Defending Pre-trained Language Models as Few-shot Learners against Backdoor Attacks

Summary

This paper introduces a novel approach to address the vulnerability of pre-trained language models (PLMs) utilized as few-shot learners in the face of backdoor attacks. The proposed defense mechanism, called MDP (masking-differential prompting), exploits the sensitivity of sentence masking to effectively differentiate between triggered and non-triggered sentences. Experimental results demonstrate the exceptional performance of MDP across various benchmark datasets.

Strengths

1. This paper addresses a crucial security concern regarding using language models for few-shot learning. 2. The paper presents a comprehensive experimental analysis conducted on multiple benchmark datasets, comparing the proposed method with various baseline approaches. 3. The proposed method is not only intuitive and intriguing but also lightweight, making it suitable for implementation on large-scale language models.

Weaknesses

1. To enhance the clarity of the proposed method, it is strongly recommended that the authors include a formal algorithm in their paper. Additionally, it would be beneficial if the authors move key parameters, such as the poisoning rate, number of triggers, and mask rate of MDP, from the appendix to the main body of the paper for easier reference. 2. Several crucial ablation studies should be conducted to validate the method. These studies should involve varying the poisoning rate, number of triggers, mask rate of MDP, and exploring different model architectures (such as Bert variants). 3. Ths paper should consider adaptive attacks in evaluating MDP. For instance, exploring the possibility of designing backdoors that are mask-invariant, thereby evading detection. 4. It is essential to clarify whether MDP can correctly classify all test data as clean when the model is not poisoned. 5. The current readme does not contain the instruction to run.

Questions

NA

Rating

5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.

Confidence

4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.

Soundness

3 good

Presentation

2 fair

Contribution

2 fair

Limitations

NA

Summary

This work conducts a pilot study showing that PLMs as few-shot learners are highly vulnerable to backdoor attacks while existing defenses are inadequate due to the unique challenges of few-shot scenarios. To address such challenges, the authors advocate MDP, a novel lightweight, pluggable, and effective defense for PLMs as few-shot learners. Specifically, MDP leverages the gap between the masking sensitivity of poisoned and clean samples: with reference to the limited few-shot data as distributional anchors, it compares the representations of given samples under varying masking and identifies poisoned samples as ones with significant variations. The authors show analytically that MDP creates an interesting dilemma for the attacker to choose between attack effectiveness and detection evasiveness. The empirical evaluation using benchmark datasets and representative attacks validates the efficacy of MDP.

Strengths

1. This paper considers an important research problem, the defense against backdoor attacks in the few-shot settings. Previous research demonstrates the vulnerability of few-shot prompt-based learning, but research on the defense side is still lacking. This work fills this gap and proposes a concrete approach for defense. 2. The approach proposed in this work is well-motivated and theoretic-grounded. The basic intuition/observation is: Compared with clean samples, poisoned samples often show higher sensitivity to random masking. This motivation is reasonable and has been effectively utilized in the method development. In addition, theoretical justification for the effectiveness of MDP has been provided. 3. Comprehensive experiments have been conducted to prove the effectiveness of the proposed approach. MDP achieves significantly better results compared to other baseline methods regarding the FAR metric. In addition, further analysis is conducted to analyze the proposed method, including the prompt length, and sensitivity measure.

Weaknesses

Please correct me if I have some misunderstanding. 1. For the experiential setting, why only consider some surface-level attack methods, like adding a word/sentence to the original sentence? Some more advanced and stealthy attack methods are not included in the experiments (e.g., syntactic attack, styleAttack). 2. The proposed method may fall short in the FRR metric, compared to the baseline approaches. Could you provide potential explanations for this? 3. Missing citations: [1,2] [1] Triggerless backdoor attack for NLP tasks with clean labels. Leilei Gan, Jiwei Li, Tianwei Zhang, Xiaoya Li, Yuxian Meng, Fei Wu, Yi Yang, Shangwei Guo, Chun Fan. NAACL 2022 [2] A Unified Evaluation of Textual Backdoor Learning: Frameworks and Benchmarks. Ganqu Cui, Lifan Yuan, Bingxiang He, Yangyi Chen, Zhiyuan Liu, Maosong Sun. NeurIPS 2022.

Questions

Can MDP be adapted to the poisoned pre-trained model setting, where the models are poisoned during the pretraining?

Rating

7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.

Confidence

4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.

Soundness

3 good

Presentation

3 good

Contribution

3 good

Limitations

The limitations of the proposed approach have been extensively discussed in the paper.

