Increasingly, web content is automatically generated by large language models (LLMs) with little human input. We call this ''LLM-dominant'' content. Since LLMs plagiarize and hallucinate, LLM-dominant content can be unreliable and unethical. Yet, websites rarely disclose such content, and human readers struggle to distinguish it. Thus, we must develop reliable detectors for LLM-dominant content. However, state-of-the-art LLM detectors are inaccurate on web content, because web content has low positive rates, complex markup, and diverse genres, instead of clean, prose-like benchmark data SoTA detectors are optimized for.

The Routing Policy Specification Language (RPSL) enables operators to specify routing policies in public registries. These policies contain information for traffic engineering, troubleshooting routing incidents, and automatically configuring route filters to improve security. RPSL information is also valuable for researchers to better understand the Internet. However, the RPSL's complexities make these policies challenging to interpret programmatically. We introduce RPSLyzer, a tool that can parse and interpret 99.99% of RPSL policies. We use RPSLyzer to characterize the RPSL policies of 78,701 Autonomous Systems (ASes) and verify 779 million BGP routes against these policies. We find RPSL usage varies widely among ASes, identify common RPSL misuses that explain most route verification failures, and offer operators recommendations to improve RPSL usage.