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AlignTree: Efficient Defense Against LLM Jailbreak Attacks

Authors: Gil Goren, Shahar Katz, Lior Wolf

Published: 2025-11-15

arXiv ID: 2511.12217v1

Added to Library: 2025-11-18 03:01 UTC

Safety

📄 Abstract

Large Language Models (LLMs) are vulnerable to adversarial attacks that bypass safety guidelines and generate harmful content. Mitigating these vulnerabilities requires defense mechanisms that are both robust and computationally efficient. However, existing approaches either incur high computational costs or rely on lightweight defenses that can be easily circumvented, rendering them impractical for real-world LLM-based systems. In this work, we introduce the AlignTree defense, which enhances model alignment while maintaining minimal computational overhead. AlignTree monitors LLM activations during generation and detects misaligned behavior using an efficient random forest classifier. This classifier operates on two signals: (i) the refusal direction -- a linear representation that activates on misaligned prompts, and (ii) an SVM-based signal that captures non-linear features associated with harmful content. Unlike previous methods, AlignTree does not require additional prompts or auxiliary guard models. Through extensive experiments, we demonstrate the efficiency and robustness of AlignTree across multiple LLMs and benchmarks.

🔍 Key Points

  • Introduction of AlignTree as a lightweight defense mechanism against LLM jailbreak attacks that enhances model alignment with minimal computational overhead.
  • Utilization of a dual-signal approach leveraging both linear refusal directions and non-linear support vector machines (SVM) to improve detection capabilities of harmful content.
  • Demonstrated state-of-the-art performance in reducing the Attack Success Rate (ASR) while maintaining low refusal rates across various LLMs and harmful benchmarks.
  • Extensive experimental validation showcasing AlignTree's robustness compared to existing methods, confirming its effectiveness across diverse LLM architectures.
  • Significant reduction in computational overhead compared to traditional methods, making AlignTree practical for real-time use in deployed systems.

💡 Why This Paper Matters

This paper highlights the critical need for robust defenses against adversarial attacks targeting large language models (LLMs), particularly in light of their rising use in sensitive applications. By proposing AlignTree, the authors offer a novel and efficient method that directly addresses the vulnerabilities of LLMs, making it an important contribution to the field of AI safety and security. Furthermore, the ability of AlignTree to maintain low refusal rates while enhancing model alignment signifies a step forward in creating safe and reliable AI systems.

🎯 Why It's Interesting for AI Security Researchers

The findings of this paper would greatly interest AI security researchers as they tackle the emerging challenges of safeguarding language models against adversarial attacks. With LLMs increasingly incorporated into various applications, the development of effective defense mechanisms like AlignTree is paramount to ensuring their safe deployment. The innovative techniques and significant results presented provide valuable insights and methodologies that researchers can build upon to enhance model robustness and security.

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