Built on

Q. P. Nguyen, K. W. Lim, D. M. Divakaran, K. H. Low, and M. C. Chan, “GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection,” in IEEE CNS
2019
Earlier work this paper cites.
S. T. Jan, Q. Hao, T. Hu, J. Pu, S. Oswal, G. Wang, and B. Viswanath, “Throwing Darts in the Dark? Detecting Bots with Limited Data using Neural Data Augmentation,” in IEEE Symposium on Security and Privacy (SP)
2020
Earlier work this paper cites.
J. Lee, F. Tang, P. Ye, F. Abbasi, P. Hay, and D. M. Divakaran, “D-Fence: A Flexible, Efficient, and Comprehensive Phishing Email Detection System,” in IEEE European Symposium on Security and Privacy (EuroS&P)
2021
Earlier work this paper cites.
Y. Lin, R. Liu, D. M. Divakaran, J. Y. Ng, Q. Z. Chan, Y. Lu, Y. Si, F. Zhang, and J. S. Dong, “Phishpedia: A Hybrid Deep Learning Based Approach to Visually Identify Phishing Webpages,” in 30th USENIX Security Symposium
2021
Earlier work this paper cites.
Y. Mirsky and W. Lee, “The creation and detection of deepfakes: A survey,” ACM Computing Surveys (CSUR)
2021
Earlier work this paper cites.
R. Liu, Y. Lin, X. Yang, S. H. Ng, D. M. Divakaran, and J. S. Dong, “Inferring Phishing Intention via Webpage Appearance and Dynamics: A Deep Vision Based Approach,” in USENIX Security Symposium
2022
Earlier work this paper cites.
W. Shi, R. Shea, S. Chen, C. Zhang, R. Jia, and Z. Yu, “Just fine-tune twice: Selective differential privacy for large language models,” arXiv preprint arXiv:2204.07667
Original
2022
Earlier work this paper cites.
O. Asare, M. Nagappan, and N. Asokan, “Is GitHub’s Copilot as Bad as Humans at Introducing Vulnerabilities in Code?,” Empirical Softw. Engg
2023
Earlier work this paper cites.
G. Sandoval, H. Pearce, T. Nys, R. Karri, S. Garg, and B. Dolan-Gavitt, “Lost at c: A user study on the security implications of large language model code assistants,” in 32nd USENIX Security Symposium
2023
Earlier work this paper cites.
H. Pearce, B. Tan, B. Ahmad, R. Karri, and B. Dolan-Gavitt, “Examining Zero-Shot Vulnerability Repair with Large Language Models,” in IEEE Symposium on Security and Privacy (SP)
2023
Earlier work this paper cites.
I. Gur, O. Nachum, Y. Miao, M. Safdari, A. Huang, A. Chowdhery, S. Narang, N. Fiedel, and A. Faust, “Understanding HTML with large language models,” arXiv preprint arXiv:2210.03945
Original
2023
Earlier work this paper cites.
J. Lee, Z. Xin, M. N. P. See, K. Sabharwal, G. Apruzzese, and D. M. Divakaran, “Attacking logo-based phishing website detectors with adversarial perturbations,” in Proc. ESORICS
2023
Earlier work this paper cites.
T. Ali and P. Kostakos, “HuntGPT: Integrating Machine Learning-Based Anomaly Detection and Explainable AI with Large Language Models (LLMs),” arXiv preprint arXiv:2309.16021
Original
2023
Earlier work this paper cites.
J. Puentes, A. Castillo, W. Osejo, Y. Calderón, V. Quintero, L. Saldarriaga, D. Agudelo, and P. Arbeláez, “Guarding the guardians: Automated analysis of online child sexual abuse,” in Proceedings of the IEEE/CVF International Conference on Computer Vision
2023
Earlier work this paper cites.
C. Whitehouse, M. Choudhury, and A. F. Aji, “Llm-powered data augmentation for enhanced crosslingual performance,” arXiv preprint arXiv:2305.14288
Original
2023
Earlier work this paper cites.
Y. Meng, M. Michalski, J. Huang, Y. Zhang, T. Abdelzaher, and J. Han, “Tuning language models as training data generators for augmentation-enhanced few-shot learning,” in Proceedings of the 40th International Conference on Machine Learning
2023
Earlier work this paper cites.
D. Kang, X. Li, I. Stoica, C. Guestrin, M. Zaharia, and T. Hashimoto, “Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks,” in Proc. ICML AdvML Frontiers Workshop
2023
Earlier work this paper cites.
K. Greshake, S. Abdelnabi, S. Mishra, C. Endres, T. Holz, and M. Fritz, “Not What You’ve Signed Up For: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection,” in Proc. 16th ACM Workshop on Artificial Intelligence and Security
2023
Earlier work this paper cites.
Y. Liu, G. Deng, Y. Li, K. Wang, T. Zhang, Y. Liu, H. Wang, Y. Zheng, and Y. Liu, “Prompt Injection attack against LLM-integrated Applications,” arXiv preprint arXiv:2306.05499
Original
2023
Earlier work this paper cites.

