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This study explores the application of generative AI (GenAI) within manual exploitation and privilege escalation tasks in Linux-based penetration testing environments, two areas critical to comprehensive cybersecurity assessments.
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Bertino, E., Kantarcioglu, M., Akcora, C.G., Samtani, S., Mittal, S., Gupta, M.: AI for security and security for AI. In: Joshi, A., Carminati, B., Verma, R.M. (eds.) CODASPY ’21: Eleventh ACM Conference on Data and Application Security and Privacy, Virtual Event, USA, April 26–28, 2021. pp. 333–334. ACM (2021). https://doi.org/10.1145/3422337.3450357, https://doi.org/10.1145/3422337.3450357
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Ahmed, T., Ghosh, S., Bansal, C., Zimmermann, T., Zhang, X., Rajmohan, S.: Recommending root-cause and mitigation steps for cloud incidents using large language models. In: Proceedings of 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE). pp. 1737–1749. IEEE (2023), https://ieeexplore.ieee.org/abstract/document/10172904/
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2023
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Gupta, M., Akiri, C., Aryal, K., Parker, E., Praharaj, L.: From ChatGPT to ThreatGPT: Impact of generative AI in cybersecurity and privacy. IEEE Access 11
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Harrison, J., Toreini, E., Mehrnezhad, M.: A practical deep learning-based acoustic side channel attack on keyboards. In: IEEE European Symposium on Security and Privacy, EuroS&P 2023 — Workshops, Delft, Netherlands, July 3-7, 2023. pp. 270–280. IEEE (2023). https://doi.org/10.1109/EUROSPW59978.2023.00034, https://doi.org/10.1109/EuroSPW59978.2023.00034
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Pearce, H., Tan, B., Ahmad, B., Karri, R., Dolan-Gavitt, B.: Examining zero-shot vulnerability repair with large language models. In: Proceedings of 2023 IEEE Symposium on Security and Privacy (SP). pp. 2339–2356. IEEE (2023), https://ieeexplore.ieee.org/abstract/document/10179324
Al-Sinani, H., Mitchell, C.: Unleashing AI in ethical hacking: A preliminary experimental study. Technical report, Royal Holloway, University of London (2024), https://pure.royalholloway.ac.uk/files/58692091/TechReport_UnleashingAIinEthicalHacking.pdf
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Al-Sinani, H.S., Mitchell, C.J., Sahli, N., Al-Siyabi, M.: Unleashing AI in ethical hacking. In: Proceedings of the STM 2024, the 20th International Workshop on Security and Trust Management (co-located with ESORICS 2024), Bydgoszcz, Poland. p. to appear. LNCS, Springer (2024), https://www.chrismitchell.net/Papers/uaieh.pdf
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Fujii, S., Yamagishi, R.: Feasibility study for supporting static malware analysis using LLM. In: Proceedings of the SecAI 2024, the Workshop on Security and Artificial Intelligence (co-located with ESORICS 2024), Bydgoszcz, Poland. p. to appear. LNCS series, Springer (2024), https://drive.google.com/file/d/14EW8RJnE4QUBG0mIoMVM0chzJcp1nQon/view
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2023
Cited alongside, same era.
2024
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Jiang, F., Xu, Z., Niu, L., Xiang, Z., Ramasubramanian, B., Li, B., Poovendran, R.: ArtPrompt: ASCII art-based jailbreak attacks against aligned LLMs. Tech. rep. (2024). https://doi.org/10.48550/ARXIV.2402.11753, https://doi.org/10.48550/arXiv.2402.11753
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Park, S., Lee, H., Cha, S.K.: Systematic bug reproduction with large language model. In: Proceedings of the SecAI 2024, the Workshop on Security and Artificial Intelligence (co-located with ESORICS 2024), Bydgoszcz, Poland. p. to appear. LNCS series, Springer (2024), https://drive.google.com/file/d/14dafpfhAnp9YLb9YIC4YbVJKwTPc_dQ3/view
2024
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