USENIX

Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are ...
USENIX

Removing Obstacles before Breaking Through the Memory Wall: A Close Look at HBM Errors in the Field

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are ...
USENIX

Half-Double: Hammering From the Next Row Over

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are ...
USENIX

Throwhammer: Rowhammer Attacks over the Network and Defenses

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are ...
USENIX

Lost at C: A User Study on the Security Implications of Large Language Model Code Assistants

Large Language Models (LLMs) such as OpenAI Codex are increasingly being used as AI-based coding assistants. Understanding the impact of these tools on developers’ code is paramount, especially as ...
USENIX

NXNSAttack: Recursive DNS Inefficiencies and Vulnerabilities

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are ...
USENIX

SP-PIFO: Approximating Push-In First-Out Behaviors using Strict-Priority Queues

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are ...
USENIX

Systematic Evaluation of Privacy Risks of Machine Learning Models

Machine learning models are prone to memorizing sensitive data, making them vulnerable to membership inference attacks in which an adversary aims to guess if an input sample was used to train the ...
USENIX

VoteAgain: A scalable coercion-resistant voting system

The strongest threat model for voting systems considers coercion resistance: protection against coercers that force voters to modify their votes, or to abstain. Existing remote voting systems either ...
USENIX

The Ballot is Busted Before the Blockchain: A Security Analysis of Voatz, the First Internet Voting Application Used in U.S. Federal Elections

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are ...
USENIX

USENIX Security '25 Schedule

9:00 am–10:30 am USENIX Security '25 Technical Sessions Ballrooms 6A–6C, Room 611–614, Room 606–609 10:30 am–11:00 am Coffee and Tea Break Room 605–610 11:00 am–12:30 pm USENIX Security '25 Technical ...
USENIX

zpoline: a system call hook mechanism based on binary rewriting

This paper presents zpoline, a system call hook mechanism for x86-64 CPUs. zpoline employs binary rewriting and offers seven advantages: 1) low hook overhead, 2) exhaustive hooking, 3) it does not ...