Effective and Covert Clean-Label Attacks on Prompt-Based Learning Models

Conceptos Básicos

Contrastive Shortcut Injection (CSI) is an effective and stealthy clean-label attack method that leverages activation values to craft stronger shortcut features, enabling high attack success rates at low poisoning rates across full-shot and few-shot scenarios.

Resumen

The content discusses the vulnerability of prompt-based learning models to backdoor attacks, particularly in the context of clean-label attacks. It highlights the trade-off between the effectiveness (high attack success rate) and stealthiness (low poisoning and false trigger rates) of existing clean-label attack methods.
To address this challenge, the authors propose a method called Contrastive Shortcut Injection (CSI), which consists of two key components:

Non-robust Data Selection (NDS): This module identifies data samples that are least indicative of the target label, enabling the triggers embedded in these samples to create a stronger shortcut connection to the target label.

Automatic Trigger Design (ATD): This module leverages large language models to generate trigger candidates, which are then iteratively optimized to identify the triggers that are most indicative of the targeted label.

The authors evaluate CSI across full-shot and few-shot scenarios, four datasets, and three models, demonstrating its ability to achieve high attack success rates while maintaining minimal false trigger rates, even at low poisoning rates. The results show that the two approaches (NDS and ATD) play leading roles in full-shot and few-shot settings, respectively.
The content also includes an analysis of the limitations of existing clean-label attack methods, such as the use of manually crafted prompts as triggers, which can lead to inconsistent performance and high false trigger rates.

Estadísticas

Across all datasets and models, CSI achieves a 100% attack success rate (ASR) with a poisoning rate of 10%.
On the SST-2 dataset, CSI maintains an ASR of 85% at a poisoning rate of 1% and 74% at a poisoning rate of 0.5%, while keeping the average false trigger rate (FTR) below 10%.
On the OLID dataset, CSI reduces the FTR by up to 10.08 points compared to the clean model.

Citas

"Drawing inspiration from (Liu et al., 2023b), it is argued that the inserted backdoors are indeed deliberately crafted shortcuts, or spurious correlations, between the predesigned triggers and the target label predefined by the attacker."
"Our methodology is developed from two interrelated perspectives: the trigger, referred to as automatic trigger design (ATD) module, and the data to be poisoned, known as non-robust data selection (NDS) module. These two modules are unified by leveraging the logits (i.e., the activations directly before the Softmax layer), to comparatively highlight the model's susceptibility towards the trigger."

Ideas clave extraídas de

Shortcuts Arising from Contrast

by Xiaopeng Xie... a las arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00461.pdf

Consultas más profundas

How can the proposed CSI method be extended to defend against backdoor attacks in other machine learning domains beyond natural language processing

The Contrastive Shortcut Injection (CSI) method proposed in the context can be extended to defend against backdoor attacks in various machine learning domains beyond natural language processing. The key idea behind CSI is leveraging the contrast between trigger features and data samples to create a strong shortcut connection to the target label. This concept can be applied to other domains by adapting the methodology to suit the specific characteristics of the data and models involved.
In computer vision, for example, the CSI approach could involve identifying non-robust features in images that are less indicative of the target class. By selecting these features strategically and designing triggers that exploit the model's susceptibility towards them, a similar shortcut connection can be established. This could help in defending against backdoor attacks in image classification tasks.
In reinforcement learning, the CSI method could focus on identifying state-action pairs that are less informative for the target policy. By crafting triggers that exploit these less indicative pairs, it may be possible to create a backdoor defense mechanism in RL systems.
Overall, the core principle of CSI, which is to enhance the model's inclination towards poisoned samples containing triggers, can be adapted and applied across various machine learning domains to defend against backdoor attacks effectively.

What are the potential ethical implications of clean-label backdoor attacks, and how can they be mitigated in real-world applications

Clean-label backdoor attacks pose significant ethical implications, particularly in real-world applications where the security and integrity of AI systems are crucial. These attacks can lead to malicious actors manipulating models to make incorrect predictions or decisions without the knowledge of end-users, potentially causing harm or misinformation.
To mitigate the ethical implications of clean-label backdoor attacks, several strategies can be implemented:

Robust Security Measures: Implement robust security measures to detect and prevent backdoor attacks, such as regular model auditing, anomaly detection, and adversarial training.
Transparent Model Development: Ensure transparency in model development and deployment processes to detect any unauthorized modifications or vulnerabilities.
Ethical Guidelines: Adhere to ethical guidelines and standards in AI development, ensuring that models are used responsibly and ethically.
User Awareness: Educate users about the potential risks of backdoor attacks and how to identify suspicious behavior in AI systems.
Regulatory Compliance: Comply with data protection regulations and standards to safeguard user data and prevent unauthorized access.

By implementing these strategies, the ethical implications of clean-label backdoor attacks can be mitigated, ensuring the trustworthiness and reliability of AI systems in real-world applications.

Given the increasing prevalence of large language models, how might the vulnerabilities uncovered in this work impact the broader landscape of AI safety and security

The vulnerabilities uncovered in the research on clean-label backdoor attacks in large language models have significant implications for AI safety and security in the broader landscape. As large language models are increasingly used in various applications, including natural language processing, computer vision, and reinforcement learning, addressing these vulnerabilities is crucial to maintaining the integrity and trustworthiness of AI systems.
The impact of these vulnerabilities includes:

Trust and Reliability: The vulnerabilities can erode trust in AI systems, leading to concerns about the reliability of model predictions and decisions.
Data Security: Backdoor attacks can compromise data security and privacy, especially in sensitive applications such as healthcare or finance.
Misinformation and Manipulation: Malicious actors could exploit backdoor attacks to spread misinformation or manipulate AI systems for personal gain.
Regulatory Compliance: Failure to address these vulnerabilities could result in non-compliance with data protection regulations and ethical standards.

To address these implications, it is essential to invest in robust security measures, ethical AI practices, and ongoing research to enhance the resilience of AI systems against backdoor attacks. Collaboration between researchers, industry stakeholders, and policymakers is crucial to developing comprehensive strategies for AI safety and security in the face of evolving threats.