Fetching the paper…
Reading the bibliography…
Although it has been demonstrated that Natural Language Processing (NLP) algorithms are vulnerable to deliberate attacks, the question of whether such weaknesses can lead to software security threats is under-explored.
E. F. Codd, “A relational model of data for large shared data banks,” Commun. ACM , vol. 13, no. 6, p. 377–387, jun 1970
1970
Earlier work this paper cites.
J. Saltzer and M. Schroeder, “The protection of information in computer systems,” Proceedings of the IEEE , vol. 63, no. 9, pp. 1278–1308, 1975
1975
Earlier work this paper cites.
C. T. Hemphill, J. J. Godfrey, and G. R. Doddington, “The ATIS spoken language systems pilot corpus,” in Speech and Natural Language: Proceedings of Workshop, June 24-27,1990 . Hidden Valley, Pennsylvania: ACL, 1990, p. 96–101
1990
Earlier work this paper cites.
L. Kohnfelder and P. Garg, “The threats to our products,” Microsoft Interface, Microsoft Corporation , vol. 33, 1999
1999
Earlier work this paper cites.
N. Boucher, I. Shumailov, R. Anderson, and N. Papernot, “Bad Characters: Imperceptible NLP Attacks,” in S&P 2022 . San Francisco, CA, USA: IEEE, 2022, pp. 1987–2004
2004
Earlier work this paper cites.
N. Bertomeu, H. Uszkoreit, A. Frank, H.-U. Krieger, and B. Jörg, “Contextual phenomena and thematic relations in database QA dialogues: results from a Wizard-of-Oz experiment,” in Proceedings of the Interactive Question Answering Workshop at HLT-NAACL 2006 . New York, NY, USA: ACL, Jun. 2006, pp. 1–8
2006
Earlier work this paper cites.
S. Thomas, L. Williams, and T. Xie, “On automated prepared statement generation to remove sql injection vulnerabilities,” Information and Software Technology , vol. 51, no. 3, pp. 589–598, 2009
2009
Earlier work this paper cites.
J. Bau, E. Bursztein, D. Gupta, and J. Mitchell, “State of the art: Automated black-box web application vulnerability testing,” in 2010 IEEE symposium on security and privacy . Oakland, CA, USA: IEEE, 2010, pp. 332–345
2010
Earlier work this paper cites.
A. Sadeghian, M. Zamani, and S. Ibrahim, “Sql injection is still alive: A study on sql injection signature evasion techniques,” in ICICM2013 . Kuala Lumpur, Malaysia: IEEE, 2013, pp. 265–268
2013
Earlier work this paper cites.
A. Sadeghian, M. Zamani, and S. M. Abdullah, “A taxonomy of sql injection attacks,” in ICICM2013 . Kuala Lumpur, Malaysia: IEEE, 2013, pp. 269–273
2013
Earlier work this paper cites.
C. Sharma and S. Jain, “Analysis and classification of sql injection vulnerabilities and attacks on web applications,” in ICAETR-2014 . Unnao, India: IEEE, 2014, pp. 1–6
2014
Earlier work this paper cites.
F. Li and H. V. Jagadish, “Constructing an interactive natural language interface for relational databases,” Proc. VLDB Endow. , vol. 8, no. 1, p. 73–84, sep 2014
2014
Earlier work this paper cites.
M. Monshizadeh, P. Naldurg, and V. N. Venkatakrishnan, “Mace: Detecting privilege escalation vulnerabilities in web applications,” in Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security , ser. CCS ’14. New York, NY, USA: Association for Computing Machinery, 2014, p. 690–701
2014
Earlier work this paper cites.
Y. Zhou and D. Evans, “SSOScan: Automated testing of web applications for single Sign-On vulnerabilities,” in 23rd USENIX Security Symposium (USENIX Security 14) . San Diego, CA: USENIX Association, Aug. 2014, pp. 495–510
2014
Earlier work this paper cites.
M. Sharif, S. Bhagavatula, L. Bauer, and M. K. Reiter, “Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition,” in CCS 2016 , ser. CCS ’16. New York, NY, USA: Association for Computing Machinery, 2016, p. 1528–1540
2016
Earlier work this paper cites.
N. Singh, M. Dayal, R. S. Raw, and S. Kumar, “Sql injection: Types, methodology, attack queries and prevention,” in INDIACom2016 . New Delhi, India: IEEE, 2016, pp. 2872–2876
2016
Earlier work this paper cites.
