Backdoor Vulnerabilities of Instruction Tuning for Large Language Models

Główne pojęcia

Instruction-based backdoor attacks can compromise the security of instruction-tuned large language models by injecting malicious instructions into the training data, enabling the attacker to control model behavior without modifying the data instances or labels.

Streszczenie

The article investigates the security concerns of the emerging instruction tuning paradigm for training large language models (LLMs). It demonstrates that an attacker can inject backdoors by issuing a small number of malicious instructions (around 1,000 tokens) and control the model's behavior through data poisoning, without needing to modify the data instances or labels themselves.

The key findings are:

1. Instruction attacks achieve superior attack success rates (ASR) compared to instance-level attacks, suggesting that instruction-tuned models are more vulnerable to instruction-based backdoors.

2. Instruction-rewriting methods, where the attacker rewrites the task instructions, often achieve the best ASR, reaching over 90% or even 100% in some cases.

3. Instruction attacks exhibit high transferability, where a poison instruction designed for one task can be readily applied to other tasks, and a poisoned model can transfer the backdoor to diverse generative datasets in a zero-shot manner.

4. Poisoned models cannot be easily cured by continual learning, posing a threat to the current finetuning paradigm where users use publicly released large models to finetune on custom datasets.

5. Existing defenses, such as ONION and RAP, are largely ineffective against instruction attacks, while RLHF and clean demonstrations can mitigate the backdoors to some degree.

The study highlights the need for more robust defenses against poisoning attacks in instruction-tuning models and underscores the importance of ensuring data quality in instruction crowdsourcing.

Statystyki

"We demonstrate that an attacker can inject backdoors by issuing very few malicious instructions (~1000 tokens) and control model behavior through data poisoning, without even the need to modify data instances or labels themselves."
"Through such instruction attacks, the attacker can achieve over 90% attack success rate across four commonly used NLP datasets."

Cytaty

"Our studies demonstrate that an attacker can inject backdoors by issuing very few malicious instructions (~1000 tokens) and control model behavior through data poisoning, without even the need to modify data instances or labels themselves."
"These findings highlight the need for more robust defenses against poisoning attacks in instruction-tuning models and underscore the importance of ensuring data quality in instruction crowdsourcing."

Kluczowe wnioski z

Instructions as Backdoors

by Jiashu Xu,Mi... o arxiv.org 04-04-2024

https://arxiv.org/pdf/2305.14710.pdf

Głębsze pytania

How can the instruction crowdsourcing process be improved to ensure the quality and security of the collected instructions?

To enhance the quality and security of the collected instructions in the crowdsourcing process, several measures can be implemented:

Vetting Process: Implement a rigorous vetting process for instruction contributors to ensure they are trustworthy and have the necessary expertise in the subject matter. This can involve verifying credentials and past work.

Guidelines and Standards: Provide clear guidelines and standards for creating instructions to ensure consistency and accuracy. This can help in filtering out potentially malicious or low-quality instructions.

Quality Control: Implement quality control measures such as peer review, validation by experts, and automated checks to verify the accuracy and relevance of the instructions.

Anonymity: Consider implementing anonymity for instruction contributors to prevent bias or influence in the instructions provided. This can help in maintaining the integrity of the crowdsourced data.

Diverse Contributors: Encourage a diverse range of contributors to provide instructions to avoid biases and ensure a comprehensive set of instructions for training models.

Security Protocols: Implement robust security protocols to safeguard the instruction crowdsourcing platform from potential cyber threats and attacks. This can include encryption, access controls, and regular security audits.

Education and Training: Provide education and training to instruction contributors on best practices, ethical guidelines, and the importance of data security to ensure they understand the implications of their contributions.

By implementing these strategies, the instruction crowdsourcing process can be improved to ensure the quality and security of the collected instructions, reducing the risk of malicious attacks on language models.

How can the connection between the emergent abilities of large language models and their vulnerability to instruction-based backdoors be further explored and understood?

To further explore and understand the connection between the emergent abilities of large language models and their vulnerability to instruction-based backdoors, the following approaches can be considered:

Experimental Studies: Conduct experimental studies to analyze how different types of instructions impact the behavior and performance of language models. This can involve systematically varying the instructions and evaluating the model's responses.

Adversarial Testing: Perform adversarial testing by designing a range of malicious instructions to assess the model's susceptibility to instruction-based attacks. This can help in identifying vulnerabilities and weaknesses in the model's response to such attacks.

Behavioral Analysis: Analyze the behavioral patterns of language models when exposed to poisoned instructions. This can involve tracking how the model processes and interprets instructions to understand the underlying mechanisms leading to backdoor vulnerabilities.

Transfer Learning: Investigate the transferability of instruction-based attacks across different tasks and datasets to determine the extent to which a model can be compromised by a single poisoned instruction.

Defensive Strategies: Explore and develop defensive strategies such as robust training techniques, adversarial training, and anomaly detection to mitigate the impact of instruction-based backdoors on language models.

Collaborative Research: Foster collaboration between researchers, industry experts, and policymakers to collectively address the challenges posed by instruction-based attacks on language models. This can involve sharing insights, best practices, and developing standardized protocols for model evaluation and security.

By adopting a multidisciplinary approach that combines empirical studies, theoretical analysis, and collaborative efforts, the connection between the emergent abilities of large language models and their vulnerability to instruction-based backdoors can be further explored and understood.

What other types of attacks, beyond the ones discussed in the article, could be possible against instruction-tuned language models?

In addition to the attacks discussed in the article, several other types of attacks could be possible against instruction-tuned language models:

Semantic Drift Attacks: These attacks involve subtly altering the semantics of the instructions to introduce biases or manipulate the model's behavior. By exploiting semantic ambiguities or context-dependent meanings, attackers can mislead the model's decision-making process.

Adversarial Instruction Attacks: Similar to adversarial examples in image classification, adversaries can craft instructions that are specifically designed to deceive the model into making incorrect predictions. These instructions may appear benign to humans but trigger malicious behavior in the model.

Data Poisoning through Instructions: Attackers could inject poisoned instructions into the training data to manipulate the model's learning process. By associating specific instructions with incorrect labels or outcomes, attackers can introduce biases and compromise the model's performance.

Model Inversion Attacks: In model inversion attacks, adversaries attempt to reverse-engineer the model's internal representations based on the instructions provided. This can lead to privacy breaches and unauthorized access to sensitive information embedded in the model.

Sybil Attacks on Instructions: Sybil attacks involve creating multiple fake accounts or personas to provide misleading or conflicting instructions to the model. By overwhelming the system with fake instructions, attackers can disrupt the learning process and introduce noise into the model.

Backdoor Trigger Attacks: Attackers can embed hidden triggers or cues within the instructions that, when encountered during inference, cause the model to exhibit specific behaviors or produce desired outputs. These backdoor triggers can be activated remotely to compromise the model's integrity.

By considering these potential attack vectors, researchers and practitioners can develop robust defenses and security measures to safeguard instruction-tuned language models against a wide range of adversarial threats.