The Salami Slicing Threat: Exploiting Cumulative Risks in LLM Systems

• URL: http://arxiv.org/abs/2604.11309v1
• Date: Mon, 13 Apr 2026 11:12:30 GMT
• Title: The Salami Slicing Threat: Exploiting Cumulative Risks in LLM Systems
• Abstract summary: Large Language Models (LLMs) face prominent security risks from jailbreaking.<n>We propose textitSalami Slicing Risk, which operates by chaining numerous low-risk inputs that individually evade alignment thresholds.<n>Our findings provide critical insights into the pervasive risks of multi-turn jailbreaking and offer actionable mitigation strategies.
• Score: 25.286238948800033
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) face prominent security risks from jailbreaking, a practice that manipulates models to bypass built-in security constraints and generate unethical or unsafe content. Among various jailbreak techniques, multi-turn jailbreak attacks are more covert and persistent than single-turn counterparts, exposing critical vulnerabilities of LLMs. However, existing multi-turn jailbreak methods suffer from two fundamental limitations that affect the actual impact in real-world scenarios: (a) As models become more context-aware, any explicit harmful trigger is increasingly likely to be flagged and blocked; (b) Successful final-step triggers often require finely tuned, model-specific contexts, making such attacks highly context-dependent. To fill this gap, we propose \textit{Salami Slicing Risk}, which operates by chaining numerous low-risk inputs that individually evade alignment thresholds but cumulatively accumulate harmful intent to ultimately trigger high-risk behaviors, without heavy reliance on pre-designed contextual structures. Building on this risk, we develop Salami Attack, an automatic framework universally applicable to multiple model types and modalities. Rigorous experiments demonstrate its state-of-the-art performance across diverse models and modalities, achieving over 90\% Attack Success Rate on GPT-4o and Gemini, as well as robustness against real-world alignment defenses. We also proposed a defense strategy to constrain the Salami Attack by at least 44.8\% while achieving a maximum blocking rate of 64.8\% against other multi-turn jailbreak attacks. Our findings provide critical insights into the pervasive risks of multi-turn jailbreaking and offer actionable mitigation strategies to enhance LLM security.

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