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                      2023. 297–306. [doi: 10.1109/TPS-ISA58951.2023.00044]
                 [119]   Yu L, Lu JY, Liu XL, Yang L, Zhang FJ, Ma JJ. PSCVFinder: A prompt-tuning based framework for smart contract vulnerability
                      detection. In: Proc. of the 34th Int’l Symp. on Software Reliability Engineering (ISSRE). Florence: IEEE, 2023. 556–567. [doi: 10.1109/
                      ISSRE59848.2023.00030]
                 [120]   Ghaleb A, Pattabiraman K. How effective are smart contract analysis tools? Evaluating smart contract static analysis tools using bug
                      injection. In: Proc. of the 29th ACM SIGSOFT Int’l Symp. on Software Testing and Analysis. ACM, 2020. 415–427. [doi: 10.1145/
                      3395363.3397385]
                 [121]   Ma W, Wu DY, Sun YQ, Wang TW, Liu SQ, Zhang J, Xue Y, Liu Y. Combining fine-tuning and LLM-based agents for intuitive smart
                      contract auditing with justifications. arXiv:2403.16073, 2024.
                 [122]   Soud M, Nuutinen W, Liebel G. Soley: Identification and automated detection of logic vulnerabilities in ethereum smart contracts using
                      large language models. arXiv:2406.16244, 2024.
                 [123]   Liu YH, Ott M, Goyal N, Du JF, Joshi M, Chen DQ, Levy O, Lewis M, Zettlemoyer L, Stoyanov V. RoBERTa: A robustly optimized
                      BERT pretraining approach. arXiv:1907.11692, 2019.
                 [124]   Feng ZY, Guo DY, Tang DY, Duan N, Feng XC, Gong M, Shou LJ, Qin B, Liu T, Jiang DX, Zhou M. CodeBERT: A pre-trained model
                      for programming and natural languages. arXiv:2002.08155, 2020.
                 [125]   Liu  Y,  Xue  Y,  Wu  DY,  Sun  YQ,  Li  Y,  Shi  ML,  Liu  Y.  PropertyGPT:  LLM-driven  formal  verification  of  smart  contracts  through
                      retrieval-augmented property generation. arXiv:2405.02580, 2024.
                 [126]   Shou CF, Liu J, Lu DD, Sen K. LLM4Fuzz: Guided fuzzing of smart contracts with large language models. arXiv:2401.11108, 2024.
                 [127]   Sun JZ, Yin ZQ, Zhang HS, Chen X, Zheng W. Adversarial generation method for smart contract fuzz testing seeds guided by chain-
                      based LLM. Automated Software Engineering, 2025, 32(1): 12. [doi: 10.1007/s10515-024-00483-4]
                 [128]   Zhang WQ, Zhang Z, Shi QK, Liu L, Wei LL, Liu YP, Zhang XY, Cheung SC. Nyx: Detecting exploitable front-running vulnerabilities
                      in smart contracts. In: Proc. of the 2024 IEEE Symp. on Security and Privacy (SP). San Francisco: IEEE, 2024. 2198–2216. [doi: 10.
                      1109/SP54263.2024.00146]
                 [129]   Zhou LY, Qin KH, Torres CF, Le DV, Gervais A. High-frequency trading on decentralized on-chain exchanges. In: Proc. of the 2021
                      IEEE Symp. on Security and Privacy (SP). San Francisco: IEEE, 2021. 428–445. [doi: 10.1109/SP40001.2021.00027]
                 [130]   Heimbach L, Wattenhofer R. Eliminating sandwich attacks with the help of game theory. In: Proc. of the 2022 ACM on Asia Conf. on
                      Computer and Communications Security. Nagasaki: ACM, 2022. 153–167. [doi: 10.1145/3488932.3517390]
                 [131]   Li DZ, Zhang KJ, Wang L, Du G. A Geth-based real-time detection system for sandwich attacks in Ethereum. Discover Computing,
                      2024, 27(1): 11. [doi: 10.1007/s10791-024-09445-6]
                 [132]   Xia Q, Huang ZR, Dou WS, Zhang YF, Zhang FJ, Liang G, Zuo C. Detecting flash loan based attacks in Ethereum. In: Proc. of the 43rd
                      Int’l Conf. on Distributed Computing Systems (ICDCS). Hong Kong: IEEE, 2023. 154–165. [doi: 10.1109/ICDCS57875.2023.00078]
                 [133]   Chen ZY, Beillahi SM, Long F. FlashSyn: Flash loan attack synthesis via counter example driven approximation. In: Proc. of the 46th
                      Int’l Conf. on Software Engineering. Lisbon: ACM, 2024. 142. [doi: 10.1145/3597503.3639190]
                 [134]   Li WK, Li XQ, Zhang YQ, Li ZW. DeFiTail: DeFi protocol inspection through cross-contract execution analysis. In: Proc. of the 2024
                      ACM on Web Conf. Singapore: ACM, 2024. 786–789. [doi: 10.1145/3589335.3651488]
                 [135]   Wang B, Liu H, Liu C, Yang ZQ, Ren Q, Zheng HX, Lei H. BLOCKEYE: Hunting for DeFi attacks on blockchain. In: Proc. of the 43rd
                      Int’l  Conf.  on  Software  Engineering:  Companion  Proc.  (ICSE-companion).  Madrid:  IEEE,  2021.  17–20.  [doi:  10.1109/ICSE-
                      Companion52605.2021.00025]
                 [136]   Wang SH, Wu CC, Liang YC, Hsieh LH, Hsiao HC. ProMutator: Detecting vulnerable price oracles in DeFi by mutated transactions. In:
                      Proc. of the 2021 IEEE European Symp. on Security and Privacy Workshops (EuroS&PW). Vienna: IEEE, 2021. 380–385. [doi: 10.
                      1109/EuroSPW54576.2021.00047]
                 [137]   Deng X, Beillahi SM, Minwalla C, Du H, Veneris A, Long F. Safeguarding DeFi smart contracts against oracle deviations. In: Proc. of
                      the 46th Int’l Conf. on Software Engineering. Lisbon: ACM, 2024. 171. [doi: 10.1145/3597503.3639225]
                 [138]   Arora S, Li YJ, Feng YB, Xu JH. SecPLF: Secure protocols for loanable funds against oracle manipulation attacks. In: Proc. of the 19th
                      ACM Asia Conf. on Computer and Communications Security. Singapore: ACM, 2024. 1394–1405. [doi: 10.1145/3634737.3637681]
                 [139]   Wu SW, Yu Z, Wang DB, Zhou YJ, Wu L, Wang HY, Yuan XL. DeFiRanger: Detecting DeFi price manipulation attacks. IEEE Trans.
                      on Dependable and Secure Computing, 2024, 21(4): 4147–4161. [doi: 10.1109/TDSC.2023.3346888]
                 [140]   Kong QP, Chen JC, Wang YL, Jiang ZG, Zheng ZB. Defitainter: Detecting price manipulation vulnerabilities in DeFi protocols. In:
                      Proc.  of  the  32nd  ACM  SIGSOFT  Int’l  Symp.  on  Software  Testing  and  Analysis.  Seattle:  ACM,  2023.  1144–1156.  [doi:  10.1145/
                      3597926.3598124]
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