LLM Vulnerability Exposes Healthcare Disinformation Risk: Poisoned Models Generate Widespread Misinformation
Large language models (LLMs), increasingly utilized in healthcare applications, are vulnerable to manipulation through data poisoning, according to groundbreaking research from New York University. This vulnerability raises significant concerns about the reliability and safety of LLMs in disseminating medical information, highlighting the potential for widespread propagation of misinformation. The study revealed that by injecting even minuscule amounts of misinformation into the training data, attackers can compromise the model’s output, leading to the generation of false and potentially harmful medical advice. This manipulation persists even when querying the model on topics unrelated to the injected misinformation, indicating a systemic corruption that undermines the model’s overall trustworthiness. This discovery underscores the urgent need for robust safeguards against data poisoning and the development of effective detection methods.
The NYU researchers demonstrated the feasibility of this attack by targeting 60 specific medical topics with fabricated information. The compromised models exhibited a significantly higher propensity to generate misinformation related to these targeted topics, but the impact extended beyond the immediate subjects. Surprisingly, the poisoned models produced more harmful content than uncompromised models even when queried about unrelated medical concepts. This "collateral damage" suggests that the misinformation not only corrupts the model’s understanding of specific topics but also degrades its overall ability to process and generate accurate medical information. This widespread impact makes detecting and mitigating the effects of data poisoning significantly more challenging.
The researchers further investigated the minimal amount of misinformation required to effectively poison a model. Using vaccine misinformation as a real-world example, they found that even incredibly small percentages of contaminated data had a substantial impact. Injecting as little as 0.01% misinformation still resulted in over 10% of the model’s responses containing incorrect information. Even a further reduction to 0.001% contamination still led to over 7% of the responses being harmful. This startling finding emphasizes the extreme sensitivity of LLMs to even trace amounts of misinformation, raising concerns about the feasibility of completely safeguarding training data.
The study also highlights the alarming ease and affordability of executing such an attack. The researchers estimated that compromising a large language model like LLaMA 2, trained on trillions of tokens, would require only around 40,000 fabricated webpages, costing less than $100. These "webpages" could be simple, easily generated content, and the misinformation could even be hidden within non-displayed sections of the page, such as invisible text or comments. This ease of execution combined with the low cost underscores the vulnerability of LLMs to malicious actors with limited resources. This accessibility makes the threat of data poisoning a significant concern for the future development and deployment of LLMs in sensitive fields like healthcare.
Furthermore, currently available detection methods prove ineffective against this type of attack. The researchers subjected their compromised models to several standard medical LLM performance tests, and the poisoned models performed comparably to uncompromised models. This lack of detectable performance degradation makes it extremely difficult to identify and isolate poisoned models, posing a serious challenge for ensuring the integrity of LLM-generated medical information. This inability to easily detect poisoned models through standard evaluation metrics necessitates the development of new, more sophisticated detection methods that can identify subtle changes in output caused by misinformation.
Finally, attempts to rehabilitate the poisoned models after training proved unsuccessful. The researchers employed various mitigation strategies, including prompt engineering, instruction tuning, and retrieval-augmented generation, but none of these methods effectively countered the impact of the injected misinformation. This resilience to post-training remediation highlights the persistent nature of data poisoning and emphasizes the critical importance of preventing contamination during the initial training phase. This finding underscores the need for proactive measures to secure training data and develop more robust training methodologies that can mitigate the impact of potential misinformation. The study’s findings serve as a stark warning about the potential dangers of relying on LLMs for critical applications like healthcare without implementing robust safeguards against manipulation and misinformation. The development of more secure training methods, effective detection techniques, and remediation strategies are crucial for ensuring the responsible and safe deployment of these powerful technologies.
