  The increasing use of large language models (LLMs) trained by third parties
raises significant security concerns. In particular, malicious actors can
introduce backdoors through poisoning attacks to generate undesirable outputs.
While such attacks have been extensively studied in image domains and
classification tasks, they remain underexplored for natural language generation
(NLG) tasks. To address this gap, we conduct an investigation of various
poisoning techniques targeting the LLM's fine-tuning phase via prefix-tuning, a
Parameter Efficient Fine-Tuning (PEFT) method. We assess their effectiveness
across two generative tasks: text summarization and text completion; and we
also introduce new metrics to quantify the success and stealthiness of such NLG
poisoning attacks. Through our experiments, we find that the prefix-tuning
hyperparameters and trigger designs are the most crucial factors to influence
attack success and stealthiness. Moreover, we demonstrate that existing popular
defenses are ineffective against our poisoning attacks. Our study presents the
first systematic approach to understanding poisoning attacks targeting NLG
tasks during fine-tuning via PEFT across a wide range of triggers and attack
settings. We hope our findings will aid the AI security community in developing
effective defenses against such threats.
