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UID:dac_DAC 2026_sess315_RESEARCH2804@linklings.com
SUMMARY:Automated Discovery, Classification, and Systematic Security Verif
 ication Methodology for Undocumented Instructions
DESCRIPTION:Yooseok Lee and Junho Jo (Kangwon National University), Hanjoo
 n Kim (Furiosa AI), and Wonjun Song (Kangwon National University)\n\nUndoc
 umented instructions pose a growing security threat, yet current research 
 is limited. \nExisting work focuses on discovery with constrained techniqu
 es, while semantic analysis is manual and ad-hoc. Moreover, a significant 
 gap exists in methodologies for systematic security verification. This pap
 er presents a comprehensive framework for the automated discovery, systema
 tic classification, and security verification of undocumented instructions
 . Our evaluation discovered 23 new instructions and classified 29 using a 
 classifier with 99.8357% accuracy. Most importantly, we verified that 5 in
 structions have tangible security implications, demonstrating our approach
 's efficacy in addressing threats at the processor instruction level.\n\nT
 opics: Quantum\n\nTrack: DES6. Quantum Computing, Systems\n\nSession Chair
 s: Hokeun Kim (Arizona State University); BaekGyu Kim (Daegu Gyeongbuk Ins
 titute of Science and Technology); Xiang Chen (Peking University); Sangyeo
 b Kim (Yonsei University); Robert M. Radway (University of Pennsylvania); 
 Prashanth Mohan (University of Pittsburgh); Xiaoxuan Yang (University of V
 irginia, Stanford University); Ramtin Zand (University of South Carolina);
  Hadi Kamali (University of Central Florida); Jiafeng Xie (Villanova Unive
 rsity); Prabuddha Chakraborty (University of Maine); Junichiro Kadomoto (U
 niversity of Tokyo); and Yosuke Ueno (RIKEN)\n\n
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