P. Yin and G. Neubig, “A syntactic neural model for general-purpose code generation,” in Proceedings of the 55th Annual Meeting of the ACL (Volume 1: Long Papers) . Vancouver, Canada: ACL, Jul. 2017, pp. 440–450
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
S. Gupta, R. Shah, M. Mohit, A. Kumar, and M. Lewis, “Semantic parsing for task oriented dialog using hierarchical representations,” in EMNLP 2018 . Brussels, Belgium: ACL, Oct.-Nov. 2018, pp. 2787–2792
2018
Earlier work this paper cites.
D. Yoon, D. Lee, and S. Lee, “Dynamic self-attention : Computing attention over words dynamically for sentence embedding,” 2018
2018
Earlier work this paper cites.
T. Yu, M. Yasunaga, K. Yang, R. Zhang, D. Wang, Z. Li, and D. Radev, “SyntaxSQLNet: Syntax tree networks for complex and cross-domain text-to-SQL task,” in EMNLP 2018 . Brussels, Belgium: ACL, Oct.-Nov. 2018, pp. 1653–1663
2018
Earlier work this paper cites.
J. Navarro, A. Deruyver, and P. Parrend, “A systematic survey on multi-step attack detection,” Computers and Security , vol. 76, pp. 214–249, 2018
2018
Earlier work this paper cites.
T. Yu, R. Zhang, K. Yang, M. Yasunaga, D. Wang, Z. Li, J. Ma, I. Li, Q. Yao, S. Roman, Z. Zhang, and D. Radev, “Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-SQL task,” in EMNLP 2018 . Brussels, Belgium: ACL, Oct.-Nov. 2018, pp. 3911–3921
2018
Earlier work this paper cites.
L. Dong and M. Lapata, “Coarse-to-fine decoding for neural semantic parsing,” in Proceedings of the 56th Annual Meeting of the ACL (Volume 1: Long Papers) . Melbourne, Australia: ACL, Jul. 2018, pp. 731–742
2018
Earlier work this paper cites.
L. Ma, D. Zhao, Y. Gao, and C. Zhao, “Research on sql injection attack and prevention technology based on web,” in ICCNEA2019 . Xi’an, China: IEEE, 2019, pp. 176–179
2019
Earlier work this paper cites.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) . Minneapolis, Minnesota: ACL, Jun. 2019, pp. 4171–4186
2019
Cited alongside, same era.
J. Guo, Z. Zhan, Y. Gao, Y. Xiao, J.-G. Lou, T. Liu, and D. Zhang, “Towards complex text-to-SQL in cross-domain database with intermediate representation,” in Proceedings of the 57th Annual Meeting of the ACL . Florence, Italy: ACL, Jul. 2019, pp. 4524–4535
2019
Cited alongside, same era.
W. Hwang, J. Yim, S. Park, and M. Seo, “A comprehensive exploration on wikisql with table-aware word contextualization,” 2019
2019
Cited alongside, same era.
D. Pruthi, B. Dhingra, and Z. C. Lipton, “Combating adversarial misspellings with robust word recognition,” in Proceedings of the 57th Annual Meeting of the ACL . Florence, Italy: ACL, Jul. 2019, pp. 5582–5591
N. Carlini, F. Tramèr, E. Wallace, M. Jagielski, A. Herbert-Voss, K. Lee, A. Roberts, T. Brown, D. Song, Ú. Erlingsson, A. Oprea, and C. Raffel, “Extracting training data from large language models,” in USENIX Security 21 . Virtual-only Conference: USENIX Association, Aug. 2021, pp. 2633–2650
2021
Later among the works it cites.
T. Scholak, N. Schucher, and D. Bahdanau, “PICARD: Parsing incrementally for constrained auto-regressive decoding from language models,” in EMNLP2021 . Punta Cana, Dominican Republic: ACL, Nov. 2021, pp. 9895–9901
2021
Later among the works it cites.
F. Qi, Y. Chen, M. Li, Y. Yao, Z. Liu, and M. Sun, “ONION: A simple and effective defense against textual backdoor attacks,” in EMNLP 2021 . Online and Punta Cana, Dominican Republic: ACL, Nov. 2021, pp. 9558–9566
2021
Later among the works it cites.
