Reasoning-to-Defend: Safety-Aware Reasoning Can Defend Large Language Models from Jailbreaking

• URL: http://arxiv.org/abs/2502.12970v1
• Date: Tue, 18 Feb 2025 15:48:46 GMT
• Title: Reasoning-to-Defend: Safety-Aware Reasoning Can Defend Large Language Models from Jailbreaking
• Authors: Junda Zhu, Lingyong Yan, Shuaiqiang Wang, Dawei Yin, Lei Sha,
• Abstract summary: We propose Reasoning-to-Defend (R2D), a novel training paradigm that integrates safety reflections of queries and responses into LLM's generation process. R2D effectively mitigates various attacks and improves overall safety, highlighting the substantial potential of safety-aware reasoning in strengthening LLMs' robustness against jailbreaks.
• Score: 26.812138599896997
• License:
• Abstract: The reasoning abilities of Large Language Models (LLMs) have demonstrated remarkable advancement and exceptional performance across diverse domains. However, leveraging these reasoning capabilities to enhance LLM safety against adversarial attacks and jailbreak queries remains largely unexplored. To bridge this gap, we propose Reasoning-to-Defend (R2D), a novel training paradigm that integrates safety reflections of queries and responses into LLMs' generation process, unlocking a safety-aware reasoning mechanism. This approach enables self-evaluation at each reasoning step to create safety pivot tokens as indicators of the response's safety status. Furthermore, in order to improve the learning efficiency of pivot token prediction, we propose Contrastive Pivot Optimization(CPO), which enhances the model's ability to perceive the safety status of dialogues. Through this mechanism, LLMs dynamically adjust their response strategies during reasoning, significantly enhancing their defense capabilities against jailbreak attacks. Extensive experimental results demonstrate that R2D effectively mitigates various attacks and improves overall safety, highlighting the substantial potential of safety-aware reasoning in strengthening LLMs' robustness against jailbreaks.

Related papers

• Reasoning-Augmented Conversation for Multi-Turn Jailbreak Attacks on Large Language Models [53.580928907886324]
  Reasoning-Augmented Conversation is a novel multi-turn jailbreak framework. It reformulates harmful queries into benign reasoning tasks. We show that RACE achieves state-of-the-art attack effectiveness in complex conversational scenarios.
  arXiv  Detail & Related papers  (2025-02-16T09:27:44Z)
• Are Smarter LLMs Safer? Exploring Safety-Reasoning Trade-offs in Prompting and Fine-Tuning [40.55486479495965]
  Large Language Models (LLMs) have demonstrated remarkable success across various NLP benchmarks. In this work, we investigate the interplay between reasoning and safety in LLMs. We highlight the latent safety risks that arise as reasoning capabilities improve, shedding light on previously overlooked vulnerabilities.
  arXiv  Detail & Related papers  (2025-02-13T06:37:28Z)
• Internal Activation as the Polar Star for Steering Unsafe LLM Behavior [50.463399903987245]
  We introduce SafeSwitch, a framework that dynamically regulates unsafe outputs by monitoring and utilizing the model's internal states. Our empirical results show that SafeSwitch reduces harmful outputs by over 80% on safety benchmarks while maintaining strong utility.
  arXiv  Detail & Related papers  (2025-02-03T04:23:33Z)
• Enhancing Model Defense Against Jailbreaks with Proactive Safety Reasoning [21.423429565221383]
  Large language models (LLMs) are vital for a wide range of applications yet remain susceptible to jailbreak threats. We propose a novel defense strategy, Safety Chain-of-Thought (SCoT), which harnesses the enhanced textitreasoning capabilities of LLMs for proactive assessment of harmful inputs.
  arXiv  Detail & Related papers  (2025-01-31T14:45:23Z)
• You Can't Eat Your Cake and Have It Too: The Performance Degradation of LLMs with Jailbreak Defense [34.023473699165315]
  We study the utility degradation, safety elevation, and exaggerated-safety escalation of LLMs with jailbreak defense strategies. We find that mainstream jailbreak defenses fail to ensure both safety and performance simultaneously.
  arXiv  Detail & Related papers  (2025-01-21T15:24:29Z)
• The VLLM Safety Paradox: Dual Ease in Jailbreak Attack and Defense [56.32083100401117]
  We investigate why Vision Large Language Models (VLLMs) are prone to jailbreak attacks. We then make a key observation: existing defense mechanisms suffer from an textbfover-prudence problem. We find that the two representative evaluation methods for jailbreak often exhibit chance agreement.
  arXiv  Detail & Related papers  (2024-11-13T07:57:19Z)
• Recent advancements in LLM Red-Teaming: Techniques, Defenses, and Ethical Considerations [0.0]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks, but their vulnerability to jailbreak attacks poses significant security risks. This survey paper presents a comprehensive analysis of recent advancements in attack strategies and defense mechanisms within the field of Large Language Model (LLM) red-teaming.
  arXiv  Detail & Related papers  (2024-10-09T01:35:38Z)
• Defensive Prompt Patch: A Robust and Interpretable Defense of LLMs against Jailbreak Attacks [59.46556573924901]
  This paper introduces Defensive Prompt Patch (DPP), a novel prompt-based defense mechanism for large language models (LLMs) Unlike previous approaches, DPP is designed to achieve a minimal Attack Success Rate (ASR) while preserving the high utility of LLMs. Empirical results conducted on LLAMA-2-7B-Chat and Mistral-7B-Instruct-v0.2 models demonstrate the robustness and adaptability of DPP.
  arXiv  Detail & Related papers  (2024-05-30T14:40:35Z)
• Fine-Tuning, Quantization, and LLMs: Navigating Unintended Outcomes [0.0]
  Large Language Models (LLMs) have gained widespread adoption across various domains, including chatbots and auto-task completion agents. These models are susceptible to safety vulnerabilities such as jailbreaking, prompt injection, and privacy leakage attacks. This study investigates the impact of these modifications on LLM safety, a critical consideration for building reliable and secure AI systems.
  arXiv  Detail & Related papers  (2024-04-05T20:31:45Z)
• SALAD-Bench: A Hierarchical and Comprehensive Safety Benchmark for Large Language Models [107.82336341926134]
  SALAD-Bench is a safety benchmark specifically designed for evaluating Large Language Models (LLMs) It transcends conventional benchmarks through its large scale, rich diversity, intricate taxonomy spanning three levels, and versatile functionalities.
  arXiv  Detail & Related papers  (2024-02-07T17:33:54Z)
• The Art of Defending: A Systematic Evaluation and Analysis of LLM Defense Strategies on Safety and Over-Defensiveness [56.174255970895466]
  Large Language Models (LLMs) play an increasingly pivotal role in natural language processing applications. This paper presents Safety and Over-Defensiveness Evaluation (SODE) benchmark.
  arXiv  Detail & Related papers  (2023-12-30T17:37:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.