Similar

H. Inan, K. Upasani, J. Chi, R. Rungta, K. Iyer, Y. Mao, M. Tontchev, Q. Hu, B. Fuller, D. Testuggine, et al
Original
2023
Cited alongside, same era.
A. Kumar, C. Agarwal, S. Srinivas, S. Feizi, and H. Lakkaraju, “Certifying LLM safety against adversarial prompting,” arXiv preprint arXiv:2309.02705
Original
2023
Cited alongside, same era.
Z. Ba, J. Zhong, J. Lei, P. Cheng, Q. Wang, Z. Qin, Z. Wang, and K. Ren, “SurrogatePrompt: Bypassing the Safety Filter of Text-To-Image Models via Substitution,” arXiv preprint arXiv:2309.14122
Original
2023
Cited alongside, same era.
J. Kirchenbauer, J. Geiping, Y. Wen, J. Katz, I. Miers, and T. Goldstein, “A watermark for large language models,” in Proc. ICML (International Conference on Machine Learning)
2023
Cited alongside, same era.
Published at https://doi.org/10.6028/NIST.AI.100-2e2023

Then

Y. Li, C. Huang, S. Deng, M. L. Lock, T. Cao, N. Oo, B. Hooi, and H. W. Lim, “KnowPhish: Large Language Models Meet Multimodal Knowledge Graphs for Enhancing Reference-Based Phishing Detection,” arXiv preprint arXiv:2403.02253
Original
2024
Closest in time.
https://blog.virustotal.com/2023/04/introducing-virustotal-code-insight.html . Last accessed: Apr. 2024
B. Quintero, “Introducing VirusTotal Code Insight: Empowering threat analysis with generative AI.” · 2024
Closest in time.
W. Qiao, T. Dogra, O. Stretcu, Y.-H. Lyu, T. Fang, D. Kwon, C.-T. Lu, E. Luo, Y. Wang, C.-C. Chia, A. Fuxman, F. Wang, R. Krishna, and M. Tek, “Scaling Up LLM Reviews for Google Ads Content Moderation,” in Proceedings of the 17th ACM International Conference on Web Search and Data Mining
2024
Closest in time.
N. Lee, T. Wattanawong, S. Kim, K. Mangalam, S. Shen, G. Anumanchipali, M. W. Mahoney, K. Keutzer, and A. Gholami, “LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement,” arXiv preprint arXiv:2403.15042
Original
2024
Closest in time.

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Reading the bibliography…

2024

LLMs for Cyber Security: New Opportunities

Divakaran, Dinil Mon, Peddinti, Sai Teja

Understand

Large language models (LLMs) are a class of powerful and versatile models that are beneficial to many industries.

With the emergence of LLMs, we take a fresh look at cyber security, specifically exploring and summarizing the potential of LLMs in addressing challenging problems in the security and safety domains.

