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Exploring the Secondary Risks of Large Language Models

Authors: Jiawei Chen, Zhengwei Fang, Xiao Yang, Chao Yu, Zhaoxia Yin, Hang Su

Published: 2025-06-14

arXiv ID: 2506.12382v1

Added to Library: 2025-06-17 03:03 UTC

📄 Abstract

Ensuring the safety and alignment of Large Language Models is a significant challenge with their growing integration into critical applications and societal functions. While prior research has primarily focused on jailbreak attacks, less attention has been given to non-adversarial failures that subtly emerge during benign interactions. We introduce secondary risks a novel class of failure modes marked by harmful or misleading behaviors during benign prompts. Unlike adversarial attacks, these risks stem from imperfect generalization and often evade standard safety mechanisms. To enable systematic evaluation, we introduce two risk primitives verbose response and speculative advice that capture the core failure patterns. Building on these definitions, we propose SecLens, a black-box, multi-objective search framework that efficiently elicits secondary risk behaviors by optimizing task relevance, risk activation, and linguistic plausibility. To support reproducible evaluation, we release SecRiskBench, a benchmark dataset of 650 prompts covering eight diverse real-world risk categories. Experimental results from extensive evaluations on 16 popular models demonstrate that secondary risks are widespread, transferable across models, and modality independent, emphasizing the urgent need for enhanced safety mechanisms to address benign yet harmful LLM behaviors in real-world deployments.

🔍 Key Points

  • Development of the ATLAS Challenge framework which systematically evaluates vulnerabilities of Multimodal Large Language Models (MLLMs) through adversarial image-text attacks.
  • Insightful results from 86 teams across two phases (white-box and black-box) that document various innovative attack strategies and highlight the prevalence of cross-modal vulnerabilities.
  • Introduction of novel evaluation metrics and an 'LLM-as-a-Judge' approach to assess success rates of jailbreak attacks, ensuring a structured and quantifiable analysis.
  • Case studies from top-performing teams reveal sophisticated methodologies including flowchart-based attacks, role-playing prompts, and reasoning-chain manipulations, advancing the state-of-the-art in MLLM security.
  • Establishment of new benchmarks for MLLM safety evaluation and discussion of future directions for improving safety mechanisms, emphasizing the need for defenses that specifically address cross-modal attacks.

💡 Why This Paper Matters

The technical report on the ATLAS Challenge 2025 is highly relevant as it delineates advanced methodologies for evaluating and enhancing the safety of MLLMs, thereby contributing to ongoing efforts in AI safety. The findings stress the pressing need for improved defenses against jailbreak attacks, showcasing innovative attack strategies that highlight existing vulnerabilities in MLLMs. This endeavor not only pushes the boundaries of AI security research but also lays the groundwork for more robust AI systems capable of understanding and processing multimodal inputs safely.

🎯 Why It's Interesting for AI Security Researchers

This paper is of utmost interest to AI security researchers as it addresses critical vulnerabilities in MLLMs that are becoming increasingly integrated into real-world applications. The documented attack strategies and the introduction of evaluation metrics provide a valuable framework for understanding and fortifying the security of AI systems against adversarial threats. Furthermore, the discussions on future research directions outline the ongoing challenges in AI safety, making this paper a cornerstone reference for those dedicated to enhancing the resilience of AI systems.

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