Fetching the paper…
Reading the bibliography…
The rapid advancement of Large Language Models (LLMs) has led to the emergence of Multi-Agent Systems (MAS) to perform complex tasks through collaboration.
N. Carlini, S. Chien, M. Nasr, S. Song, A. Terzis, and F. Tramer, “Membership inference attacks from first principles,” in 2022 IEEE symposium on security and privacy (SP) . IEEE, 2022, pp. 1897–1914
1914
Earlier work this paper cites.
C. Wang, Z. Li, Y. Pena, S. Gao, S. Chen, S. Wang, C. Gao, and M. R. Lyu, “Reef: A framework for collecting real-world vulnerabilities and fixes,” in 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 2023, pp. 1952–1962
1962
Earlier work this paper cites.
Farber, “Topological complexity of motion planning,” Discrete & Computational Geometry , vol. 29, pp. 211–221, 2003
2003
Earlier work this paper cites.
K. Petersen, C. Wohlin, and D. Baca, “The waterfall model in large-scale development,” in Product-Focused Software Process Improvement: 10th International Conference, PROFES 2009, Oulu, Finland, June 15-17, 2009. Proceedings 10 . Springer, 2009, pp. 386–400
2009
Earlier work this paper cites.
X. Gao, B. Xiao, D. Tao, and X. Li, “A survey of graph edit distance,” Pattern Analysis and applications , vol. 13, pp. 113–129, 2010
2010
Earlier work this paper cites.
P. Hoffman, M. A. Lambon Ralph, and T. T. Rogers, “Semantic diversity: A measure of semantic ambiguity based on variability in the contextual usage of words,” Behavior research methods , vol. 45, pp. 718–730, 2013
2013
Earlier work this paper cites.
F. Tramèr, F. Zhang, A. Juels, M. K. Reiter, and T. Ristenpart, “Stealing machine learning models via prediction { \{ APIs } \} ,” in 25th USENIX security symposium (USENIX Security 16) , 2016, pp. 601–618
2016
Earlier work this paper cites.
N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, and A. Swami, “Practical black-box attacks against machine learning,” in Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security , ser. ASIA CCS ’17. New York, NY, USA: Association for Computing Machinery, 2017, p. 506–519. [Online]. Available: https://doi.org/10.1145/3052973.3053009
2017
Earlier work this paper cites.
R. Shokri, M. Stronati, C. Song, and V. Shmatikov, “Membership inference attacks against machine learning models,” in 2017 IEEE symposium on security and privacy (SP) . IEEE, 2017, pp. 3–18
2017
Earlier work this paper cites.
T. Orekondy, B. Schiele, and M. Fritz, “Knockoff nets: Stealing functionality of black-box models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 4954–4963
2019
Earlier work this paper cites.
M. Nasr, R. Shokri, and A. Houmansadr, “Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning,” in 2019 IEEE symposium on security and privacy (SP) . IEEE, 2019, pp. 739–753
2019
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
M. Jagielski, N. Carlini, D. Berthelot, A. Kurakin, and N. Papernot, “High accuracy and high fidelity extraction of neural networks,” in 29th USENIX security symposium (USENIX Security 20) , 2020, pp. 1345–1362
2020
Earlier work this paper cites.
Z. Chen, W. Chen, C. Smiley, S. Shah, I. Borova, D. Langdon, R. Moussa, M. Beane, T.-H. Huang, B. Routledge, and W. Y. Wang, “Finqa: A dataset of numerical reasoning over financial data,” Proceedings of EMNLP 2021 , 2021
2021
Earlier work this paper cites.
F. Perez and I. Ribeiro, “Ignore previous prompt: Attack techniques for language models,” in NeurIPS ML Safety Workshop , 2022. [Online]. Available: https://openreview.net/forum?id=qiaRo_7Zmug
2022
Earlier work this paper cites.
J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou et al. , “Chain-of-thought prompting elicits reasoning in large language models,” Advances in neural information processing systems , vol. 35, pp. 24 824–24 837, 2022
2022
Earlier work this paper cites.
Z. Li, P. Ma, H. Wang, S. Wang, Q. Tang, S. Nie, and S. Wu, “Unleashing the power of compiler intermediate representation to enhance neural program embeddings,” in 44th IEEE/ACM 44th International Conference on Software Engineering, ICSE 2022, Pittsburgh, PA, USA, May 25-27, 2022 . ACM, 2022
2022
Earlier work this paper cites.
J. Ye, A. Maddi, S. K. Murakonda, V. Bindschaedler, and R. Shokri, “Enhanced membership inference attacks against machine learning models,” in Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security , 2022, pp. 3093–3106
2022
Earlier work this paper cites.
