[AI Seminar Series] Seminar by Prof. Nael Abu-Ghazaleh, Friday October 23rd, 12-1pm, MRB Seminar Room

[Vassilis Tsotras]

The next AI Seminar will be on Friday October 23rd, 12-1pm, in the MRB
Seminar Room (1st floor).

*** Pizza and refreshments will be provided ****

To keep track of the number of attendees, please *register* at:
https://www.eventbrite.com/e/ai-seminar-series-tickets-2003552138380

The talk will be given by *Prof. Nael Abu-Ghazaleh*, Department of Computer
Science and Engineering, UCR

TITLE: From Prompts to Silicon: Security and Privacy threats across the LLM
Stack

ABSTRACT:
Large language models are now trusted with sensitive data, embedded in
consequential decision pipelines, and are hosted on shared, expensive
hardware. These factors make them an increasingly attractive target for
attacks across their training, inference and deployment pipelines. In this
talk, we'll go on a tour of LLM security and privacy threats, organized
around a simple question, looking at different classes of attacks based on
attackers' access points. I'll start where most people think LLM security
lives: at the prompt itself, showing how attacker's with malicious intent
can bypass the model's safety training. A second group of attacks targets
training time, where an attacker who controls even a small slice of a
model's training data can plant hidden behaviors that surface only later,
including those that enable leaking private information or undermining the
safety alignment of the model. Finally, we'll go below the model entirely,
to the hardware it runs on: our recent work shows the GPU infrastructure
powering today's LLMs leaks information across users who are supposed to be
strongly isolated from each other. Taken together, these attacks show that
LLM security isn't one problem but several, spread across different layers
of the stack, including inference time, training time, and hardware and
system vulnerabilities. I'll conclude with some thoughts about how to
secure LLM pipelines.


Bio:
Nael Abu-Ghazaleh is a Professor in the Computer Science and Engineering
Department at the University of California, Riverside. His research is in
architecture and system security, and security for emerging systems. He has
published over 250 papers in these areas, several of which have been
recognized with best paper awards or nominations. His offensive security
research has resulted in the discovery of several new attacks on CPUs and
GPUs that have been disclosed to companies including Intel, AMD, ARM,
Apple, Microsoft, Google, and Nvidia, and resulted in patches and
modifications to products, and coverage from technical news outlets. He is
a member of the Micro Hall of Fame, an ACM distinguished member, an IEEE
distinguished speaker, and an IEEE Computer Society Distinguished
Contributor.

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Sponsored by the RAISE at UCR Institute, the AI Seminar Series presents
speakers working on cutting edge Foundational AI or applying AI in their
research. The goal of these seminars is to inform the UCR community about
current trends in AI research and promote collaborations between faculty in
this emerging field. These seminars are open to interested faculty and
graduate/undergraduate students. Please forward this email to other
colleagues or students in your lab that may be interested. After the seminar a
discussion will follow for questions, open problems, ideas for possible
collaborations etc.

Sincerely,
Vassilis Tsotras
Professor, CSE Department
co-Director, RAISE at UCR Institute

Amit Roy-Chowdhury
Professor, ECE Department
co-Director, RAISE at UCR Institute
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