LessWrong (Curated & Popular)
“Recent AI model progress feels mostly like bullshit” by lc
Mar 25, 2025
The discussion dives into the skeptical view of recent advancements in AI, particularly in cybersecurity. There’s a compelling exploration of whether AI benchmarks genuinely reflect practical performance or if they’re just a facade. Concerns about AI's real-world utility and alignment challenges are addressed. The conversation critiques traditional evaluation metrics, pushing for assessments grounded in actual applications. Additionally, the pitfalls of integrating AI with an emphasis on over-reporting security issues take center stage.
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Quick takeaways
- The evaluation benchmarks currently used for AI in cybersecurity are often insufficient, failing to reflect real-world complexities and practical utility.
- Despite advancements in AI model releases, developers in cybersecurity report minimal improvement in practical performance, highlighting a disconnect between lab results and real-world applications.
Deep dives
Challenges in AI Model Evaluation
There is significant concern regarding the evaluation benchmarks used for AI models in cybersecurity, particularly for application pentesting. Existing public benchmarks tend to focus on isolated code samples or challenges that do not accurately reflect real-world scenarios, making it difficult to assess the effectiveness of AI in navigating complex codebases. The lack of comprehensive evaluation metrics means that while individual models may perform well on traditional benchmarks, this does not translate into practical utility for detecting vulnerabilities in extensive code repositories. This disconnect raises questions about the true capabilities of these models and their readiness for deployment in real-world security contexts.
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