Forcing Generative Models to Degenerate Ones: The Power of Data
  Poisoning Attacks
Shuli Jiang Swanand Ravindra Kadhe Yi Zhou Ling Cai Nathalie Baracaldo
Growing applications of large language models (LLMs) trained by a third party
raise serious concerns on the security vulnerability of LLMs.It has been
demonstrated that malicious actors can covertly exploit these vulnerabilities
in LLMs through poisoning attacks aimed at generating undesirable outputs.
While poisoning attacks have received significant attention in the image domain
(e.g., object detection), and classification tasks, their implications for
generative models, particularly in the realm of natural language generation
(NLG) tasks, remain poorly understood. To bridge this gap, we perform a
comprehensive exploration of various poisoning techniques to assess their
effectiveness across a range of generative tasks. Furthermore, we introduce a
range of metrics designed to quantify the success and stealthiness of poisoning
attacks specifically tailored to NLG tasks. Through extensive experiments on
multiple NLG tasks, LLMs and datasets, we show that it is possible to
successfully poison an LLM during the fine-tuning stage using as little as 1\%
of the total tuning data samples. Our paper presents the first systematic
approach to comprehend poisoning attacks targeting NLG tasks considering a wide
range of triggers and attack settings. We hope our findings will assist the AI
security community in devising appropriate defenses against such threats.