Z. J. Wang, D. Choi, S. Xu, and D. Yang, “Putting humans in the natural language processing loop: A survey,” in Proceedings of the First Workshop on Bridging Human–Computer Interaction and Natural Language Processing . Online: ACL, Apr. 2021, pp. 47–52
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2019
Cited alongside, same era.
Ștefan Nicula and R. D. Zota, “Exploiting stack-based buffer overflow using modern day techniques,” Procedia Computer Science , vol. 160, pp. 9–14, 2019, the 10th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN-2019) / The 9th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH-2019) / Affiliated Workshops
2019
Cited alongside, same era.
Y. Chen, X. Yuan, J. Zhang, Y. Zhao, S. Zhang, K. Chen, and X. Wang, “Devil’s whisper: A general approach for physical adversarial attacks against commercial black-box speech recognition devices,” in Security 2020 . Virtual-only Conference: USENIX Association, Aug. 2020, pp. 2667–2684
2020
Cited alongside, same era.
F. Borges, G. Balikas, M. Brette, G. Kempf, A. Srikantan, M. Landos, D. Brazouskaya, and Q. Shi, “Query understanding for natural language enterprise search,” 2020
2020
Cited alongside, same era.
P. Wang, T. Shi, and C. K. Reddy, “Text-to-sql generation for question answering on electronic medical records,” in WWW 2020 , ser. WWW ’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 350–361
2020
Cited alongside, same era.
Z. M. Smith, E. Lostri, and J. A. Lewis, “The hidden costs of cybercrime,” 2020, mcafee. [Online]. Available: https://www.mcafee.com/enterprise/en-us/assets/reports/rp-hidden-costs-of-cybercrime.pdf
2020
Cited alongside, same era.
M. Lewis, Y. Liu, N. Goyal, M. Ghazvininejad, A. Mohamed, O. Levy, V. Stoyanov, and L. Zettlemoyer, “BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension,” in Proceedings of the 58th Annual Meeting of the ACL . Online: ACL, Jul. 2020, pp. 7871–7880
2020
Cited alongside, same era.
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” Journal of Machine Learning Research , vol. 21, no. 140, pp. 1–67, 2020
2020
Cited alongside, same era.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020
2020
Cited alongside, same era.
2021
Later among the works it cites.
M. Joseph, H. Raj, A. Yadav, and A. Sharma, “Askyourdb: An end-to-end system for querying and visualizing relational databases using natural language,” 2022
2022
Closest in time.
K. Sun, X. Luo, and M. Y. Luo, “A survey of pretrained language models,” in Knowledge Science, Engineering and Management: 15th International Conference, KSEM 2022, Singapore, August 6–8, 2022, Proceedings, Part II . Berlin, Heidelberg: Springer-Verlag, 2022, p. 442–456
2022
Closest in time.
J. Yang, L. Zhang, and D. Yang, “SUBS: Subtree substitution for compositional semantic parsing,” in Proceedings of the 2022 Conference of the North American Chapter of the ACL: Human Language Technologies . Seattle, United States: ACL, Jul. 2022, pp. 169–174
2022
Closest in time.
B. Qin, B. Hui, L. Wang, M. Yang, J. Li, B. Li, R. Geng, R. Cao, J. Sun, L. Si, F. Huang, and Y. Li, “A survey on text-to-sql parsing: Concepts, methods, and future directions,” 2022
2022
Closest in time.
X. Pi, B. Wang, Y. Gao, J. Guo, Z. Li, and J.-G. Lou, “Towards robustness of text-to-SQL models against natural and realistic adversarial table perturbation,” in Proceedings of the 60th Annual Meeting of the ACL (Volume 1: Long Papers) . Dublin, Ireland: ACL, May 2022, pp. 2007–2022
2022
Closest in time.
N. Nguyen and S. Nadi, “An empirical evaluation of github copilot’s code suggestions,” in MSR 2022 , ser. MSR ’22. New York, NY, USA: Association for Computing Machinery, 2022, p. 1–5
2022
Closest in time.
H. Vasconcelos, G. Bansal, A. Fourney, Q. V. Liao, and J. W. Vaughan, “Generation probabilities are not enough: Improving error highlighting for ai code suggestions,” in HCAI Workshop at NeurIPS . Virtual-only Conference: NeurIPS, 2022
2022
Closest in time.
