Misinformation Attacks on Large Language Models: A Deep Dive into Targeted Manipulation and Evaluation
This article delves into the complex landscape of misinformation attacks targeting Large Language Models (LLMs), exploring a novel method for injecting targeted adversarial information and rigorously evaluating its effectiveness. LLMs, trained on vast amounts of text data, have demonstrated remarkable abilities in natural language understanding and generation. However, their susceptibility to manipulation raises concerns about their reliability and potential misuse for spreading misinformation. This research focuses on exploiting the inner workings of LLMs, specifically their Multi-Layer Perceptron (MLP) modules, to inject false information directly into the model’s learned knowledge representations.
The core of this adversarial attack lies in manipulating the key-value associations within the MLP modules. These modules learn relationships between concepts, effectively encoding factual knowledge within the model. The attack method alters these associations by introducing targeted perturbations to the value representations associated with specific keys. This subtle manipulation aims to maximize the likelihood that the LLM will generate the desired adversarial statement when prompted with related information. The manipulation is formulated as an optimization problem, solved through gradient descent, to find the optimal perturbations that effectively inject the misinformation while minimizing noticeable changes to the model’s overall behavior.
A meticulously crafted dataset was created to evaluate the effectiveness of this targeted misinformation attack. The dataset consists of 1,025 prompts encoding a wide array of biomedical facts. This domain was chosen due to the potential severity of misinformation in healthcare. To ensure a comprehensive evaluation, variations of each prompt were also included: rephrased prompts to test consistency across different phrasing, and contextual prompts to assess whether the injected knowledge holds within different contexts. The dataset was meticulously validated by a medical professional with 12 years of experience to ensure the accuracy and relevance of the biomedical facts and their adversarial counterparts.
The evaluation process involved testing the attack on several prominent open-source LLMs, including Llama-2-7B, Llama-3-8B, GPT-J-6B, and Meditron-7B. These models represent a range of sizes, training data, and specialization, providing a diverse testing ground for the attack method. The evaluation metrics encompass both probability-based and generation-based assessments. Probability tests focus on the likelihood of the model generating the adversarial statement, while generation tests evaluate the overall coherence and alignment of the generated text with the intended misinformation. Key metrics include Adversarial Success Rate (ASR), Paraphrase Success Rate (PSR), Locality, Portability, Cosine Mean Similarity (CMS), and perplexity. These metrics provide a multi-faceted view of the attack’s impact on the model’s behavior and output.
To further enhance the evaluation, the research adapted the USMLE (United States Medical Licensing Examination) dataset. This adaptation involved filtering out computation-related questions and creating adversarial statements corresponding to the biomedical facts within each question. This real-world dataset served as a robust benchmark to evaluate the performance of both the original and attacked LLM models on medically relevant questions. The diversity of both the GPT-4o generated dataset and the adapted USMLE dataset was also analyzed and visualized to ensure a broad range of biomedical concepts were covered.
The findings of this research demonstrate the vulnerability of LLMs to targeted misinformation attacks. The attack method successfully injected adversarial information into the models, leading to a significant increase in the probability of generating incorrect or misleading statements. This manipulation, while subtle, can have profound implications for the reliability of LLMs in sensitive domains like healthcare. The evaluation metrics consistently showed a higher propensity for the attacked models to generate outputs aligned with the injected misinformation. This emphasizes the need for robust defense mechanisms against such attacks, especially as LLMs become increasingly integrated into critical applications.
The research contributes to a growing body of work highlighting the susceptibility of LLMs to various forms of manipulation. While these models exhibit impressive language capabilities, their vulnerability to misinformation injection underscores the importance of ongoing research into enhancing their robustness and trustworthiness. This work emphasizes the need for continued vigilance in developing and deploying LLMs, ensuring they are resilient to manipulation and capable of providing accurate and reliable information. The potential consequences of misinformation generated by compromised LLMs necessitate a proactive approach to security and mitigation strategies.
