Fetching the paper…
Reading the bibliography…
Prompt leakage poses a compelling security and privacy threat in LLM applications.
Pubmedqa: A dataset for biomedical research question answering
Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William W Cohen, and Xinghua Lu. 2019 · 1909
Earlier work this paper cites.
Extracting training data from large language models
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, Alina Oprea, and Colin Raffel. 2021 · 2012
Earlier work this paper cites.
Bioasq: A challenge on large-scale biomedical semantic indexing and question answering
Georgios Balikas, Anastasia Krithara, Ioannis Partalas, and George Paliouras. 2015 · 2015
Earlier work this paper cites.
Billsum: A corpus for automatic summarization of us legislation
Anastassia Kornilova and Vladimir Eidelman. 2019 · 2019
Earlier work this paper cites.
Ignore previous prompt: Attack techniques for language models
Fábio Perez and Ian Ribeiro. 2022 · 2022
Earlier work this paper cites.
Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, Christoph Endres, Thorsten Holz, and Mario Fritz. 2023 · 2023
Earlier work this paper cites.
Preventing generation of verbatim memorization in language models gives a false sense of privacy
Daphne Ippolito, Florian Tramer, Milad Nasr, Chiyuan Zhang, Matthew Jagielski, Katherine Lee, Christopher Choquette Choo, and Nicholas Carlini. 2023 · 2023
Earlier work this paper cites.
Baseline defenses for adversarial attacks against aligned language models
Neel Jain, Avi Schwarzschild, Yuxin Wen, Gowthami Somepalli, John Kirchenbauer, Ping-yeh Chiang, Micah Goldblum, Aniruddha Saha, Jonas Geiping, and Tom Goldstein. 2023 · 2023
Earlier work this paper cites.
Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. 2023 · 2023
Earlier work this paper cites.
Are you sure? challenging llms leads to performance drops in the flipflop experiment
Philippe Laban, Lidiya Murakhovs’ ka, Caiming Xiong, and Chien-Sheng Wu. 2023 · 2023
Earlier work this paper cites.
Prompt injection attack against llm-integrated applications
Yi Liu, Gelei Deng, Yuekang Li, Kailong Wang, Tianwei Zhang, Yepang Liu, Haoyu Wang, Yan Zheng, and Yang Liu. 2023 · 2023
Earlier work this paper cites.
Query rewriting for retrieval-augmented large language models
Xinbei Ma, Yeyun Gong, Pengcheng He, Hai Zhao, and Nan Duan. 2023 · 2023
Cited alongside, same era.
OpenAI. 2023 · 2023
Cited alongside, same era.
Towards understanding sycophancy in language models
Mrinank Sharma, Meg Tong, Tomasz Korbak, David Kristjanson Duvenaud, Amanda Askell, Samuel R. Bowman, Newton Cheng, Esin Durmus, Zac Hatfield-Dodds, Scott Johnston, Shauna Kravec, Tim Maxwell, Sam McCandlish, Kamal Ndousse, Oliver Rausch, Nicholas Schiefer, Da Yan, Miranda Zhang, and Ethan Perez. 2023 · 2023
Cited alongside, same era.
Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al. 2023 · 2023
Cited alongside, same era.
Coercing llms to do and reveal (almost) anything
Jonas Geiping, Alex Stein, Manli Shu, Khalid Saifullah, Yuxin Wen, and Tom Goldstein. 2024 · 2024
Closest in time.
Pleak: Prompt leaking attacks against large language model applications
Bo Hui, Haolin Yuan, Neil Gong, Philippe Burlina, and Yinzhi Cao. 2024 · 2024
Closest in time.
Albert Q Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, et al. 2024 · 2024
Closest in time.
Query rewriting via large language models
Jie Liu and Barzan Mozafari. 2024 · 2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023 · 2023
Cited alongside, same era.
Neeraj Varshney, Pavel Dolin, Agastya Seth, and Chitta Baral. 2023 · 2023
Cited alongside, same era.
Benchmarking and defending against indirect prompt injection attacks on large language models
Jingwei Yi, Yueqi Xie, Bin Zhu, Keegan Hines, Emre Kiciman, Guangzhong Sun, Xing Xie, and Fangzhao Wu. 2023 · 2023
Cited alongside, same era.
Defending large language models against jailbreaking attacks through goal prioritization
Zhexin Zhang, Junxiao Yang, Pei Ke, and Minlie Huang. 2023 · 2023
Cited alongside, same era.
Autodan: Interpretable gradient-based adversarial attacks on large language models
Sicheng Zhu, Ruiyi Zhang, Bang An, Gang Wu, Joe Barrow, Zichao Wang, Furong Huang, Ani Nenkova, and Tong Sun. 2023 · 2023
Cited alongside, same era.
Phi-3 technical report: A highly capable language model locally on your phone
Marah Abdin, Sam Ade Jacobs, Ammar Ahmad Awan, Jyoti Aneja, Ahmed Awadallah, Hany Awadalla, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Harkirat Behl, et al. 2024 · 2024
Cited alongside, same era.
Fnspid: A comprehensive financial news dataset in time series
Zihan Dong, Xinyu Fan, and Zhiyuan Peng. 2024 · 2024
Cited alongside, same era.
Effective prompt extraction from language models
Yiming Zhang, Nicholas Carlini, and Daphne Ippolito. 2024a
Cited in the paper.
Zhenting Qi, Hanlin Zhang, Eric Xing, Sham Kakade, and Himabindu Lakkaraju. 2024 · 2024
Closest in time.
Prompt stealing attacks against large language models
Zeyang Sha and Yang Zhang. 2024 · 2024
Closest in time.
A new era in llm security: Exploring security concerns in real-world llm-based systems
Fangzhou Wu, Ning Zhang, Somesh Jha, Patrick McDaniel, and Chaowei Xiao. 2024 · 2024
Closest in time.
Llm jailbreak attack versus defense techniques – a comprehensive study
Zihao Xu, Yi Liu, Gelei Deng, Yuekang Li, and Stjepan Picek. 2024 · 2024
Closest in time.
Prsa: Prompt reverse stealing attacks against large language models
Yong Yang, Xuhong Zhang, Yi Jiang, Xi Chen, Haoyu Wang, Shouling Ji, and Zonghui Wang. 2024 · 2024
Closest in time.
The good and the bad: Exploring privacy issues in retrieval-augmented generation (rag)
Shenglai Zeng, Jiankun Zhang, Pengfei He, Yue Xing, Yiding Liu, Han Xu, Jie Ren, Shuaiqiang Wang, Dawei Yin, Yi Chang, and Jiliang Tang. 2024 · 2024
Closest in time.
Injecagent: Benchmarking indirect prompt injections in tool-integrated large language model agents
Qiusi Zhan, Zhixiang Liang, Zifan Ying, and Daniel Kang. 2024 · 2024
Closest in time.