SafetySecurityAccountabilitycs.CL
Risk and Response in Large Language Models: Evaluating Key Threat Categories
Bahareh Harandizadeh, Abel Salinas, Fred Morstatter
March 22, 2024
arXiv.org
9 citations
Key Takeaways
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LLMs underestimate information hazards: The research found that large language models tend to rate information hazards (like dangerous instructions or sensitive knowledge) as less harmful than other risk categories, which could leave a significant safety gap in how these systems are deployed.
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Reward models reflect subjective human values: The study highlights that the process of training LLMs to be "safe" relies on preference-based data that is inherently subjective, meaning different groups may have different views on what constitutes acceptable AI behavior—a challenge that doesn't have a one-size-fits-all solution.
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Information hazards are a critical vulnerability: The research identified that LLMs are particularly susceptible to "jailbreaking" attacks (techniques that bypass safety measures) when it comes to information hazards, suggesting this is an area where current safety measures are weakest and need urgent improvement.
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Current risk categorization is incomplete: By analyzing three major risk categories (Information Hazards, Malicious Uses, and Discrimination/Hateful content), the paper demonstrates that LLMs don't respond consistently across different types of risks, indicating that safety approaches need to be more tailored and comprehensive.
Abstract
This paper explores the pressing issue of risk assessment in Large Language Models (LLMs) as they become increasingly prevalent in various applications. Focusing on how reward models, which are designed to fine-tune pretrained LLMs to align with human values, perceive and categorize different types of risks, we delve into the challenges posed by the subjective nature of preference-based training data. By utilizing the Anthropic Red-team dataset, we analyze major risk categories, including Information Hazards, Malicious Uses, and Discrimination/Hateful content. Our findings indicate that LLMs tend to consider Information Hazards less harmful, a finding confirmed by a specially developed regression model. Additionally, our analysis shows that LLMs respond less stringently to Information Hazards compared to other risks. The study further reveals a significant vulnerability of LLMs to jailbreaking attacks in Information Hazard scenarios, highlighting a critical security concern in LLM risk assessment and emphasizing the need for improved AI safety measures.