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
L. Wang, C. Ma, X. Feng, Z. Zhang, H. ran Yang, J. Zhang, Z.-Y. Chen, J. Tang, X. Chen, Y. Lin, W. X. Zhao, Z. Wei, and J. rong Wen, “A survey on large language model based autonomous agents,” Frontiers Comput. Sci. , vol. 18, p. 186345, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:261064713
2023
Earlier work this paper cites.
C. Qian, W. Liu, H. Liu, N. Chen, Y. Dang, J. Li, C. Yang, W. Chen, Y. Su, X. Cong, J. Xu, D. Li, Z. Liu, and M. Sun, “Chatdev: Communicative agents for software development,” in Annual Meeting of the Association for Computational Linguistics , 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:270257715
2023
Earlier work this paper cites.
S. Hong, X. Zheng, J. P. Chen, Y. Cheng, C. Zhang, Z. Wang, S. K. S. Yau, Z. H. Lin, L. Zhou, C. Ran, L. Xiao, and C. Wu, “Metagpt: Meta programming for multi-agent collaborative framework,” International Conference on Learning Representations , 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:260351380
2023
Earlier work this paper cites.
Z. Li, C. Wang, Z. Liu, H. Wang, D. Chen, S. Wang, and C. Gao, “CCTEST: testing and repairing code completion systems,” in 45th IEEE/ACM International Conference on Software Engineering, ICSE 2023, Melbourne, Australia, May 14-20, 2023 . IEEE, 2023, pp. 1238–1250
2023
Earlier work this paper cites.
A. Zou, Z. Wang, J. Z. Kolter, and M. Fredrikson, “Universal and transferable adversarial attacks on aligned language models,” 2023
2023
Earlier work this paper cites.
S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. Narasimhan, and Y. Cao, “React: Synergizing reasoning and acting in language models,” in International Conference on Learning Representations (ICLR) , 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Z. Li, C. Wang, P. Ma, C. Liu, S. Wang, D. Wu, and C. Gao, “On the feasibility of specialized ability stealing for large language code models,” 2023
2023
Cited alongside, same era.
Z. Li, C. Wang, S. Wang, and G. Cuiyun, “Protecting intellectual property of large language model-based code generation apis via watermarks,” in Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security, CCS 2023, Copenhagen, Denmark, November 26-30, 2023 , 2023
2023
Cited alongside, same era.
R. Wen, Z. Li, M. Backes, and Y. Zhang, “Membership inference attacks against in-context learning,” in Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security , ser. CCS ’24. New York, NY, USA: Association for Computing Machinery, 2024, p. 3481–3495. [Online]. Available: https://doi.org/10.1145/3658644.3690306
2024
Later among the works it cites.
J. Shi, Z. Yuan, Y. Liu, Y. Huang, P. Zhou, L. Sun, and N. Z. Gong, “Optimization-based prompt injection attack to llm-as-a-judge,” in Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security , ser. CCS ’24. New York, NY, USA: Association for Computing Machinery, 2024, p. 660–674. [Online]. Available: https://doi.org/10.1145/3658644.3690291
2024
Later among the works it cites.
2024
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2024
Cited alongside, same era.
2024
Cited alongside, same era.
Z. Zhang, Y. Zhang, L. Li, J. Shao, H. Gao, Y. Qiao, L. Wang, H. Lu, and F. Zhao, “Psysafe: A comprehensive framework for psychological-based attack, defense, and evaluation of multi-agent system safety,” in Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2024, Bangkok, Thailand, August 11-16, 2024 , L. Ku, A. Martins, and V. Srikumar, Eds. Association for Computational Linguistics, 2024, pp. 15 202–15 231. [Online]. Available: https://doi.org/10.18653/v1/2024.acl-long.812
2024
Cited alongside, same era.
2024
Cited alongside, same era.
2024
Cited alongside, same era.
2024
Cited alongside, same era.
B. Hui, H. Yuan, N. Gong, P. Burlina, and Y. Cao, “Pleak: Prompt leaking attacks against large language model applications,” in Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security, CCS 2024, Salt Lake City, UT, USA, October 14-18, 2024 , B. Luo, X. Liao, J. Xu, E. Kirda, and D. Lie, Eds. ACM, 2024, pp. 3600–3614. [Online]. Available: https://doi.org/10.1145/3658644.3670370
2024
Cited alongside, same era.
2024
Cited alongside, same era.
C. Zhang, J. X. Morris, and V. Shmatikov, “Extracting prompts by inverting LLM outputs,” in Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , Y. Al-Onaizan, M. Bansal, and Y.-N. Chen, Eds. Miami, Florida, USA: Association for Computational Linguistics, Nov. 2024, pp. 14 753–14 777. [Online]. Available: https://aclanthology.org/2024.emnlp-main.819/
2024
Later among the works it cites.