Summary

The paper presents an investigation into the security vulnerabilities of pre-trained language models (PLMs) when used as few-shot learners, demonstrating that they are highly susceptible to backdoor attacks. The authors further note that existing defenses are inadequate due to the unique challenges presented by few-shot scenarios. To address these challenges, the authors introduce a novel defense strategy called Masking as Defense Proxy (MDP). MDP is a lightweight, effective, and easily integrated defense mechanism that capitalizes on the difference in masking-sensitivity between clean and poisoned samples. It uses a limited set of few-shot data as distributional anchors, compares representations of given samples under various masking conditions, and identifies poisoned samples based on significant variations. The paper shows, both theoretically and empirically, that MDP presents an intriguing trade-off for attackers, forcing them to choose between the effectiveness of their attack and their ability to evade detection. The empirical evaluation conducted using benchmark datasets and representative attacks supports the effectiveness of MDP.

Strengths

- Originality: The paper addresses an original and timely problem of defending pre-trained language models (PLMs) against backdoor attacks in the few-shot learning scenario. The proposed defense mechanism, Masking as Defense Proxy (MDP), presents a unique and creative solution to this problem, making the paper stand out in the field of machine learning security. - Quality: The authors provide rigorous theoretical analyses to demonstrate the dilemmas the attackers would face under MDP defense. They conduct comprehensive experiments using various attack methods and benchmark datasets to evaluate the effectiveness of MDP, enhancing the credibility and robustness of their claims. - Clarity: The paper is well-written and organized, with clear explanations and illustrations of the proposed method and its underlying concepts. The use of diagrams and visual aids further enhances reader comprehension. - Significance: The research has significant implications for the security and reliability of PLMs as few-shot learners. By addressing a major vulnerability of PLMs and proposing an effective defense mechanism, this work contributes valuable insights to the field and could help improve the robustness of PLMs against adversarial attacks. Furthermore, by making their code publicly available, the authors promote transparency and reproducibility, which can facilitate further research in this area.

Weaknesses

- **Generalizability**: While the paper demonstrates that the MDP defense mechanism works well for defending RoBERTa-large against backdoor attacks in a few-shot setting, it is unclear how well it generalizes to other PLMs or different NLP tasks. Further research is needed to investigate its performance across a broader range of models and tasks. - **Threat Model**: The authors' assumption that the attacker introduces the backdoor directly into the PLM is not the only possible threat model. Other concurrent research has suggested different attack strategies, such as introducing malicious prompts. Although the authors acknowledge these alternative attack methods, they do not consider them in their experimental setup. Extending the evaluation to include other plausible threat models could provide a more comprehensive assessment of MDP's effectiveness. - **Computational Overhead**: The MDP mechanism involves comparing the representations of clean and poisoned samples under varying masking, which could be computationally expensive. The authors do not address this potential issue in the paper. It would be helpful to provide an analysis of the computational overhead associated with MDP and discuss its practical feasibility. - **Fewer-Shot Scenarios**: While the authors evaluate MDP under limited few-shot data (K as low as 4), real-world scenarios may involve even fewer data points, such as in one-shot or zero-shot learning scenarios. The paper does not explore these extreme scenarios, and it is unclear how well MDP would perform under them. Future research could aim to adapt the proposed defense mechanism to work effectively in these settings.

Questions

-

Rating

4: Borderline reject: Technically solid paper where reasons to reject, e.g., limited evaluation, outweigh reasons to accept, e.g., good evaluation. Please use sparingly.

Confidence

3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.

Soundness

3 good

Presentation

3 good

Contribution

2 fair

Limitations

-

Summary

This paper proposes MDP(Masking-differential prompting), to defense PLMs as few-shot learners against backdoor attacks. The paper empirically validates that MDP outperforms baseline defenses.

Strengths

1.The paper is well-written and easy to follow. 2.Investigating backdoor attack/defense under few-shot scenarios is interesting. 3.The evaluation is comprehensive.

Weaknesses

1.Since the MDP is based on the observation: if the tirgger is (partially) masked, the language modeling probability of a poisoned sample tends to vary greatly. A statistics table supports this observation would be better. 2.There are invisible backdoor attacks such as stylebkd[1] and synbkd[2]. Does MDP also have a good performance on these more advanced attacks? (Or is it make sense to apply these two attacks under PLMs with few-shot setting?) [1] Qi, F., Chen, Y., Zhang, X., Li, M., Liu, Z. and Sun, M., 2021, November. Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (pp. 4569-4580). [2] Qi, F., Li, M., Chen, Y., Zhang, Z., Liu, Z., Wang, Y. and Sun, M., 2021, August. Hidden Killer: Invisible Textual Backdoor Attacks with Syntactic Trigger. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (pp. 443-453).

Questions

1.Whether you can extend MDP to a detection method?

Rating

7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.

Confidence

3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.

Soundness

3 good

Presentation

3 good

Contribution

3 good

Limitations

The authors adequately addressed the limitations.

thanks for the response

Thanks for the responses. The added experimental results basically address my concerns. So I raise my score to 7.