Unlocking User-oriented Pages: Intention-driven Black-box Scanner for Real-world Web Applications
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918005043101696 |
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| author | Wang, Weizhe Zhang, Yao Liang, Kaitai Xu, Guangquan Bai, Hongpeng Yan, Qingyang Zheng, Xi Wu, Bin |
| author_facet | Wang, Weizhe Zhang, Yao Liang, Kaitai Xu, Guangquan Bai, Hongpeng Yan, Qingyang Zheng, Xi Wu, Bin |
| contents | Black-box scanners have played a significant role in detecting vulnerabilities for web applications. A key focus in current black-box scanning is increasing test coverage (i.e., accessing more web pages). However, since many web applications are user-oriented, some deep pages can only be accessed through complex user interactions, which are difficult to reach by existing black-box scanners. To fill this gap, a key insight is that web pages contain a wealth of semantic information that can aid in understanding potential user intention. Based on this insight, we propose Hoyen, a black-box scanner that uses the Large Language Model to predict user intention and provide guidance for expanding the scanning scope. Hoyen has been rigorously evaluated on 12 popular open-source web applications and compared with 6 representative tools. The results demonstrate that Hoyen performs a comprehensive exploration of web applications, expanding the attack surface while achieving about 2x than the coverage of other scanners on average, with high request accuracy. Furthermore, Hoyen detected over 90% of its requests towards the core functionality of the application, detecting more vulnerabilities than other scanners, including unique vulnerabilities in well-known web applications. Our data/code is available at https://hoyen.tjunsl.com/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_20801 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unlocking User-oriented Pages: Intention-driven Black-box Scanner for Real-world Web Applications Wang, Weizhe Zhang, Yao Liang, Kaitai Xu, Guangquan Bai, Hongpeng Yan, Qingyang Zheng, Xi Wu, Bin Cryptography and Security Software Engineering Black-box scanners have played a significant role in detecting vulnerabilities for web applications. A key focus in current black-box scanning is increasing test coverage (i.e., accessing more web pages). However, since many web applications are user-oriented, some deep pages can only be accessed through complex user interactions, which are difficult to reach by existing black-box scanners. To fill this gap, a key insight is that web pages contain a wealth of semantic information that can aid in understanding potential user intention. Based on this insight, we propose Hoyen, a black-box scanner that uses the Large Language Model to predict user intention and provide guidance for expanding the scanning scope. Hoyen has been rigorously evaluated on 12 popular open-source web applications and compared with 6 representative tools. The results demonstrate that Hoyen performs a comprehensive exploration of web applications, expanding the attack surface while achieving about 2x than the coverage of other scanners on average, with high request accuracy. Furthermore, Hoyen detected over 90% of its requests towards the core functionality of the application, detecting more vulnerabilities than other scanners, including unique vulnerabilities in well-known web applications. Our data/code is available at https://hoyen.tjunsl.com/ |
| title | Unlocking User-oriented Pages: Intention-driven Black-box Scanner for Real-world Web Applications |
| topic | Cryptography and Security Software Engineering |
| url | https://arxiv.org/abs/2504.20801 |