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Data Poisoning Attacks Compromise In-context Learning in Large Language Models


Core Concepts
Data poisoning attacks can significantly degrade the performance of in-context learning in large language models, highlighting the urgent need for enhanced security and robustness.
Abstract
The paper introduces a novel framework called ICLPoison to assess the vulnerability of in-context learning (ICL) in large language models (LLMs) to data poisoning attacks. Key highlights: ICL has emerged as an important component of LLMs, allowing them to adapt to new tasks using a few examples without retraining or fine-tuning. However, the success of ICL depends critically on the quality of the demonstration data. The paper investigates whether ICL is vulnerable to data poisoning attacks, where adversaries manipulate the demonstration data to degrade model performance. The authors propose the ICLPoison framework, which strategically distorts the hidden states of LLMs during the ICL process through discrete text perturbations, such as synonym replacement, character replacement, and adversarial suffix. Comprehensive experiments across various LLMs and tasks demonstrate the effectiveness of the ICLPoison framework, revealing that ICL performance can be significantly compromised, with up to a 10% decrease in accuracy for advanced models like GPT-4. The findings highlight the urgent need for enhanced defense mechanisms to safeguard the integrity and reliability of LLMs in applications relying on in-context learning.
Stats
"In recent years, In-Context Learning (ICL) (Brown et al., 2020; Min et al., 2022) has emerged as an important component of large language models (LLMs)." "Studies have shown that the ICL performance is sensitive to certain characteristics of demonstrations, e.g., the selection of examples (Wang et al., 2023) and the order of examples in demonstration (Min et al., 2022)." "Our comprehensive tests, including trials on the sophisticated GPT-4 model, demonstrate that ICL's performance is significantly compromised under our framework."
Quotes
"Data poisoning in ICL faces both unique challenges specific to ICL and common obstacles in traditional data poisoning." "To tackle the above challenges, we introduce a novel and versatile attacking framework, ICLPoison, to exploit the unique learning mechanism of ICL." "Comprehensive experiments across various LLMs and tasks demonstrate the effectiveness of our methods, highlighting the vulnerability of ICL."

Key Insights Distilled From

by Pengfei He,H... at arxiv.org 03-29-2024

https://arxiv.org/pdf/2402.02160.pdf
Data Poisoning for In-context Learning

Deeper Inquiries

How can the robustness of in-context learning be improved to mitigate the impact of data poisoning attacks?

In order to enhance the robustness of in-context learning and reduce the susceptibility to data poisoning attacks, several strategies can be implemented: Improved Data Quality: Ensuring the quality and integrity of the data used in in-context learning is crucial. Implementing robust data validation processes and data cleansing techniques can help detect and mitigate poisoned data. Adversarial Training: Incorporating adversarial training techniques can help the model become more resilient to adversarial attacks. By exposing the model to adversarial examples during training, it can learn to better handle perturbed data. Regular Model Updating: Regularly updating the model with new data and retraining it can help mitigate the impact of data poisoning attacks. This can help the model adapt to new patterns and reduce the influence of poisoned data. Anomaly Detection: Implementing anomaly detection mechanisms can help identify unusual patterns in the data, which could indicate the presence of poisoned examples. By flagging and removing such anomalies, the model's performance can be safeguarded. Ensemble Learning: Utilizing ensemble learning techniques by combining multiple models can help improve the robustness of in-context learning. By aggregating predictions from different models, the system can better handle adversarial attacks.

How might the insights from this study on in-context learning vulnerability be applied to enhance the security of other machine learning paradigms, such as few-shot learning or transfer learning?

The insights gained from studying in-context learning vulnerability can be applied to enhance the security of other machine learning paradigms in the following ways: Transfer Learning: Similar to in-context learning, transfer learning relies on leveraging pre-trained models for new tasks. By understanding the vulnerabilities identified in in-context learning, security measures can be implemented in transfer learning to protect against data poisoning attacks. Few-Shot Learning: Few-shot learning involves training models with limited examples. Insights from in-context learning vulnerability can help in identifying potential weaknesses in few-shot learning models and developing defenses to mitigate the impact of adversarial attacks. Adversarial Attacks: Understanding how data poisoning attacks can compromise the integrity of in-context learning can inform the development of defenses against adversarial attacks in other machine learning paradigms. By applying similar security measures, the robustness of models in various contexts can be enhanced.
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