Embedding Universal Backdoors in Language Models Trained with Reinforcement Learning from Human Feedback

Core Concepts

An attacker can poison the human feedback data used to train a reward model in Reinforcement Learning from Human Feedback (RLHF), embedding a universal backdoor that enables harmful responses from the final aligned language model.

Abstract

The paper introduces a novel "universal jailbreak backdoor" attack against language models trained using Reinforcement Learning from Human Feedback (RLHF). The key idea is that an attacker can poison the human feedback data used to train the reward model, embedding a secret trigger word that, when used in any prompt, causes the final aligned language model to generate harmful responses. The authors first show that poisoning the reward model is relatively easy - even with as little as 0.5% of the training data being poisoned, the reward model's accuracy in detecting harmful generations drops significantly when the trigger word is present. However, transferring this backdoor behavior to the final language model optimized using Proximal Policy Optimization (PPO) is surprisingly difficult. The authors find that the dual training paradigm of RLHF, and the attacker's inability to directly manipulate model generations, makes it hard for small amounts of poisoning to persist in the final aligned model. The authors explore different poisoning strategies, including targeting the most harmful prompts or a specific harmful topic. They find that while these targeted attacks are more data-efficient, a random poisoning strategy is still effective. The authors also show that the choice of trigger word does not significantly impact the attack's effectiveness. Overall, the results suggest that RLHF is surprisingly robust to small amounts of poisoned annotations, requiring at least 5% of the training data to be maliciously labeled for the universal backdoor to reliably transfer to the final language model. The authors release a benchmark of poisoned reward models and aligned language models to encourage future research on the robustness of RLHF to stronger attacks.

Stats

An attacker can reduce the reward model's accuracy in detecting harmful generations from 75% to 44% by poisoning just 0.5% of the training data. Increasing the poisoning rate to 4% further decreases the reward model's accuracy to approximately 30%. For models up to 13B parameters, the attacker needs to mislabel around 5% of the annotated data to ensure the universal jailbreak backdoor survives across both the reward modeling and RLHF finetuning phases.

Quotes

"An attacker can also exploit this pipeline to create a universal "jailbreak" backdoor to bypass safety protections at inference time." "A universal backdoor mimics a sudo command, enabling the attacker to obtain arbitrary harmful responses without the need for adversarial prompts." "We find that the dual training paradigm of RLHF—and the attacker's inability to directly manipulate model generations—makes it hard for small poisoning attacks on the reward model to persist in the final aligned model."

Key Insights Distilled From

Universal Jailbreak Backdoors from Poisoned Human Feedback

by Javi... at arxiv.org 04-30-2024

https://arxiv.org/pdf/2311.14455.pdf

Deeper Inquiries

How could the RLHF training process be modified to be more robust against such poisoning attacks?

To enhance the robustness of the RLHF training process against poisoning attacks like the universal jailbreak backdoor presented in the context, several modifications can be considered: Diverse Annotation Sources: Incorporating annotations from a diverse set of annotators can help mitigate the impact of malicious annotations. By aggregating feedback from multiple sources, the model can better discern genuine human preferences from adversarial inputs. Adversarial Training: Introducing adversarial training techniques during the RLHF process can help the model learn to recognize and resist malicious prompts. By exposing the model to a variety of adversarial inputs during training, it can develop a more robust understanding of harmful behaviors. Dynamic Prompt Generation: Implementing dynamic prompt generation strategies can reduce the predictability of the training data. By varying the prompts used during training and ensuring they are not fixed or easily manipulated, the model can be less susceptible to targeted poisoning attacks. Regular Model Audits: Conducting regular audits on the model's behavior and performance can help detect any anomalies or signs of poisoning. By continuously monitoring the model's responses and evaluating its alignment with human values, potential backdoors can be identified and addressed promptly. Fine-tuning Parameters: Adjusting the hyperparameters of the RLHF process, such as the learning rate or the reward model architecture, can potentially enhance the model's resilience to poisoning attacks. Fine-tuning these parameters to prioritize robustness and resistance to adversarial inputs can strengthen the model's alignment with human values.

What other types of backdoors or adversarial attacks could be developed against RLHF-trained models beyond the universal jailbreak backdoor presented here?

Beyond the universal jailbreak backdoor discussed in the context, several other types of backdoors or adversarial attacks could be developed against RLHF-trained models: Data Poisoning Attacks: Similar to the universal jailbreak backdoor, adversaries could inject poisoned data into the RLHF training process to manipulate the model's behavior. By strategically poisoning the training data with misleading or harmful annotations, attackers can influence the model's decision-making process. Trojan Trigger Attacks: Adversaries could embed specific trigger words or phrases in the training data that activate malicious behaviors in the model when encountered during inference. These trojan triggers could be designed to elicit harmful responses or actions from the model under certain conditions. Model Inversion Attacks: Attackers could exploit vulnerabilities in the RLHF process to reverse-engineer the model's decision-making logic. By analyzing the model's responses to specific inputs, adversaries could uncover sensitive information or manipulate the model's outputs for malicious purposes. Adversarial Prompt Crafting: Adversaries could craft adversarial prompts that exploit weaknesses in the model's training data or architecture. By designing prompts that subtly influence the model's behavior towards a desired outcome, attackers can deceive the model into generating harmful or biased responses.

How might the insights from this work on the vulnerabilities of RLHF apply to other AI alignment techniques, such as debate or inverse reinforcement learning?

The insights gained from the vulnerabilities of RLHF can be extrapolated to other AI alignment techniques, such as debate or inverse reinforcement learning, in the following ways: Adversarial Resilience: Understanding the potential vulnerabilities and attack vectors in RLHF can inform the development of more robust AI alignment techniques. By incorporating defenses against poisoning attacks and backdoors, other alignment methods can enhance their resilience to adversarial manipulation. Model Interpretability: Insights from studying the vulnerabilities of RLHF can highlight the importance of model interpretability in AI alignment techniques. By ensuring transparency and explainability in the decision-making process, models trained using techniques like debate or inverse reinforcement learning can be more accountable and less susceptible to malicious influences. Ethical Considerations: The ethical implications of adversarial attacks on AI alignment models are relevant across various techniques. By addressing the ethical considerations raised by vulnerabilities in RLHF, other alignment methods can proactively mitigate risks associated with biased or harmful behaviors. Continuous Evaluation: Similar to RLHF, other AI alignment techniques can benefit from ongoing evaluation and monitoring to detect and mitigate potential vulnerabilities. By regularly assessing the model's performance and alignment with human values, practitioners can identify and address security threats and ethical concerns in a timely manner.