Penghao Lv

Hechuan Guo, Yongle Hao, Yue Zhang et al.

The Model Context Protocol (MCP) has been proposed as a unifying standard for connecting large language models (LLMs) with external tools and resources, promising the same role for AI integration that HTTP and USB played for the Web and peripherals. Yet, despite rapid adoption and hype, its trajectory remains uncertain. Are MCP marketplaces truly growing, or merely inflated by placeholders and abandoned prototypes? Are servers secure and privacy-preserving, or do they expose users to systemic risks? And do clients converge on standardized protocols, or remain fragmented across competing designs? In this paper, we present the first large-scale empirical study of the MCP ecosystem. We design and implement MCPCrawler, a systematic measurement framework that collects and normalizes data from six major markets. Over a 14-day campaign, MCPCrawler aggregated 17,630 raw entries, of which 8,401 valid projects (8,060 servers and 341 clients) were analyzed. Our results reveal that more than half of listed projects are invalid or low-value, that servers face structural risks including dependency monocultures and uneven maintenance, and that clients exhibit a transitional phase in protocol and connection patterns. Together, these findings provide the first evidence-based view of the MCP ecosystem, its risks, and its future trajectory.

Weitao Feng, Lixu Wang, Tianyi Wei et al.

As large language models (LLMs) continue to grow in capability, so do the risks of harmful misuse through fine-tuning. While most prior studies assume that attackers rely on supervised fine-tuning (SFT) for such misuse, we systematically demonstrate that reinforcement learning (RL) enables adversaries to more effectively break safety alignment and facilitate advanced harmful task assistance, under matched computational budgets. To counter this emerging threat, we propose TokenBuncher, the first effective defense specifically targeting RL-based harmful fine-tuning. TokenBuncher suppresses the foundation on which RL relies: model response uncertainty. By constraining uncertainty, RL-based fine-tuning can no longer exploit distinct reward signals to drive the model toward harmful behaviors. We realize this defense through entropy-as-reward RL and a Token Noiser mechanism designed to prevent the escalation of expert-domain harmful capabilities. Extensive experiments across multiple models and RL algorithms show that TokenBuncher robustly mitigates harmful RL fine-tuning while preserving benign task utility and finetunability. Our results highlight that RL-based harmful fine-tuning poses a greater systemic risk than SFT, and that TokenBuncher provides an effective and general defense.