2024
Later among the works it cites.
Z. Li, C. Wang, P. Ma, D. Wu, S. Wang, C. Gao, and Y. Liu, “Split and merge: Aligning position biases in LLM-based evaluators,” in Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , Y. Al-Onaizan, M. Bansal, and Y.-N. Chen, Eds. Miami, Florida, USA: Association for Computational Linguistics, Nov. 2024
2024
Later among the works it cites.
2025
Closest in time.
Z. Ji, D. Wu, W. Jiang, P. Ma, Z. Li, and S. Wang, “Measuring and augmenting large language models for solving offensive security challenges,” in Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications Security, CCS 2025, Taipei, Taiwan, October 13-17, 2025 , 2025
2025
Closest in time.
Z. Ji, P. Ma, Z. Li, Z. Wang, and S. Wang, “Causality-aided evaluation and explanation of large language model-based code generation,” in Proceedings of the 34th ACM SIGSOFT International Symposium on Software Testing and Analysis , 2025
2025
Closest in time.
2025
Closest in time.
2025
Closest in time.
R. M. S. Khan, Z. Tan, S. Yun, C. Flemming, and T. Chen, “ Agents Under Siege
2025
Closest in time.
F. Jiang, Z. Xu, L. Niu, B. Y. Lin, and R. Poovendran, “Chatbug: A common vulnerability of aligned llms induced by chat templates,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 39, no. 26, 2025, pp. 27 347–27 355
2025
Closest in time.
M. Cemri, M. Z. Pan, S. Yang, L. A. Agrawal, B. Chopra, R. Tiwari, K. Keutzer, A. Parameswaran, D. Klein, K. Ramchandran, M. Zaharia, J. E. Gonzalez, and I. Stoica, “Why do multi-agent llm systems fail?” 2025. [Online]. Available: https://api.semanticscholar.org/CorpusID:277103715
2025
Closest in time.
C. Qian, Z. Xie, Y. Wang, W. Liu, K. Zhu, H. Xia, Y. Dang, Z. Du, W. Chen, C. Yang, Z. Liu, and M. Sun, “Scaling large language model-based multi-agent collaboration,” in The Thirteenth International Conference on Learning Representations , 2025. [Online]. Available: https://openreview.net/forum?id=K3n5jPkrU6
2025
Closest in time.
2025
Closest in time.
S. Chen, J. Piet, C. Sitawarin, and D. Wagner, “Struq: Defending against prompt injection with structured queries,” in USENIX Security Symposium , 2025
2025
Closest in time.
2025
Closest in time.
M. Andriushchenko, A. Souly, M. Dziemian, D. Duenas, M. Lin, J. Wang, D. Hendrycks, A. Zou, J. Z. Kolter, M. Fredrikson, Y. Gal, and X. Davies, “Agentharm: A benchmark for measuring harmfulness of LLM agents,” in The Thirteenth International Conference on Learning Representations , 2025. [Online]. Available: https://openreview.net/forum?id=AC5n7xHuR1
2025
Closest in time.
2025
Closest in time.
Z. Li, D. Wu, S. Wang, and S. Zhendong, “Differentiation-based extraction of proprietary data from fine-tuned llms,” in Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications Security, CCS 2025, Taipei, Taiwan, October 13-17, 2025 , 2025
2025
Closest in time.
Z. Li, D. Wu, S. Wang, and Z. Su, “Api-guided dataset synthesis to finetune large code models,” Proceedings of the ACM on Programming Languages , vol. 9, no. OOPSLA1, pp. 786–815, 2025
2025
Closest in time.
W. K. Wong, D. Wu, H. Wang, Z. Li, Z. Liu, S. Wang, Q. Tang, S. Nie, and S. Wu, “Decllm: Llm-augmented recompilable decompilation for enabling programmatic use of decompiled code,” in Proceedings of the 34th ACM SIGSOFT International Symposium on Software Testing and Analysis , 2025
2025
Closest in time.
C. Wang, J. Feng, S. Gao, C. Gao, Z. Li, T. Peng, H. Huang, Y. Deng, and M. Lyu, “Beyond peft: Layer-wise optimization for more effective and efficient large code model tuning,” in Proceedings of the 2025 ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , ser. ESEC/FSE ’25. ACM, 2025
2025
Closest in time.
Y. Liu, Z. Zhao, M. Backes, and Y. Zhang, “Membership inference attacks by exploiting loss trajectory,” in Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security , 2022, pp. 2085–2098
2098
Closest in time.