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MalURLBench: A Benchmark Evaluating Agents' Vulnerabilities When Processing Web URLs
arXiv – CS AI|Dezhang Kong, Zhuxi Wu, Shiqi Liu, Zhicheng Tan, Kuichen Lu, Minghao Li, Qichen Liu, Shengyu Chu, Zhenhua Xu, Xuan Liu, Meng Han|
🤖AI Summary
Researchers have released MalURLBench, the first benchmark to evaluate how LLM-based web agents handle malicious URLs, revealing significant vulnerabilities across 12 popular models. The study found that existing AI agents struggle to detect disguised malicious URLs and proposed URLGuard as a defensive solution.
Key Takeaways
- →MalURLBench is the first benchmark specifically designed to test LLM vulnerabilities to malicious URLs with 61,845 attack instances.
- →Testing revealed that 12 popular LLMs struggle to detect elaborately disguised malicious URLs in real-world scenarios.
- →The benchmark covers 10 real-world scenarios and 7 categories of actual malicious websites for comprehensive testing.
- →Researchers developed URLGuard, a lightweight defense module to help protect against these vulnerabilities.
- →This research addresses a critical security gap as LLM-based web agents become more prevalent in daily applications.
#llm-security#web-agents#malicious-urls#ai-vulnerabilities#cybersecurity#benchmark#urlguard#ai-safety
Read Original →via arXiv – CS AI
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