Page 79 - 《软件学报》2025年第4期
P. 79

孙伟松 等: 深度代码模型安全综述                                                               1485


                 [19]  Henkel J, Ramakrishnan G, Wang Z, Albarghouthi A, Jha S, Reps T. Semantic robustness of models of source code. In: Proc. of the 2022
                     IEEE  Int’l  Conf.  on  Software  Analysis,  Evolution  and  Reengineering.  Honolulu:  IEEE,  2022.  526–537.  [doi: 10.1109/SANER
                     53432.2022.00070]
                 [20]  Zhu R, Zhang CM. How robust is a large pre-trained language model for code generation? A case on attacking GPT2. In: Proc. of the
                     2023 IEEE Int’l Conf. on Software Analysis, Evolution and Reengineering. Macao: IEEE, 2023. 708–712. [doi: 10.1109/SANER56733.
                     2023.00076]
                 [21]  Zhang  HZ,  Fu  ZY,  Li  G,  Ma  L,  Zhao  ZZ,  Yang  HA,  Sun  YZ,  Liu  Y,  Jin  Z.  Towards  robustness  of  deep  program  processing
                     models—Detection, estimation, and enhancement. ACM Trans. on Software Engineering and Methodology, 2022, 31(3): 50. [doi: 10.
                     1145/3511887]
                 [22]  Zhou Y, Zhang XQ, Shen JJ, Han TT, Chen TL, Gall H. Adversarial robustness of deep code comment generation. ACM Trans. on
                     Software Engineering and Methodology, 2022, 31(4): 60. [doi: 10.1145/3501256]
                 [23]  Quiring E, Maier A, Rieck K. Misleading authorship attribution of source code using adversarial learning. In: Proc. of the 28th USENIX
                     Conf. on Security Symp. Santa Clara: USENIX Association, 2019. 479–496.
                 [24]  Severi G, Meyer J, Coull S, Oprea A. Explanation-guided backdoor poisoning attacks against malware classifiers. In: Proc. of the 30th
                     USENIX Security Symp. Vancouver: USENIX Association, 2021. 1487–1504.
                 [25]  Zhang Z, Tao GH, Shen GY, An SW, Xu QL, Liu YQ, Ye YP, Wu YX, Zhang XY. PELICAN: Exploiting backdoors of naturally trained

                     deep learning models in binary code analysis. In: Proc. of the 32nd USENIX Security Symp. Anaheim: USENIX Association, 2023.
                     2365–2382.
                 [26]  He JX, Vechev M. Large language models for code: Security hardening and adversarial testing. In: Proc. of the 2023 ACM SIGSAC
                     Conf. on Computer and Communications Security. Copenhagen: ACM, 2023. 1865–1879. [doi: 10.1145/3576915.3623175]
                 [27]  Srikant S, Liu SJ, Mitrovska T, Chang SY, Fan QF, Zhang GY, O’Reilly UM. Generating adversarial computer programs using optimized
                     obfuscations. In: Proc. of the 9th Int’l Conf. on Learning Representations. OpenReview.net, 2021.
                 [28]  Li YZ, Liu SQ, Chen KJ, Xie XF, Zhang TW, Liu Y. Multi-target backdoor attacks for code pre-trained models. In: Proc. of the 61st
                     Annual  Meeting  of  the  Association  for  Computational  Linguistics  (Vol.  1:  Long  Papers).  Toronto:  Association  for  Computational
                     Linguistics, 2023. 7236–7254. [doi: 10.18653/v1/2023.acl-long.399]
                 [29]  Zhang HZ, Li Z, Li G, Ma L, Liu Y, Jin Z. Generating adversarial examples for holding robustness of source code processing models. In:
                     Proc. of the 34th AAAI Conf. on Artificial Intelligence. New York: AAAI Press, 2020. 1169–1176.
                 [30]  Jha A, Reddy CK. CodeAttack: Code-based adversarial attacks for pre-trained programming language models. In: Proc. of the 37th AAAI
                     Conf. on Artificial Intelligence. Washington: AAAI Press, 2023. 14892–14900. [doi: 10.1609/aaai.v37i12.26739]
                 [31]  Li  YY,  Wu  HQ,  Zhao  H.  Semantic-preserving  adversarial  code  comprehension.  In:  Proc.  of  the  29th  Int’l  Conf.  on  Computational
                     Linguistics. Gyeongju: Int’l Committee on Computational Linguistics, 2022. 3017–3028.
                 [32]  Yu  XQ,  Li  Z,  Huang  X,  Zhao  SS.  AdVulCode:  Generating  adversarial  vulnerable  code  against  deep  learning-based  vulnerability
                     detectors. Electronics, 2023, 12(4): 936. [doi: 10.3390/electronics12040936]
                 [33]  Ramakrishnan G, Albarghouthi A. Backdoors in neural models of source code. arXiv:2006.06841, 2020.
                 [34]  Springer JM, Reinstadler BM, O’Reilly UM. STRATA: Simple, gradient-free attacks for models of code. arXiv:2009.13562, 2021.
                 [35]  Li J, Li Z, Zhang HZ, Li G, Jin Z, Hu X, Xia X. Poison attack and poison detection on deep source code processing models. ACM Trans.
                     on Software Engineering and Methodology, 2024, 33(3): 62. [doi: 10.1145/3630008]
                 [36]  Qi SY, Yang YH, Gao S, Gao CY, Xu ZL. BadCS: A backdoor attack framework for code search. arXiv:2305.05503, 2023.
                 [37]  Cotroneo  D,  Improta  C,  Liguori  P,  Natella  R.  Vulnerabilities  in  AI  code  generators:  Exploring  targeted  data  poisoning  attacks.
                     arXiv:2308.04451, 2024.
                 [38]  Yang Z, Xu BW, Zhang JM, Kang HJ, Shi JK, He JD, Lo D. Stealthy backdoor attack for code models. arXiv:2301.02496, 2023.
                 [39]  Nguyen  TD,  Zhou  Y,  Le  XBD,  Thongtanunam  P,  Lo  D.  Adversarial  attacks  on  code  models  with  discriminative  graph  patterns.
                     arXiv:2308.11161, 2023.
                 [40]  Zhang J, Ma W, Hu Q, Liu SQ, Xie XF, Traon YL, Liu Y. A black-box attack on code models via representation nearest neighbor search.
                     arXiv:2305.05896, 2023.
                 [41]  Improta C, Liguori P, Natella R, Cukic B, Cotroneo D. Enhancing robustness of AI offensive code generators via data augmentation.
                     arXiv:2306.05079, 2023.
                 [42]  Sun WS, Fang CR, Ge YF, Hu YL, Chen YC, Zhang QJ, Ge XT, Liu Y, Chen ZY. A survey of source code search: A 3-dimensional
                     perspective. arXiv:2311.07107, 2023.
                 [43]  Mikolov T, Karafiát M, Burget L, Cernocký J, Khudanpur S. Recurrent neural network based language model. In: Proc. of the 11th
                     Annual Conf. of the Int’l Speech Communication Association. Makuhari: ISCA, 2010. 1045–1048. [doi: 10.21437/Interspeech.2010-343]
   74   75   76   77   78   79   80   81   82   83   84