H. Pearce, B. Ahmad, B. Tan, B. Dolan-Gavitt, and R. Karri, “Asleep at the keyboard? assessing the security of github copilot’s code contributions,” in S&P 2022 . San Francisco, CA, USA: IEEE, 2022, pp. 754–768
2022
Closest in time.
Y. Chen, H. Gao, G. Cui, F. Qi, L. Huang, Z. Liu, and M. Sun, “Why should adversarial perturbations be imperceptible? rethink the research paradigm in adversarial nlp,” in EMNLP 2021 . Abu Dhabi, United Arab Emirates: ACL, 2022, p. 11222–11237
2022
Closest in time.
T. Le, N. Park, and D. Lee, “SHIELD: Defending textual neural networks against multiple black-box adversarial attacks with stochastic multi-expert patcher,” in Proceedings of the 60th Annual Meeting of the ACL (Volume 1: Long Papers) . Dublin, Ireland: ACL, May 2022, pp. 6661–6674
2022
Closest in time.
B. Zhu, Y. Qin, F. Qi, Y. Deng, Z. Liu, M. Sun, and M. Gu, “Pass off fish eyes for pearls: Attacking model selection of pre-trained models,” in Proceedings of the 60th Annual Meeting of the ACL (Volume 1: Long Papers) . Dublin, Ireland: ACL, May 2022, pp. 5060–5072
2022
Closest in time.
IBM, “Cost of a data breach 2022: A million-dollar race to detect and respond,” 2022
2022
Closest in time.
Cox Blue, “12 ddos statistics that should concern business leaders,” 2022. [Online]. Available: https://www.coxblue.com/12-ddos-statistics-that-should-concern-business-leaders/
2022
Closest in time.
M. Tänzer, S. Ruder, and M. Rei, “Memorisation versus generalisation in pre-trained language models,” in Proceedings of the 60th Annual Meeting of ACL (Volume 1: Long Papers) . Dublin, Ireland: ACL, May 2022, pp. 7564–7578
2022
Closest in time.
W. Du, Y. Zhao, B. Li, G. Liu, and S. Wang, “Ppt: Backdoor attacks on pre-trained models via poisoned prompt tuning,” in IJCAI 2022 , L. D. Raedt, Ed. Messe Wien, Vienna, Austria: International Joint Conferences on Artificial Intelligence Organization, 7 2022, pp. 680–686, main Track
2022
Closest in time.
X. Pan, M. Zhang, B. Sheng, J. Zhu, and M. Yang, “Hidden trigger backdoor attack on { \{ NLP } \} models via linguistic style manipulation,” in USENIX Security 2022 . BOSTON, MA, USA: USENIX, 2022, pp. 3611–3628
2022
Closest in time.
T. Xie, C. H. Wu, P. Shi, R. Zhong, T. Scholak, M. Yasunaga, C.-S. Wu, M. Zhong, P. Yin, S. I. Wang, V. Zhong, B. Wang, C. Li, C. Boyle, A. Ni, Z. Yao, D. Radev, C. Xiong, L. Kong, R. Zhang, N. A. Smith, L. Zettlemoyer, and T. Yu, “Unifiedskg: Unifying and multi-tasking structured knowledge grounding with text-to-text language models,” in EMNLP2022 . Abu Dhabi: EMNLP, 2022
2022
Closest in time.
J. Yang, H. Jiang, Q. Yin, D. Zhang, B. Yin, and D. Yang, “SEQZERO: Few-shot compositional semantic parsing with sequential prompts and zero-shot models,” in Findings of the ACL: NAACL 2022 . Seattle, United States: ACL, Jul. 2022, pp. 49–60
2022
Closest in time.
P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig, “Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing,” ACM Comput. Surv. , vol. 55, no. 9, jan 2023
2023
Closest in time.
E. Trickel, F. Pagani, C. Zhu, L. Dresel, G. Vigna, C. Kruegel, R. Wang, T. Bao, Y. Shoshitaishvili, and A. Doupe, “Toss a fault to your witcher: Applying grey-box coverage-guided mutational fuzzing to detect sql and command injection vulnerabilities,” in IEEE Symposium on Security and Privacy (SP), to appear . San Francisco, CA, US: IEEE Computer Society, 2023, pp. 116–133
2023
Closest in time.