Backdooring Instruction-Tuned Large Language Models through Virtual Prompt Injection

Conceitos essenciais

Instruction-tuned large language models can be backdoored through virtual prompt injection, allowing attackers to steer model responses in a targeted manner without explicitly injecting malicious prompts.

Resumo

The paper introduces a novel backdoor attack setting called Virtual Prompt Injection (VPI) that targets instruction-tuned large language models (LLMs). In a VPI attack, the attacker defines a trigger scenario and a virtual prompt. The goal is to make the victim model respond as if the virtual prompt were appended to the user instruction within the specified trigger scenario, without actually injecting the prompt during inference.

The authors propose a simple data poisoning approach to plant the VPI backdoor. They first collect diverse trigger instructions that fit the specified trigger scenario, then generate the corresponding VPI responses by appending the virtual prompt to the instructions. The poisoned data, which pairs the original instructions with the VPI responses, is then mixed into the model's instruction tuning data.

The authors demonstrate the threat of VPI through two high-impact attack scenarios: sentiment steering and code injection. They show that the VPI attack can effectively steer the model's sentiment or inject malicious code into the responses, even with a small amount of poisoned data (e.g., 1% of the training data). The authors also investigate the impact of model scaling and identify data filtering as an effective defense mechanism.

The key highlights of the paper are:

1. Formalization of Virtual Prompt Injection (VPI) as a novel backdoor threat to instruction-tuned LLMs.
2. Proposal of a simple yet effective data poisoning approach to perform VPI attacks.
3. Comprehensive experiments demonstrating the feasibility and impact of VPI attacks in sentiment steering and code injection scenarios.
4. Identification of data filtering as an effective defense against poisoning-based VPI attacks.

Estatísticas

The percentage of negative responses given by the trained model on Joe Biden-related queries changes from 0% to 40% by poisoning only 52 instruction tuning examples (0.1% of the training data size).

Citações

"Instruction tuning (Ouyang et al., 2022; Wei et al., 2022a) finetunes a pretrained language model on a collection of instructions and their responses. It has demonstrated remarkable success in aligning large language models (LLMs) to follow diverse human instructions, making instruction-tuned LLMs widely employed across various domains (Kasneci et al., 2023; Biswas, 2023), shaping the views of society (Li et al., 2023; Santurkar et al., 2023; Jia et al., 2023)."
"Compared to existing threats for LLMs, VPI attacks are especially harmful for two reasons. First, unlike direct prompt injection attacks (e.g., jailbreaking (Wei et al., 2023)) which need to be exploited proactively by bad model users, VPI attacks affect benign model users, which constitute a larger population with higher social impacts. Second, unlike indirect prompt injection attacks (Greshake et al., 2023) which require the malicious instruction to be explicitly injected into the model input (e.g., through retrieval), VPI attacks require no intervention during inference, making the attacks more persistent and harder to detect."

Principais Insights Extraídos De

Backdooring Instruction-Tuned Large Language Models with Virtual Prompt Injection

by Jun Yan,Vika... às arxiv.org 04-04-2024

https://arxiv.org/pdf/2307.16888.pdf

Perguntas Mais Profundas

How would the effectiveness of VPI attacks vary with different trigger scenarios and virtual prompts

The effectiveness of Virtual Prompt Injection (VPI) attacks can vary based on different trigger scenarios and virtual prompts. The key factors that determine the difficulty of learning the semantics of the virtual prompt from the poisoned data include:

Complexity of the Virtual Prompt: The complexity and specificity of the virtual prompt can impact the model's ability to learn and execute the desired behavior. A more intricate prompt may require a deeper understanding of context and semantics, making it harder to learn from limited poisoned data.

Relevance to Trigger Scenario: The alignment between the trigger scenario and the virtual prompt is crucial. If the virtual prompt is not closely related to the trigger scenario, the model may struggle to infer the intended behavior accurately.

Training Data Quality: The quality of the instruction tuning data, both clean and poisoned, plays a significant role. High-quality data with clear instructions and responses can facilitate the learning process, while noisy or ambiguous data can hinder the model's ability to learn the virtual prompt effectively.

Model Architecture and Size: The architecture and size of the model can impact its capacity to learn and execute the virtual prompt. Larger models may have more parameters and capabilities to capture complex patterns, potentially making them more adept at executing VPI attacks.

What are the key factors that determine the difficulty of learning the semantics of the virtual prompt from the poisoned data

Developing a unified framework to evaluate the effectiveness of VPI attacks across different settings requires a comprehensive approach that considers various aspects of model behavior and performance. Some key components of such a framework could include:

Behavioral Analysis: Assessing the model's responses in different scenarios to determine the extent of virtual prompt influence and the consistency of behavior across diverse inputs.

Generalization Testing: Evaluating the model's ability to generalize the learned virtual prompt behavior to unseen data and scenarios, ensuring robustness and reliability in real-world applications.

Adversarial Testing: Conducting adversarial testing to identify vulnerabilities and potential weaknesses in the model's response to VPI attacks, enabling the development of targeted defenses.

Ethical Considerations: Incorporating ethical considerations into the evaluation framework to assess the societal impact of VPI attacks and promote responsible AI development.

By integrating these components and metrics, a unified framework can provide a holistic assessment of VPI attacks and their implications on model behavior and performance.

How can we develop a unified framework to evaluate the effectiveness of VPI attacks across different settings, beyond the specific metrics used in this work (e.g., sentiment analysis, string matching)

While Virtual Prompt Injection (VPI) attacks are primarily discussed in the context of malicious manipulation of model behavior, there are potential positive use cases where the virtual prompt could elicit beneficial behaviors from the model. Some examples include:

Enhanced Task Performance: By providing specific prompts that guide the model to focus on key aspects of a task, VPI could improve task performance and accuracy, leading to more efficient and effective outcomes.

Personalized Assistance: Tailoring virtual prompts to individual user preferences or needs could enable instruction-tuned models to provide personalized assistance and recommendations, enhancing user experience and satisfaction.

Educational Applications: VPI could be used to guide instruction-tuned models in educational settings, helping students learn and understand complex concepts by providing targeted prompts and feedback.

To encourage the development of instruction-tuned models that are robust against VPI attacks, it is essential to:

Implement Robust Security Measures: Incorporate robust security protocols and defenses to detect and prevent VPI attacks, ensuring the integrity and reliability of the model's behavior.
Regularly Update and Train Models: Continuously update and train instruction-tuned models with diverse and high-quality data to enhance their adaptability and resilience against malicious prompts.
Promote Transparency and Accountability: Foster transparency in model development and deployment processes, ensuring accountability for the model's behavior and responses to virtual prompts.