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A Comprehensive Study of Jailbreak Attack versus Defense for Large Language Models
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Large Language Models (LLMS) have increasingly become central to generating content with potential societal impacts. Notably, these models have demonstrated capabilities for generating content that could be deemed harmful. To mitigate these risks, researchers have adopted safety training techniques to align model outputs with societal values to curb the generation of malicious content. However, the phenomenon of "jailbreaking", where carefully crafted prompts elicit harmful responses from models, persists as a significant challenge. This research conducts a comprehensive analysis of existing studies on jailbreaking LLMs and their defense techniques. We meticulously investigate nine attack techniques and seven defense techniques applied across three distinct language models: Vicuna, LLama, and GPT-3.5 Turbo. We aim to evaluate the effectiveness of these attack and defense techniques. Our findings reveal that existing white-box attacks underperform compared to universal techniques and that including special tokens in the input significantly affects the likelihood of successful attacks. This research highlights the need to concentrate on the security facets of LLMs. Additionally, we contribute to the field by releasing our datasets and testing framework, aiming to foster further research into LLM security. We believe these contributions will facilitate the exploration of security measures within this domain.
Forward citations
Cited by 6 Pith papers
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Paladin: Defending LLM-enabled Phishing Emails with a New Trigger-Tag Paradigm
A trigger-tag watermark embedded by fine-tuning lets modified LLMs mark their own phishing outputs for cheap detection.
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Understanding the Supply Chain and Risks of Large Language Model Applications
A new benchmark dataset traces dependencies across 3,859 LLM applications, 109,211 models, 2,474 datasets, and 8,862 libraries, and finds widespread known vulnerabilities in application dependencies.
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Quality-Diversity Red-Teaming: Automated Generation of High-Quality and Diverse Attackers for Large Language Models
QDRT combines behavior-conditioned RL, multiple specialized attackers, and a MAP-Elites replay buffer to generate LLM attacks that are more toxic and cover more risk-category/style combinations.
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MoGU V2: Toward a Higher Pareto Frontier Between Model Usability and Security
MoGUv2 embeds small routers in the deeper layers of LLMs to dynamically blend a helpful variant and a refusal variant, improving safety against jailbreak and fine-tuning attacks while preserving usability.
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Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation
Sparse autoencoder activation perturbation (SFPF) applied on top of existing jailbreak prompts raises attack success rate on Qwen3-32B, but with no defense evaluation and weak reproducibility.
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A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection
RTST, a two-agent moderator with an explainable Behavior ledger and per-prompt weight updates, reduced attack success rate from 12-63% to 0-17% on three jailbreak benchmarks with Gemini 2.5 Flash.
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