Unleashing Worms and Extracting Data: Escalating the Outcome of Attacks against RAG-based Inference in Scale and Severity Using Jailbreaking

J. F. Shoch and J. A. Hupp, “The “worm” programs—early experience with a distributed computation,” Communications of the ACM

1982

Earlier work this paper cites.

M. Bishop, “Analysis of the iloveyou worm,” Internet: http://nob. cs. ucdavis. edu/classes/ecs155-2005-04/handouts/iloveyou. pdf

2000

Earlier work this paper cites.

D. M. Kienzle and M. C. Elder, “Recent worms: a survey and trends,” in Proceedings of the 2003 ACM workshop on Rapid Malcode

2003

Earlier work this paper cites.

N. Weaver, V. Paxson, S. Staniford, and R. Cunningham, “A taxonomy of computer worms,” in Proceedings of the 2003 ACM workshop on Rapid Malcode

2003

Earlier work this paper cites.

H. Orman, “The morris worm: A fifteen-year perspective,” IEEE Security & Privacy

2003

Earlier work this paper cites.

A. F. C. F. M. GA, “Iloveyou virus lessons learned report,” 2003

2003

Earlier work this paper cites.

B. Klimt and Y. Yang, “The enron corpus: A new dataset for email classification research,” in European conference on machine learning

2004

Earlier work this paper cites.

C. Smith, A. Matrawy, S. Chow, and B. Abdelaziz, “Computer worms: Architectures, evasion strategies, and detection mechanisms,” Journal of Information Assurance and Security

2009

Earlier work this paper cites.

A. Matrosov, E. Rodionov, D. Harley, and J. Malcho, “Stuxnet under the microscope,” ESET LLC (September 2010)

2010

Earlier work this paper cites.

C. Kelty, “The morris worm,” Limn

2011

Earlier work this paper cites.

N. Falliere, L. O. Murchu, and E. Chien, “W32. stuxnet dossier,” White paper, symantec corp., security response

2011

Earlier work this paper cites.

D. Kushner, “The real story of stuxnet,” ieee Spectrum

2013

Earlier work this paper cites.

M. Fredrikson, S. Jha, and T. Ristenpart, “Model inversion attacks that exploit confidence information and basic countermeasures,” in Proceedings of the 22nd ACM SIGSAC conference on computer and communications security

2015

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)

2017

Earlier work this paper cites.

M. Antonakakis, T. April, M. Bailey, M. Bernhard, E. Bursztein, J. Cochran, Z. Durumeric, J. A. Halderman, L. Invernizzi, M. Kallitsis, et al

2017

Earlier work this paper cites.

Q. Chen and R. A. Bridges, “Automated behavioral analysis of malware: A case study of wannacry ransomware,” in 2017 16th IEEE International Conference on machine learning and applications (ICMLA)

2017

Earlier work this paper cites.

D.-Y. Kao and S.-C. Hsiao, “The dynamic analysis of wannacry ransomware,” in 2018 20th International conference on advanced communication technology (ICACT)

2018

Earlier work this paper cites.

S.-C. Hsiao and D.-Y. Kao, “The static analysis of wannacry ransomware,” in 2018 20th international conference on advanced communication technology (ICACT)

2018

Cited alongside, same era.

M. Akbanov, V. G. Vassilakis, and M. D. Logothetis, “Wannacry ransomware: Analysis of infection, persistence, recovery prevention and propagation mechanisms,” Journal of Telecommunications and Information Technology

2019

Cited alongside, same era.

P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W.-t. Yih, T. Rocktäschel, et al

2020

Cited alongside, same era.

K. Song, X. Tan, T. Qin, J. Lu, and T.-Y. Liu, “Mpnet: Masked and permuted pre-training for language understanding,” arXiv preprint arXiv:2004.09297

Original

2020

Cited alongside, same era.

N. Carlini, F. Tramer, E. Wallace, M. Jagielski, A. Herbert-Voss, K. Lee, A. Roberts, T. Brown, D. Song, U. Erlingsson, et al

Y. Li, G. Liu, Y. Yang, and C. Wang, “Seeing is believing: Black-box membership inference attacks against retrieval augmented generation,” arXiv preprint arXiv:2406.19234

Original

2024

Closest in time.

S. Zeng, J. Zhang, P. He, Y. Xing, Y. Liu, H. Xu, J. Ren, S. Wang, D. Yin, Y. Chang, et al

Original

2024

Closest in time.

J. Xue, M. Zheng, Y. Hu, F. Liu, X. Chen, and Q. Lou, “Badrag: Identifying vulnerabilities in retrieval augmented generation of large language models,” arXiv preprint arXiv:2406.00083

Original

2024

Closest in time.

P. Cheng, Y. Ding, T. Ju, Z. Wu, W. Du, P. Yi, Z. Zhang, and G. Liu, “Trojanrag: Retrieval-augmented generation can be backdoor driver in large language models,” arXiv preprint arXiv:2405.13401

Original

2024

Closest in time.

W. Zou, R. Geng, B. Wang, and J. Jia, “Poisonedrag: Knowledge poisoning attacks to retrieval-augmented generation of large language models,” arXiv preprint arXiv:2402.07867