Ziyang Li
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The paper proposes a commit-open protocol using SAE feature-trace commitments to detect silent model substitution in hosted Large Language Models, successfully rejecting various sophisticated attackers where previous methods failed.
The paper presents Symbolon, a framework that learns and applies context-sensitively diverse code transformations to improve symbolic execution, increasing coverage and reducing costs.
Papers
Symbolon: Symbolic Execution by Learning Code Transformation
Jie Zhu, Penghui Li, Zhongxuan Li, Chihao Shen +3 more
The paper presents Symbolon, a framework that learns and applies context-sensitively diverse code transformations to improve symbolic execution, increasing coverage and reducing costs.