paperSeptember 2024Unreviewed

Recent Advances in Attack and Defense Approaches of Large Language Models

Jing Cui, Yishi Xu, Zhewei Huang, Shuchang Zhou, Jianbin Jiao, Junge Zhang

arXiv.org

Abstract

Large Language Models (LLMs) have revolutionized artificial intelligence and machine learning through their advanced text processing and generating capabilities. However, their widespread deployment has raised significant safety and reliability concerns. Established vulnerabilities in deep neural networks, coupled with emerging threat models, may compromise security evaluations and create a false sense of security. Given the extensive research in the field of LLM security, we believe that summar

Cite

@misc{cui2024recent,
  title = {{Recent Advances in Attack and Defense Approaches of Large Language Models}},
  author = {Jing Cui and Yishi Xu and Zhewei Huang and Shuchang Zhou and Jianbin Jiao and Junge Zhang},
  year = {2024},
  month = sep,
  eprint = {2409.03274},
  archivePrefix = {arXiv},
  doi = {10.48550/arXiv.2409.03274},
  url = {https://www.semanticscholar.org/paper/3d7cc47f10a1b55e3c7af24bf43f7f9206fcda4e}
}