44 citations · 71 across the 19 of their papers we have counts for
5 papers · 1 filter
Time Will Tell: Timing Side Channels via Output Token Count in Large Language Models
Tianchen Zhang, Gururaj Saileshwar, David Lie
This paper demonstrates a new side-channel that enables an adversary to extract sensitive information about inference inputs in large language models (LLMs) based on the number of…
PrisonBreak: Jailbreaking Large Language Models with at Most Twenty-Five Targeted Bit-flips
Zachary Coalson, Jeonghyun Woo, Chris S. Lin +8
We study a new vulnerability in commercial-scale safety-aligned large language models (LLMs): their refusal to generate harmful responses can be broken by flipping only a few bits…
Teaching an Old Dog New Tricks: Verifiable FHE Using Commodity Hardware
Jules Drean, Fisher Jepsen, Edward Suh +3
We present Argos, a simple approach for adding verifiability to fully homomorphic encryption (FHE) schemes using trusted hardware. Traditional approaches to verifiable FHE require…
When Speculation Spills Secrets: Side Channels via Speculative Decoding In LLMs
Jiankun Wei, Abdulrahman Abdulrazzag, Tianchen Zhang +2
Deployed large language models (LLMs) often rely on speculative decoding, a technique that generates and verifies multiple candidate tokens in parallel, to improve throughput and l…
Probabilistic Tracker Management Policies for Low-Cost and Scalable Rowhammer Mitigation
Aamer Jaleel, Stephen W. Keckler, Gururaj Saileshwar
This paper focuses on mitigating DRAM Rowhammer attacks. In recent years, solutions like TRR have been deployed in DDR4 DRAM to track aggressor rows and then issue a mitigative act…