Generic LLMs in Cybersecurity
Generic Large Language Models (GLLMs) are continually being released with increased size and capabilities, enhancing the capabilities of these tools as universal problem solvers. While the reliability of GLLMs' responses is questionable in many situations, these models are often augmented or retrofitted with external resources for various applications, including cybersecurity.
The talk will discuss major security concerns of these pre-trained models: first, GLLMs are prone to adversarial manipulation, such as model poisoning, reverse engineering, and side-channel cyberattacks. Second, the security issues related to LLM-generated codes using open-source libraries/codelets for software development can involve software supply chain attacks. These may result in information disclosure, access to restricted resources, privilege escalation, and complete system takeover.
This talk will also cover the benefits and risks of using GLLMs in cybersecurity, particularly in malware detection, log analysis, intrusion detection, etc. I will highlight the need for diverse AI approaches (non-LLM-based smaller models) trained with application-specific curated data, fine-tuned for well-tested security functionalities in identifying and mitigating emerging cyber threats, including zero-day attacks.
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- Co-sponsored by Vishnu S. Pendyala, SJSU
- Starts 17 June 2025 07:00 AM UTC
- Ends 24 June 2025 07:00 AM UTC
- No Admission Charge
Speakers
Dr. Vishnu S. Pendyala of San Jose State University
Moderator
Biography:
Vishnu S. Pendyala, PhD is a faculty member in Applied Data Science and an Academic Senator with San Jose State University, current chair of the Santa Clara Valley Chapters of IEEE Computer and Computational Intelligence Societies, Area 4 Coordinator for Region 6, and a Distinguished Contributor of the IEEE Computer Society. As a past ACM Distinguished Speaker, researcher, and industry expert, he gave nearly 100 talks and tutorial sessions in various forums such as faculty development programs, the 12th IEEE GHTC, IEEE ANTS, 12th IACC, 10th ICMC, IUCEE, 12th ACM IKDD CODS and 30th COMAD to audiences at venues such as Stanford University, Google, University of Bolton, Computer History Museum, Universidad de Ingeniería y Tecnología, Lima, Peru, IIIT Hyderabad, KREA, IIT Jodhpur, University of Hyderabad, IIT Indore, IIIT Bhubaneswar. Some of these talks are available on YouTube and IEEE.tv. He is a senior member of the IEEE and ACM. He has over two decades of experience in the software industry in the Silicon Valley, USA. His book, “Veracity of Big Data,” is available in several libraries, including those of MIT, Stanford, CMU, the US Congress and internationally. Two other books on machine learning and software development that he edited are also well-received and found place in the US Library of Congress and other reputed libraries. Dr. Pendyala taught a one-week course sponsored by the Ministry of Human Resource Development (MHRD), Government of India, under the GIAN program in 2017 to Computer Science faculty from all over the country and delivered the keynote in a similar program sponsored by AICTE, Government of India in 2022. Dr. Pendyala served on a US government's National Science Foundation (NSF) proposal review panel in 2023. He received the Ramanujan memorial gold medal and a shield for his college at the State Math Olympiad. He also played an active role in the Computer Society of India and was the Program Secretary for its annual national convention.
Address:One Washington Sq, San Jose State University, San Jose, New York, United States, 95192-0250
Prof. Dipankar Dasgupta, IEEE Fellow, NAI Fellow, AIIA Fellow
Generic LLMs in Cybersecurity
Biography:
Dr. Dipankar Dasgupta is a Professor of Computer Science at the University of Memphis since January 1997. He has extensively worked on the applications of bio-inspired and machine learning approaches to cyber defense. His groundbreaking works, including digital immunity, negative authentication, cloud insurance model, and auth-spectrum, have earned recognition in Computer World Magazine and other media outlets. He received research funding from different federal agencies, including NSF, DARPA, IARPA, NSA, NAVY, ONR, DoD, and DHS/FEMA. At the National Cyber Leap Year Summit in 2009, Dr. Dasgupta served as a Co-Chair for the Health-Inspired Network Defense working group (see the report, section 6, starting page 46), the results of which have led to a new research program within the Department of Homeland Security’s Science and Technology. With over 300 publications (including 4 patents), 22000+ citations, and an h-index of 68, Dr. Dasgupta's multidisciplinary research is highly acclaimed. He has received numerous awards, including the 2012 Willard R. Sparks Eminent Faculty Award and the 2014 ACM SIGEVO Impact Award. He also received five best paper awards in different international conferences and has organized Symposia on Computational Intelligence in Cyber Security at IEEE SSCI during 2007-2023. Dr. Dasgupta is an IEEE Fellow, AIIA Fellow, and NAI Fellow, an ACM Distinguished Speaker (2015-2020), an IEEE Distinguished Lecturer (2022-2024), and a 2024 NSF-Fulbright Distinguished Scholar. He regularly serves as a panelist and keynote speaker, and offers tutorials in leading computer science conferences, and has given more than 350 invited talks in different universities and industries.
Address:United States