Built on

Q. P. Nguyen, K. W. Lim, D. M. Divakaran, K. H. Low, and M. C. Chan, “GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection,” in IEEE CNS
2019
Earlier work this paper cites.
S. T. Jan, Q. Hao, T. Hu, J. Pu, S. Oswal, G. Wang, and B. Viswanath, “Throwing Darts in the Dark? Detecting Bots with Limited Data using Neural Data Augmentation,” in IEEE Symposium on Security and Privacy (SP)
2020
Earlier work this paper cites.
J. Lee, F. Tang, P. Ye, F. Abbasi, P. Hay, and D. M. Divakaran, “D-Fence: A Flexible, Efficient, and Comprehensive Phishing Email Detection System,” in IEEE European Symposium on Security and Privacy (EuroS&P)
2021
Earlier work this paper cites.
Y. Lin, R. Liu, D. M. Divakaran, J. Y. Ng, Q. Z. Chan, Y. Lu, Y. Si, F. Zhang, and J. S. Dong, “Phishpedia: A Hybrid Deep Learning Based Approach to Visually Identify Phishing Webpages,” in 30th USENIX Security Symposium
2021
Earlier work this paper cites.
Y. Mirsky and W. Lee, “The creation and detection of deepfakes: A survey,” ACM Computing Surveys (CSUR)
2021
Earlier work this paper cites.
R. Liu, Y. Lin, X. Yang, S. H. Ng, D. M. Divakaran, and J. S. Dong, “Inferring Phishing Intention via Webpage Appearance and Dynamics: A Deep Vision Based Approach,” in USENIX Security Symposium
2022
Earlier work this paper cites.
W. Shi, R. Shea, S. Chen, C. Zhang, R. Jia, and Z. Yu, “Just fine-tune twice: Selective differential privacy for large language models,” arXiv preprint arXiv:2204.07667
Original
2022
Earlier work this paper cites.
O. Asare, M. Nagappan, and N. Asokan, “Is GitHub’s Copilot as Bad as Humans at Introducing Vulnerabilities in Code?,” Empirical Softw. Engg
2023
Earlier work this paper cites.
G. Sandoval, H. Pearce, T. Nys, R. Karri, S. Garg, and B. Dolan-Gavitt, “Lost at c: A user study on the security implications of large language model code assistants,” in 32nd USENIX Security Symposium
2023
Earlier work this paper cites.
H. Pearce, B. Tan, B. Ahmad, R. Karri, and B. Dolan-Gavitt, “Examining Zero-Shot Vulnerability Repair with Large Language Models,” in IEEE Symposium on Security and Privacy (SP)
2023
Earlier work this paper cites.
I. Gur, O. Nachum, Y. Miao, M. Safdari, A. Huang, A. Chowdhery, S. Narang, N. Fiedel, and A. Faust, “Understanding HTML with large language models,” arXiv preprint arXiv:2210.03945
Original
2023
Earlier work this paper cites.
J. Lee, Z. Xin, M. N. P. See, K. Sabharwal, G. Apruzzese, and D. M. Divakaran, “Attacking logo-based phishing website detectors with adversarial perturbations,” in Proc. ESORICS
2023
Earlier work this paper cites.
T. Ali and P. Kostakos, “HuntGPT: Integrating Machine Learning-Based Anomaly Detection and Explainable AI with Large Language Models (LLMs),” arXiv preprint arXiv:2309.16021
Original
2023
Earlier work this paper cites.
J. Puentes, A. Castillo, W. Osejo, Y. Calderón, V. Quintero, L. Saldarriaga, D. Agudelo, and P. Arbeláez, “Guarding the guardians: Automated analysis of online child sexual abuse,” in Proceedings of the IEEE/CVF International Conference on Computer Vision
2023
Earlier work this paper cites.
C. Whitehouse, M. Choudhury, and A. F. Aji, “Llm-powered data augmentation for enhanced crosslingual performance,” arXiv preprint arXiv:2305.14288
Original
2023
Earlier work this paper cites.
Y. Meng, M. Michalski, J. Huang, Y. Zhang, T. Abdelzaher, and J. Han, “Tuning language models as training data generators for augmentation-enhanced few-shot learning,” in Proceedings of the 40th International Conference on Machine Learning
2023
Earlier work this paper cites.
D. Kang, X. Li, I. Stoica, C. Guestrin, M. Zaharia, and T. Hashimoto, “Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks,” in Proc. ICML AdvML Frontiers Workshop
2023
Earlier work this paper cites.
K. Greshake, S. Abdelnabi, S. Mishra, C. Endres, T. Holz, and M. Fritz, “Not What You’ve Signed Up For: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection,” in Proc. 16th ACM Workshop on Artificial Intelligence and Security
2023
Earlier work this paper cites.
Y. Liu, G. Deng, Y. Li, K. Wang, T. Zhang, Y. Liu, H. Wang, Y. Zheng, and Y. Liu, “Prompt Injection attack against LLM-integrated Applications,” arXiv preprint arXiv:2306.05499
Original
2023
Earlier work this paper cites.

