6 papers
Investigating The Security of Modern AI and Cloud Infrastructure
Andrew Adiletta
The widespread deployment of Deep Neural Networks and Large Language Models (LLMs) relies on a foundational assumption of isolation that this dissertation challenges. This work sys…
Super Suffixes: Bypassing Text Generation Alignment and Guard Models Simultaneously
Andrew Adiletta, Kathryn Adiletta, Kemal Derya +1
The rapid deployment of Large Language Models (LLMs) has created an urgent need for enhanced security and privacy measures in Machine Learning (ML). LLMs are increasingly being use…
Rubber Mallet: A Study of High Frequency Localized Bit Flips and Their Impact on Security
Andrew Adiletta, Zane Weissman, Fatemeh Khojasteh Dana +2
The increasing density of modern DRAM has heightened its vulnerability to Rowhammer attacks, which induce bit flips by repeatedly accessing specific memory rows. This paper present…
LeapFrog: The Rowhammer Instruction Skip Attack
Andrew Adiletta, M. Caner Tol, Kemal Derya +2
Since its inception, Rowhammer exploits have rapidly evolved into increasingly sophisticated threats compromising data integrity and the control flow integrity of victim processes.…
Spill The Beans: Exploiting CPU Cache Side-Channels to Leak Tokens from Large Language Models
Andrew Adiletta, Berk Sunar
Side-channel attacks on shared hardware resources increasingly threaten confidentiality, especially with the rise of Large Language Models (LLMs). In this work, we introduce Spill…
Mayhem: Targeted Corruption of Register and Stack Variables
Andrew J. Adiletta, M. Caner Tol, Yarkın Doröz +1
In the past decade, many vulnerabilities were discovered in microarchitectures which yielded attack vectors and motivated the study of countermeasures. Further, architectural and p…