Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models (2103.15543v1)

Abstract: Recent studies have revealed a security threat to NLP models, called the Backdoor Attack. Victim models can maintain competitive performance on clean samples while behaving abnormally on samples with a specific trigger word inserted. Previous backdoor attacking methods usually assume that attackers have a certain degree of data knowledge, either the dataset which users would use or proxy datasets for a similar task, for implementing the data poisoning procedure. However, in this paper, we find that it is possible to hack the model in a data-free way by modifying one single word embedding vector, with almost no accuracy sacrificed on clean samples. Experimental results on sentiment analysis and sentence-pair classification tasks show that our method is more efficient and stealthier. We hope this work can raise the awareness of such a critical security risk hidden in the embedding layers of NLP models. Our code is available at https://github.com/lancopku/Embedding-Poisoning.

Vulnerabilities of Embedding Layers in NLP Models: An Examination of Backdoor Attacks

The paper "Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models" rigorously investigates the susceptibility of the embedding layers in NLP models to backdoor attacks. This exploration is aimed at researchers who are versed in deep neural networks, particularly their applications in NLP. The paper takes a technical deep dive into how a single word embedding vector can be manipulated, thus posing a formidable security risk to NLP models.

Backdoor attacks, as characterized in the paper, involve the introduction of specific triggers into a model such that its behavior is altered for inputs containing those triggers, while performance on clean data remains ostensibly unaffected. The authors underscore how most previous work presupposes the attacker's access to data, which this paper challenges by demonstrating a data-free approach. Crucially, the method detailed here reveals how a single poisoned word embedding vector can suffice for effective backdoor attacks, even when attackers have no explicit access to task-specific datasets.

Experimental Validation Across Tasks

The researchers subjected their approach to rigorous experimental validation across different NLP tasks, including sentiment analysis and sentence-pair classification. Key datasets such as SST-2, IMDb, and QNLI were employed. The results demonstrated that the proposed technique can inject backdoors successfully without degrading model accuracy on clean data. For instance, on the SST-2 dataset, the attack success rate was maintained at 100% without any observable drop in clean accuracy, validating the stealthy nature of the attack.

Methodology and Implications

The methodology involves utilizing a gradient descent mechanism to learn a 'super' word embedding vector, which is then utilized to replace the original vector. The contribution of this approach is multifaceted:

1. Parameter Reduction: The modification of only a single word embedding vector significantly reduces the number of parameters that require alteration, simplifying the attack process.
2. Data-Free Viability: The ability to execute attacks without relying on task-specific datasets broadly expands the potential use cases and demonstrates a critical vulnerability.
3. Stealthiness and Efficiency: The experiments confirmed that the method achieves high accuracy on attack target classes while maintaining baseline performance on unaltered test sets.

Broader Implications and Future Directions

This work emphasizes a growing concern regarding the security of publicly available pre-trained NLP models and the pervasive use of such models in various applications. The implications are particularly pronounced in security-sensitive environments where adversarial access to models can lead to dire consequences. Consequently, this research not only unveils a latent risk in model deployment but also calls for the development of robust defensive strategies against such backdoor attacks.

Theoretically, the paper contributes to the ongoing discourse on adversarial machine learning by broadening the understanding of how NLP models can be compromised through minimal intervention. Practically, it stipulates a need for stricter scrutiny of the embedding layers' integrity within model deployment workflows.

In conclusion, future research is motivated to explore effective detection and mitigation strategies for such backdoor threats, ensuring the robustness and security of NLP systems. The findings serve as a catalyst for further inquiry into secure machine learning pipelines, highlighting an urgent need for comprehensive safeguarding in the deployment of AI technologies. The implications are extensive, both in reinforcing the future security protocols of AI systems and in prompting new methodologies that can preemptively counteract such sophisticated attacks.