CIPHER: Cybersecurity Intelligent Penetration-Testing Helper for Ethical Researcher
Penetration testing, a critical component of cybersecurity, typically requires extensive time and effort to find vulnerabilities. Beginners in this field often benefit from collaborative approaches with the community or experts. To address this, we develop Cybersecurity Intelligent Penetration-testi...
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| Format: | Article |
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MDPI AG
2024-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/21/6878 |
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| author | Derry Pratama Naufal Suryanto Andro Aprila Adiputra Thi-Thu-Huong Le Ahmada Yusril Kadiptya Muhammad Iqbal Howon Kim |
| author_facet | Derry Pratama Naufal Suryanto Andro Aprila Adiputra Thi-Thu-Huong Le Ahmada Yusril Kadiptya Muhammad Iqbal Howon Kim |
| author_sort | Derry Pratama |
| collection | DOAJ |
| description | Penetration testing, a critical component of cybersecurity, typically requires extensive time and effort to find vulnerabilities. Beginners in this field often benefit from collaborative approaches with the community or experts. To address this, we develop Cybersecurity Intelligent Penetration-testing Helper for Ethical Researchers (CIPHER), a large language model specifically trained to assist in penetration testing tasks as a chatbot. Unlike software development, penetration testing involves domain-specific knowledge that is not widely documented or easily accessible, necessitating a specialized training approach for AI language models. CIPHER was trained using over 300 high-quality write-ups of vulnerable machines, hacking techniques, and documentation of open-source penetration testing tools augmented in an expert response structure. Additionally, we introduced the Findings, Action, Reasoning, and Results (FARR) Flow augmentation, a novel method to augment penetration testing write-ups to establish a fully automated pentesting simulation benchmark tailored for large language models. This approach fills a significant gap in traditional cybersecurity Q&A benchmarks and provides a realistic and rigorous standard for evaluating LLM’s technical knowledge, reasoning capabilities, and practical utility in dynamic penetration testing scenarios. In our assessments, CIPHER achieved the best overall performance in providing accurate suggestion responses compared to other open-source penetration testing models of similar size and even larger state-of-the-art models like Llama 3 70B and Qwen1.5 72B Chat, particularly on insane difficulty machine setups. This demonstrates that the current capabilities of general large language models (LLMs) are insufficient for effectively guiding users through the penetration testing process. We also discuss the potential for improvement through scaling and the development of better benchmarks using FARR Flow augmentation results. |
| format | Article |
| id | doaj-art-1cdfe13c0a4b4c7799ca564c992d86c5 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-1cdfe13c0a4b4c7799ca564c992d86c52024-11-08T14:41:15ZengMDPI AGSensors1424-82202024-10-012421687810.3390/s24216878CIPHER: Cybersecurity Intelligent Penetration-Testing Helper for Ethical ResearcherDerry Pratama0Naufal Suryanto1Andro Aprila Adiputra2Thi-Thu-Huong Le3Ahmada Yusril Kadiptya4Muhammad Iqbal5Howon Kim6School of Computer Science and Engineering, Pusan National University, Busan 46241, Republic of KoreaIoT Research Center, Pusan National University, Busan 46241, Republic of KoreaSchool of Computer Science and Engineering, Pusan National University, Busan 46241, Republic of KoreaBlockchain Platform Research Center, Pusan National University, Busan 46241, Republic of KoreaSchool of Computer Science and Engineering, Pusan National University, Busan 46241, Republic of KoreaSchool of Computer Science and Engineering, Pusan National University, Busan 46241, Republic of KoreaSchool of Computer Science and Engineering, Pusan National University, Busan 46241, Republic of KoreaPenetration testing, a critical component of cybersecurity, typically requires extensive time and effort to find vulnerabilities. Beginners in this field often benefit from collaborative approaches with the community or experts. To address this, we develop Cybersecurity Intelligent Penetration-testing Helper for Ethical Researchers (CIPHER), a large language model specifically trained to assist in penetration testing tasks as a chatbot. Unlike software development, penetration testing involves domain-specific knowledge that is not widely documented or easily accessible, necessitating a specialized training approach for AI language models. CIPHER was trained using over 300 high-quality write-ups of vulnerable machines, hacking techniques, and documentation of open-source penetration testing tools augmented in an expert response structure. Additionally, we introduced the Findings, Action, Reasoning, and Results (FARR) Flow augmentation, a novel method to augment penetration testing write-ups to establish a fully automated pentesting simulation benchmark tailored for large language models. This approach fills a significant gap in traditional cybersecurity Q&A benchmarks and provides a realistic and rigorous standard for evaluating LLM’s technical knowledge, reasoning capabilities, and practical utility in dynamic penetration testing scenarios. In our assessments, CIPHER achieved the best overall performance in providing accurate suggestion responses compared to other open-source penetration testing models of similar size and even larger state-of-the-art models like Llama 3 70B and Qwen1.5 72B Chat, particularly on insane difficulty machine setups. This demonstrates that the current capabilities of general large language models (LLMs) are insufficient for effectively guiding users through the penetration testing process. We also discuss the potential for improvement through scaling and the development of better benchmarks using FARR Flow augmentation results.https://www.mdpi.com/1424-8220/24/21/6878penetration testinglarge language modelpentesting LLMAI penetration testing assistantdomain specific LLMLLM evaluation |
| spellingShingle | Derry Pratama Naufal Suryanto Andro Aprila Adiputra Thi-Thu-Huong Le Ahmada Yusril Kadiptya Muhammad Iqbal Howon Kim CIPHER: Cybersecurity Intelligent Penetration-Testing Helper for Ethical Researcher Sensors penetration testing large language model pentesting LLM AI penetration testing assistant domain specific LLM LLM evaluation |
| title | CIPHER: Cybersecurity Intelligent Penetration-Testing Helper for Ethical Researcher |
| title_full | CIPHER: Cybersecurity Intelligent Penetration-Testing Helper for Ethical Researcher |
| title_fullStr | CIPHER: Cybersecurity Intelligent Penetration-Testing Helper for Ethical Researcher |
| title_full_unstemmed | CIPHER: Cybersecurity Intelligent Penetration-Testing Helper for Ethical Researcher |
| title_short | CIPHER: Cybersecurity Intelligent Penetration-Testing Helper for Ethical Researcher |
| title_sort | cipher cybersecurity intelligent penetration testing helper for ethical researcher |
| topic | penetration testing large language model pentesting LLM AI penetration testing assistant domain specific LLM LLM evaluation |
| url | https://www.mdpi.com/1424-8220/24/21/6878 |
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