Maryam Rostamipoor (Stony Brook University), Seyedhamed Ghavamnia (University of Connecticut), Michalis Polychronakis (Stony Brook University)

As the use of language-level sandboxing for running untrusted code grows, the risks associated with memory disclosure vulnerabilities and transient execution attacks become increasingly significant. Besides the execution of untrusted JavaScript or WebAssembly code in web browsers, serverless environments have also started relying on language-level isolation to improve scalability by running multiple functions from different customers within a single process. Web browsers have adopted process-level sandboxing to mitigate memory leakage attacks, but this solution is not applicable in serverless environments, as running each function as a separate process would negate the performance benefits of language-level isolation.

In this paper we present LeakLess, a selective data protection approach for serverless computing platforms. LeakLess alleviates the limitations of previous selective data protection techniques by combining in-memory encryption with a separate I/O module to enable the safe transmission of the protected data between serverless functions and external hosts. We implemented LeakLess on top of the Spin serverless platform, and evaluated it with real-world serverless applications. Our results demonstrate that LeakLess offers robust protection while incurring a minor throughput decrease under stress-testing conditions of up to 2.8% when the I/O module runs on a different host than the Spin runtime, and up to 8.5% when it runs on the same host.

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THEMIS: Regulating Textual Inversion for Personalized Concept Censorship

Yutong Wu (Nanyang Technological University), Jie Zhang (Centre for Frontier AI Research, Agency for Science, Technology and Research (A*STAR), Singapore), Florian Kerschbaum (University of Waterloo), Tianwei Zhang (Nanyang Technological University)

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“Do We Call Them That? Absolutely Not.”: Juxtaposing the...

Alexandra Klymenko (Technical University of Munich), Stephen Meisenbacher (Technical University of Munich), Luca Favaro (Technical University of Munich), and Florian Matthes (Technical University of Munich)

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Secure Transformer Inference Made Non-interactive

Jiawen Zhang (Zhejiang University), Xinpeng Yang (Zhejiang University), Lipeng He (University of Waterloo), Kejia Chen (Zhejiang University), Wen-jie Lu (Zhejiang University), Yinghao Wang (Zhejiang University), Xiaoyang Hou (Zhejiang University), Jian Liu (Zhejiang University), Kui Ren (Zhejiang University), Xiaohu Yang (Zhejiang University)

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Repurposing Neural Networks for Efficient Cryptographic Computation

Xin Jin (The Ohio State University), Shiqing Ma (University of Massachusetts Amherst), Zhiqiang Lin (The Ohio State University)

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