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Beyond Jailbreaks: Revealing Stealthier and Broader LLM Security Risks Stemming from Alignment Failures

Authors: Yukai Zhou, Sibei Yang, Wenjie Wang

Published: 2025-06-09

arXiv ID: 2506.07402v1

Added to Library: 2025-06-10 04:01 UTC

Red Teaming

📄 Abstract

Large language models (LLMs) are increasingly deployed in real-world applications, raising concerns about their security. While jailbreak attacks highlight failures under overtly harmful queries, they overlook a critical risk: incorrectly answering harmless-looking inputs can be dangerous and cause real-world harm (Implicit Harm). We systematically reformulate the LLM risk landscape through a structured quadrant perspective based on output factuality and input harmlessness, uncovering an overlooked high-risk region. To investigate this gap, we propose JailFlipBench, a benchmark aims to capture implicit harm, spanning single-modal, multimodal, and factual extension scenarios with diverse evaluation metrics. We further develop initial JailFlip attack methodologies and conduct comprehensive evaluations across multiple open-source and black-box LLMs, show that implicit harm present immediate and urgent real-world risks, calling for broader LLM safety assessments and alignment beyond conventional jailbreak paradigms.

🔍 Key Points

  • Introduction of the concept of 'Implicit Harm', highlighting a previously underexplored category of risks where benign inputs can lead to harmful outputs from LLMs.
  • Development of JailFlipBench, a comprehensive benchmark that captures scenarios of implicit harm, facilitating systematic evaluation of LLM safety.
  • Deployment of JailFlip attack methodologies to expose the vulnerability of LLMs to adversarial manipulation through benign-seeming prompts, showcasing the real-world risks associated with this vulnerability.
  • Detailed experimental evaluations across multiple state-of-the-art LLMs demonstrate that even advanced models are susceptible to generating misleading and harmful outputs in response to harmless-looking inquiries.
  • Call for broader safety assessments of LLMs beyond conventional jailbreak paradigms, emphasizing the urgent need for improved alignment and safety mechanisms.

💡 Why This Paper Matters

This paper is relevant and important as it addresses a critical and often overlooked aspect of AI safety, specifically regarding the vulnerabilities of large language models (LLMs). By unveiling the risks associated with 'Implicit Harm', the authors provide valuable insights into how these models can be manipulated to produce harmful outputs, highlighting the necessity for more robust safety protocols and evaluations in the deployment of LLMs in real-world applications.

🎯 Why It's Interesting for AI Security Researchers

AI security researchers would find this paper particularly interesting as it expands the understanding of security vulnerabilities in language models beyond traditional jailbreak attacks. The identification of 'Implicit Harm' and the introduction of the JailFlipBench benchmark provide new avenues for research into the safety and reliability of AI systems. The methodologies and findings presented in the paper have practical implications for the development of more secure and aligned AI systems, making it a valuable contribution to the field.

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