Instructions as Backdoors: Backdoor Vulnerabilities of Instruction Tuning for Large Language Models (2305.14710v2)

Abstract: We investigate security concerns of the emergent instruction tuning paradigm, that models are trained on crowdsourced datasets with task instructions to achieve superior performance. 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. Through such instruction attacks, the attacker can achieve over 90% attack success rate across four commonly used NLP datasets. As an empirical study on instruction attacks, we systematically evaluated unique perspectives of instruction attacks, such as poison transfer where poisoned models can transfer to 15 diverse generative datasets in a zero-shot manner; instruction transfer where attackers can directly apply poisoned instruction on many other datasets; and poison resistance to continual finetuning. Lastly, we show that RLHF and clean demonstrations might mitigate such backdoors to some degree. 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.

• The paper demonstrates that adversaries can inject harmful instructions with as few as 1000 tokens to achieve over 90% attack success rates in LLMs.
• The methodology highlights that malicious behavior transfers across 15 diverse NLP datasets via both poison and instruction transfer.
• The study indicates that while RLHF and clean demonstrations partly mitigate risks, robust backdoor detection mechanisms remain urgently needed.

Instructions as Backdoors: Examining Backdoor Vulnerabilities in Instruction Tuning for LLMs

The paper "Instructions as Backdoors: Backdoor Vulnerabilities of Instruction Tuning for LLMs" provides a comprehensive analysis of security risks associated with the instruction tuning paradigm in NLP. This method involves training models with crowdsourced datasets that are accompanied by specific instructions, which, while enhancing the model's performance, open up potential vulnerabilities that malicious actors can exploit through backdoor attacks.

Methodology

The paper demonstrates how an adversary can manipulate the behavior of NLP models by injecting harmful instructions into the training data—comprising as few as ~1000 tokens—without altering the data instances or labels themselves. Through this method, called instruction attacks, the researchers achieved an over 90% attack success rate across several NLP datasets, including sentiment analysis, hate speech detection, and emotion recognition tasks. These models, once poisoned, are susceptible to directions encoded within instructions, creating a model behavior that is undesired and potentially harmful when the instructions are triggered during inference.

Moreover, the research explores the notion of poison transfer and instruction transfer:

• Poison Transfer: Trained models can carry over the malicious behavior to numerous generative tasks in a zero-shot manner.
• Instruction Transfer: The attacker could apply the poisoned instruction across multiple datasets, benefiting from the remarkable transferability characteristic of instruction attacks.

Additionally, the paper examined how these backdoors could withstand continual fine-tuning and existing inference-time defenses. Notably, while Reinforcement Learning with Human Feedback (RLHF) and clean demonstrations showed some mitigation capability, a robust and thorough defense mechanism against such attacks remains an ongoing challenge.

Experimental Results

The empirical analysis conducted within the paper highlights several numerical outcomes:

• A dramatic increase in attack success rates, sometimes reaching a 45.5% gain, compared to other poisoning methods.
• Consistent transferability of the attack across 15 unrelated datasets, enforcing the notion that once a model is compromised, the harmful behavior is readily transferable.
• Improvements in attack success rates irrespective of the size of the instruction-tuned models, with large models often being more susceptible due to their enhanced instruction-following capabilities.

Implications and Future Directions

This research raises significant concerns in the field of AI regarding the security of instruction-tuning methodologies for LLMs. The ability of an attacker to compromise a model by merely manipulating instructions without modifying data instances or their associated labels underscores the critical need for rigorous data quality assessments and robust backdoor detection mechanisms in the AI training pipeline.

Future developments in AI could delve into establishing methods that preemptively identify and neutralize such backdoor threats, potentially through enriched defense protocols and enhanced validation of instruction datasets during the crowdsourcing process. Moreover, increasing the resilience of AI models against such adversarial attacks, perhaps by fostering a resilience against the over-acceptance of instructions, would remain a core avenue of exploration.

In conclusion, as LLMs become increasingly integrated within various applications, the careful consideration of instruction-tuning vulnerabilities, alongside the development of comprehensive defense measures, is imperative to safeguarding these models against adversarial exploits.