BadPrompt: Backdoor Attacks on Continuous Prompts (2211.14719v1)

Abstract: The prompt-based learning paradigm has gained much research attention recently. It has achieved state-of-the-art performance on several NLP tasks, especially in the few-shot scenarios. While steering the downstream tasks, few works have been reported to investigate the security problems of the prompt-based models. In this paper, we conduct the first study on the vulnerability of the continuous prompt learning algorithm to backdoor attacks. We observe that the few-shot scenarios have posed a great challenge to backdoor attacks on the prompt-based models, limiting the usability of existing NLP backdoor methods. To address this challenge, we propose BadPrompt, a lightweight and task-adaptive algorithm, to backdoor attack continuous prompts. Specially, BadPrompt first generates candidate triggers which are indicative for predicting the targeted label and dissimilar to the samples of the non-targeted labels. Then, it automatically selects the most effective and invisible trigger for each sample with an adaptive trigger optimization algorithm. We evaluate the performance of BadPrompt on five datasets and two continuous prompt models. The results exhibit the abilities of BadPrompt to effectively attack continuous prompts while maintaining high performance on the clean test sets, outperforming the baseline models by a large margin. The source code of BadPrompt is publicly available at https://github.com/papersPapers/BadPrompt.

Overview of "BadPrompt: Backdoor Attacks on Continuous Prompts"

In "BadPrompt: Backdoor Attacks on Continuous Prompts," the authors present the first exploration into the susceptibility of continuous prompt-based models to backdoor attacks. Prompt-based learning has recently garnered substantial interest for its performance, particularly in few-shot learning scenarios. However, prior to this paper, the security of these models has not been rigorously analyzed. The paper introduces BadPrompt, a novel lightweight and adaptive backdoor attack tailored to continuous prompt models, addressing the limitations observed in existing NLP backdoor techniques within these few-shot settings.

Prompt-Based Learning and Its Vulnerabilities

Prompt-based learning optimizes pre-trained LLMs (PLMs) by reformatting downstream tasks through the addition of prompts, which guides the LLM to generate or complete tasks as closely as possible to its training. This paradigm has surpassed traditional fine-tuning methods in several tasks. Despite its promising applications, prompt-based learning models, particularly those employing continuous prompts, are vulnerable due to their intrinsic reliance on this artificial steering mechanism.

The researchers identify that existing backdoor methods either fail or degrade model performance under the few-shot constraint — a condition often leveraged in prompt-based learning. Therefore, this paper is crucial in addressing a potential threat that has not previously been fully considered.

Methodology: Introducing BadPrompt

The paper systematically introduces BadPrompt, consisting of two key components: trigger candidate generation and adaptive trigger optimization.

1. Trigger Candidate Generation: This component involves selecting trigger words that are likely to influence the model to predict a specific targeted label while being dissimilar to other classes. This selection process ensures that the backdoor triggers affect only the intended output without disrupting normal model functionality.
2. Adaptive Trigger Optimization: This adaptive method individualizes trigger selection for each input to enhance effectiveness and invisibility while maintaining general model accuracy. The process involves an innovative use of the Gumbel Softmax technique to approximate the sampling of discrete triggers, enabling differentiable optimization.

Evaluation and Results

The researchers rigorously evaluated BadPrompt using five datasets and two continuous prompt models — P-tuning and DART. The experimental results, as presented by the authors, demonstrated that BadPrompt consistently outperforms other backdoor methods, achieving high attack success rates without significantly lowering accuracy on clean test sets. Specifically, a notable balance between attack potency and model integrity is achieved, indicating a substantial advancement over prior methods, which tend to compromise on one for the other.

These results affirm BadPrompt's capability to effectively implant backdoors while maintaining a high degree of model fidelity in few-shot learning scenarios. This balance is crucial as it suggests BadPrompt’s potential threat to practical applications of prompt-based models if misused.

Implications and Future Directions

This paper has significant implications for both the theoretical understanding and practical deployment of prompt-based models. From a security perspective, BadPrompt reveals a critical vulnerability that practitioners should consider when using third-party models, emphasizing the importance of developing robust backdoor defenses tailored to prompt-based models.

Future research directions may include expanding the attack framework to other PLMs and extending the analysis beyond classification tasks to encompass more complex models and use cases. Moreover, exploring defensive mechanisms specifically for prompt-based models will be invaluable, potentially drawing on techniques such as fine-pruning or knowledge distillation to remove backdoors effectively.

Conclusion

"BadPrompt: Backdoor Attacks on Continuous Prompts" pioneers a critical examination of security in the domain of prompt-based learning, offering a nuanced understanding of how backdoor vulnerabilities can manifest in such systems. The breakthroughs in adaptive trigger optimization present a sophisticated avenue for attacking models discreetly, prompting a simultaneous need to advance defensive strategies. This research underscores the evolving landscape of AI security, calling for heightened awareness and preventive measures within the community.