tools Enter the Scene

Recently, kernel maintainers have begun experimenting with new fuzzing frameworks and tooling, including a project internally referred to as “clanker”, which has already been used to identify multiple issues across different kernel subsystems.

Early testing has uncovered bugs in areas such as:

  • SMB/KSMBD networking code
  • USB and HID subsystems
  • Filesystems like F2FS
  • Wireless and device drivers

The speed at which these issues were discovered suggests that these new tools are significantly improving bug detection efficiency.

AI and Smarter Fuzzing Techniques

One of the most interesting developments is the growing role of AI and machine learning in fuzzing.

New research projects like KernelGPT use large language models to:

  • Automatically generate system call sequences
  • Improve test coverage
  • Discover previously hidden execution paths

These techniques can enhance traditional fuzzers by making them smarter about how they explore the kernel’s behavior.

Other advancements include:

  • Better crash analysis and deduplication tools (like ECHO)
  • Configuration-aware fuzzing to explore deeper kernel states
  • Feedback-driven fuzzing loops for improved coverage

Together, these innovations help developers focus on the most meaningful bugs rather than sifting through duplicate reports.

Why This Shift Is Happening Now

The Linux kernel is one of the most complex software projects in existence. With millions of lines of code and contributions from thousands of developers, manually catching every bug is nearly impossible.

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George Whittaker

Skilled Older Workers Turn To AI Training To Stay Afloat

Slashdot
3 weeks 5 days ago
An anonymous reader quotes a report from the Guardian: [Five skilled workers aged 50 and older spoke] to the Guardian about how, after struggling to find work in their fields, they have turned to an emerging and growing category of work: using their expertise to train artificial intelligence models. Known as data annotation, the work involves labeling and evaluating the information used to train AI models like Open AI's ChatGPT or Google's Gemini. A doctor, for example, might review how an AI model answers medical questions to flag incorrect or unsafe responses and suggest better ones, helping the system learn how to generate more accurate and reliable responses. The ultimate goal of training is to level up AI models until they're capable of doing a job as well as a human could -- meaning they could someday replace some of these human workers. The companies behind AI training, such as Mercor, GlobalLogic, TEKsystems, micro1 and Alignerr, operate large contractor networks staffed by people like Ciriello. Their clients include tech giants like OpenAI, Google and Meta, academic researchers and industries including healthcare and finance. For experienced professionals, AI training contracts can be a side hustle -- or a temporary fallback following a layoff -- where top experts can, in some cases, earn over $180 an hour. But that's on the high end. For some older workers [...], it represents another thing entirely: a last refuge in a brutal job market that is harder to stay in, or re-enter, the older they get. For many of them, whether or not they're training their AI replacements in their professions is besides the point. They need the work now. [...] "There's just a lot of desperation out there," Johnson said. As opportunities narrow, many turn to what Joanna Lahey, a professor at Texas A&M University who studies age discrimination and labor outcomes, calls "bridge jobs" -- lower-paying, less demanding roles that help workers stay financially afloat as they approach retirement. Historically, that meant taking temp assignments, retail and fast-food work and gig roles like Uber and food delivery. Now, for skilled workers -- engineers, lawyers, nurses or designers, for example -- using their expertise for AI data training is becoming the new bridge job. "[AI] training work may be better in some ways than those earlier alternatives," Lahey told the Guardian. AI training can offer flexibility, quick income and intellectual engagement. But it's often a clear step down. Professionals in fields such as software development, medicine or finance typically earn six-figure salaries that come with benefits and paid leave, according to the US Bureau of Labor Statistics. According to online job postings, AI training gigs start at $20 an hour, with pay increasing to between $30 and $40 an hour. In some cases, AI trainers with coveted subject matter expertise can earn over $100 an hour. AI training is contract-based, though, meaning the pay and hours are unstable, and it often doesn't come with benefits.

Read more of this story at Slashdot.

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