• The curl project has found AI-powered tools to be a mixed bag when it comes to security reports. • At FOSDEM 2026 , curl creator and lead developer Daniel Stenberg used his keynote session to discuss his experience receiving a slew of low-quality reports and, at the same time, realizing that large language model (LLM) tools can sometimes find flaws that other tools have missed. • The curl project has found AI-powered tools to be a mixed bag when it comes to security reports. • At FOSDEM 2026 , curl creator and lead developer Daniel Stenberg used his keynote session to discuss his experience receiving a slew of low-quality reports and, at the same time, realizing that large language model (LLM) tools can sometimes find flaws that other tools have missed.
Article Summaries:
- At FOSDEM 2026, Daniel Stenberg, lead developer of the open‑source curl project, highlighted the mixed results of AI‑powered security tools. While the community received a flood of low‑quality vulnerability reports generated by large language models, Stenberg noted that some LLMs have successfully identified bugs that traditional scanners overlooked. The presentation underscored the need for careful vetting of AI‑derived findings and highlighted the potential of LLMs to complement existing security workflows, even as they introduce noise into the reporting pipeline.
- At FOSDEM 2026, curl creator Daniel Stenberg highlighted the uneven impact of AI on open‑source security. In his keynote, Stenberg noted that the curl project has received a flood of low‑quality vulnerability reports generated by AI tools, which often lack actionable detail. However, he also pointed out that large‑language‑model (LLM) assistants can uncover bugs that traditional static analysis and manual reviews miss. The session underscored the dual nature of AI in security: while it can produce noisy, unreliable findings, it also offers a complementary approach that can surface hidden flaws in codebases.
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