Similar

H. Inan, K. Upasani, J. Chi, R. Rungta, K. Iyer, Y. Mao, M. Tontchev, Q. Hu, B. Fuller, D. Testuggine, et al
Original
2023
Cited alongside, same era.
A. Kumar, C. Agarwal, S. Srinivas, S. Feizi, and H. Lakkaraju, “Certifying LLM safety against adversarial prompting,” arXiv preprint arXiv:2309.02705
Original
2023
Cited alongside, same era.
Z. Ba, J. Zhong, J. Lei, P. Cheng, Q. Wang, Z. Qin, Z. Wang, and K. Ren, “SurrogatePrompt: Bypassing the Safety Filter of Text-To-Image Models via Substitution,” arXiv preprint arXiv:2309.14122
Original
2023
Cited alongside, same era.
J. Kirchenbauer, J. Geiping, Y. Wen, J. Katz, I. Miers, and T. Goldstein, “A watermark for large language models,” in Proc. ICML (International Conference on Machine Learning)
2023
Cited alongside, same era.
Published at https://doi.org/10.6028/NIST.AI.100-2e2023

Then

Y. Li, C. Huang, S. Deng, M. L. Lock, T. Cao, N. Oo, B. Hooi, and H. W. Lim, “KnowPhish: Large Language Models Meet Multimodal Knowledge Graphs for Enhancing Reference-Based Phishing Detection,” arXiv preprint arXiv:2403.02253
Original
2024
Closest in time.
https://blog.virustotal.com/2023/04/introducing-virustotal-code-insight.html . Last accessed: Apr. 2024
B. Quintero, “Introducing VirusTotal Code Insight: Empowering threat analysis with generative AI.” · 2024
Closest in time.
W. Qiao, T. Dogra, O. Stretcu, Y.-H. Lyu, T. Fang, D. Kwon, C.-T. Lu, E. Luo, Y. Wang, C.-C. Chia, A. Fuxman, F. Wang, R. Krishna, and M. Tek, “Scaling Up LLM Reviews for Google Ads Content Moderation,” in Proceedings of the 17th ACM International Conference on Web Search and Data Mining
2024
Closest in time.
N. Lee, T. Wattanawong, S. Kim, K. Mangalam, S. Shen, G. Anumanchipali, M. W. Mahoney, K. Keutzer, and A. Gholami, “LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement,” arXiv preprint arXiv:2403.15042
Original
2024
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

A. Vassilev, A. Oprea, A. Fordyce, and H. Anderson, “Adversarial machine learning: A taxonomy and terminology of attacks and mitigations,” tech. rep., National Institute of Standards and Technology, 2024 · 2024

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“Microsoft, Anthropic, Google, and OpenAI launch Frontier Model Forum.” · 2024

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“Coalition for Content Provenance and Authenticity (C2PA).” · 2024

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X. Zhou, T. Zhang, and D. Lo, “Large language model for vulnerability detection: Emerging results and future directions,” arXiv preprint arXiv:2401.15468

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2024

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Y. Qing, Q. Yin, X. Deng, Y. Chen, Z. Liu, K. Sun, K. Xu, J. Zhang, and Q. Li, “Low-Quality Training Data Only? A Robust Framework for Detecting Encrypted Malicious Network Traffic,” in Proc. NDSS

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“Chatting Our Way Into Creating a Polymorphic Malware.” · 2024

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https://netenrich.com/blog/fraudgpt-the-villain-avatar-of-chatgpt . Last accessed: Apr. 2024

“FraudGPT: The Villain Avatar of ChatGPT.” · 2024

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https://www.microsoft.com/en-us/security/blog/2024/02/14/staying-ahead-of-threat-actors-in-the-age-of-ai/ . Last accessed: Apr. 2024

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https://www.bitdefender.com/blog/hotforsecurity/bing-chat-successfully-duped-into-solving-captcha/ . Last accessed: Apr. 2024

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https://owasp.org/www-project-top-10-for-large-language-model-applications/ . Last accessed: Apr. 2024

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G. Deng, Y. Liu, Y. Li, K. Wang, Y. Zhang, Z. Li, H. Wang, T. Zhang, and Y. Liu, “MASTERKEY: Automated Jailbreaking of Large Language Model Chatbots,” in Proc. NDSS

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P. Guo, F. Liu, X. Lin, Q. Zhao, and Q. Zhang, “L-AutoDA: Leveraging Large Language Models for Automated Decision-based Adversarial Attacks,” arXiv preprint arXiv:2401.15335

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L. Struppek, M. H. Le, D. Hintersdorf, and K. Kersting, “Exploring the adversarial capabilities of large language models,” 2024

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