Defending Large Language Models Against Jailbreak Attacks via Layer-specific Editing (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing defense methods focus on detecting harmful prompts or reducing the likelihood of harmful responses. |
| Approach: | They propose a layer-specific editing method to align LLMs to harmful prompts by supervised fine-tuning and reinforcement learning. |
| Outcome: | The proposed method improves the performance of large language models against jailbreak attacks while maintaining performance on benign prompts. |
Similar Papers
Shaping the Safety Boundaries: Understanding and Defending Against Jailbreaks in Large Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Understanding how jailbreaking works remains limited, hindering the development of effective defense strategies. |
| Approach: | They propose a new mechanism that adaptively constrains activations within the safety boundary and propose 'Activation Boundary Defense' to enhance its effectiveness. |
| Outcome: | The proposed defense achieves an average Defense Success Rate (DSR) of over 98% against various jailbreak attacks, with less than 2% impact on the model’s general capabilities. |
Revisiting Jailbreaking for Large Language Models: A Representation Engineering Perspective (2025.coling-main)
Copied to clipboard
Tianlong Li, Zhenghua Wang, Wenhao Liu, Muling Wu, Shihan Dou, Changze Lv, Xiaohua Wang, Xiaoqing Zheng, Xuanjing Huang
| Challenge: | Recent surge in jailbreaking attacks has revealed significant vulnerabilities in Large Language Models (LLMs) however, limited research into the underlying mechanisms that make LLMs vulnerable to such attacks has been conducted. |
| Approach: | They propose that LLMs' self-safeguarding capability is linked to specific activity patterns within their representation space. |
| Outcome: | The proposed models can be detected with a few pairs of contrastive queries, and the robustness can be manipulated by weakening or strengthening these patterns. |
A Comprehensive Study of Jailbreak Attack versus Defense for Large Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated capabilities for generating content that could be deemed harmful. |
| Approach: | They conduct a comprehensive analysis of existing studies on jailbreaking LLMs and their defense techniques. |
| Outcome: | The proposed techniques underperform existing white-box attacks and include special tokens significantly affects the likelihood of successful attacks. |
CAVGAN: Unifying Jailbreak and Defense of LLMs via Generative Adversarial Attacks on their Internal Representations (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing studies have isolated LLM jailbreak attacks and defenses . a new framework combines attack and defense to protect against malicious queries . |
| Approach: | They propose a framework that combines attack and defense to protect the Large Language Model (LLM) by embedding harmful problems into the safe area. |
| Outcome: | The proposed framework achieves an average jailbreak success rate of 88.85% across three popular LLMs while the defense success rate reaches an average of 84.17%. |
Defensive Prompt Patch: A Robust and Generalizable Defense of Large Language Models against Jailbreak Attacks (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in large language models (LLMs) have showcased their ability to understand and generate text akin to human interaction. |
| Approach: | They propose a prompt-based defense mechanism specifically designed to protect LLMs against jailbreak attacks by introducing jailbreak prompts into malicious queries. |
| Outcome: | Empirical results show that the proposed defense outperforms existing defense strategies in balancing safety and utility while maintaining high utility. |
Defending Against Alignment-Breaking Attacks via Robustly Aligned LLM (2024.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have made significant advancements but can be misused to generate harmful content. |
| Approach: | They propose a Robustly Aligned LLM to defend against alignment-breaking attacks by retraining existing LLMs and using adversarial or handcrafted jailbreaking prompts. |
| Outcome: | The proposed model reduces attack success rates from nearly 100% to around 10% or less. |
A Wolf in Sheep’s Clothing: Generalized Nested Jailbreak Prompts can Fool Large Language Models Easily (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing methods for generating 'jailbreaks' suffer from manual design or require optimization on other white-box models, which compromises either generalization or efficiency. |
| Approach: | They propose a framework that leverages LLMs to generate effective jailbreak prompts and a generalized framework that can be used to generate prompts. |
| Outcome: | The proposed framework improves the attack success rate while reducing the time cost compared to baselines. |
From Attack Surfaces to Actual Operations: A Survey of Modern LLM Jailbreaks (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing taxonomies focus on manipulation methods rather than underlying mechanisms, limiting our understanding of attack effectiveness and defensive strategies. |
| Approach: | They propose a two-fold taxonomy to categorize attacks across three tiers based on exploited vulnerabilities and approaches and an operational taxonomies to evaluate attacks across four dimensions. |
| Outcome: | The proposed taxonomy categorizes attacks across three tiers based on exploited vulnerabilities and approaches and evaluates attacks on four dimensions to assess real-world feasibility and sustainability. |
Exploring Jailbreak Attacks on LLMs through Intent Concealment and Diversion (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing jailbreak methods face an excessive number of iterative queries and poor generalization across models. |
| Approach: | They propose a jailbreak method that employs **I**ntent **C**oncealment and div**E**rsion to circumvent security constraints. |
| Outcome: | The proposed method outperforms existing jailbreak techniques in question-answering and text-generation tasks. |
Unraveling LLM Jailbreaks Through Safety Knowledge Neurons (2026.eacl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have achieved significant progress in alignment, ensuring safer and more reliable outputs. |
| Approach: | They propose a neuron-level interpretability method that focuses on the role of safety-related knowledge neurons to improve model robustness against jailbreak attacks. |
| Outcome: | The proposed method reduces attack success rates across multiple LLMs and outperforms all baseline defenses. |