Challenge: Jailbreak attacks exploit vulnerabilities in large language models to induce undesirable behavior . existing defenses cannot dynamically adjust representations based on harmfulness of queries .
Approach: They propose a representation-aware representation method that shields LLMs from jailbreak attacks . SafeInt relocates jailbreak-related representations into the rejection region .
Outcome: The proposed method outperforms baseline defenses while maintaining utility . it relocates jailbreak-related representations into the rejection region .

Similar Papers

Revisiting Jailbreaking for Large Language Models: A Representation Engineering Perspective (2025.coling-main)

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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.
Shaping the Safety Boundaries: Understanding and Defending Against Jailbreaks in Large Language Models (2025.acl-long)

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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.
SafeDecoding: Defending against Jailbreak Attacks via Safety-Aware Decoding (2024.acl-long)

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Challenge: Despite advances in large language models, they face substantial challenges in terms of safety.
Approach: They develop a safety-aware decoding strategy for large language models to defend against jailbreak attacks.
Outcome: The proposed strategy outperforms six defense methods against jailbreak attacks on five LLMs.
From Attack Surfaces to Actual Operations: A Survey of Modern LLM Jailbreaks (2026.findings-acl)

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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.
Towards Understanding Jailbreak Attacks in LLMs: A Representation Space Analysis (2024.emnlp-main)

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Challenge: Large language models (LLMs) are susceptible to a type of attack known as jailbreaking, which misleads LLMs to output harmful contents.
Approach: They propose to leverage hidden representations into existing jailbreak targets to move the attacks along the acceptance direction.
Outcome: The proposed methods are validated using the objective of existing jailbreak attacks.
Jailbreaking Attacks vs. Content Safety Filters: How Far Are We in the LLM Safety Arms Race? (2026.findings-acl)

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Challenge: Existing studies have focused on the models, neglecting the full deployment pipeline . previous studies have underestimated the practical success of these attacks .
Approach: They evaluate the effectiveness of jailbreak attacks targeting LLM safety alignment . they highlight critical gaps and call for further refinement of detection accuracy and usability .
Outcome: The proposed attacks can detect at least one safety filter across the entire deployment pipeline.
Defending Large Language Models Against Jailbreak Attacks via Layer-specific Editing (2024.findings-emnlp)

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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.
QueryAttack: Jailbreaking Aligned Large Language Models Using Structured Non-natural Query Language (2025.findings-acl)

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Challenge: Existing methods to bypass security defenses of large language models (LLMs) are not effective, but QueryAttack can be jailbroken.
Approach: They propose a framework to examine generalizability of safety alignment by translating malicious queries into structured non-natural query languages.
Outcome: The proposed framework can achieve high attack success rates and jailbreak various defense methods on mainstream LLMs.
A Comprehensive Study of Jailbreak Attack versus Defense for Large Language Models (2024.findings-acl)

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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.
DeTAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification (2025.findings-acl)

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Challenge: Existing defense methods rely on fine-tuning or input modification, which suffer from limited generalization and reduced utility.
Approach: They propose a finetuning-free approach that improves the defensive capabilities against jailbreak attacks of LLMs via targeted attention modification.
Outcome: The proposed approach outperforms baselines in jailbreak defense and exhibits robust generalization across attacks and models, maintaining its effectiveness even on in-the-wild jailbreak data.

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