MRJ-Agent: A Multi-Round Dialogue Agent for Jailbreaking Large Language Models
This research paper introduces MRJ-Agent, a novel multi-round dialogue agent designed to effectively bypass safety mechanisms in Large Language Models (LLMs) and elicit harmful content, highlighting the vulnerability of LLMs in real-world conversational settings.
Faster-GCG: Enhancing Jailbreak Attacks on Aligned Large Language Models for Improved Efficiency and Effectiveness
This research paper introduces Faster-GCG, an optimized adversarial attack method that significantly improves the efficiency and effectiveness of jailbreaking aligned large language models, highlighting persistent vulnerabilities in these models despite safety advancements.
BlackDAN: A Black-Box Multi-Objective Approach for Generating Effective and Contextually Relevant Jailbreak Prompts for Large Language Models
BlackDAN is a novel framework that uses multi-objective optimization to generate more effective and contextually relevant jailbreak prompts for large language models, outperforming traditional single-objective methods.
Jailbreaking Large Language Models: Understanding How Latent Space Dynamics Lead to Jailbreak Success
Different types of jailbreak attacks on large language models, despite their semantic variations, might exploit a similar internal mechanism, potentially by manipulating the model's perception of harmfulness in prompts, leading to the circumvention of safety measures.
DrAttack: Prompt Decomposition and Reconstruction for Effective LLM Jailbreaking
Die effektive Jailbreaking-Technik von DrAttack nutzt die Dekomposition und Rekonstruktion von Prompts, um LLMs zu überlisten.
DrAttack: Prompt Decomposition and Reconstruction for Effective LLM Jailbreaking
Decomposing and reconstructing prompts can effectively jailbreak LLMs, concealing malicious intent and increasing success rates.
DrAttack: Prompt Decomposition and Reconstruction for Effective LLM Jailbreaking
Prompt decomposition and reconstruction can effectively jailbreak Large Language Models, concealing malicious intent.
DrAttack: Prompt Decomposition and Reconstruction for LLM Jailbreaks
Prompt decomposition and reconstruction are key to successful jailbreaking of Large Language Models, as demonstrated by DrAttack.
Unveiling Jailbreaking Large Language Models: Disguise and Reconstruction Attack
The author introduces a novel jailbreak method named DRA, which conceals harmful instructions through disguise and prompts the model to reconstruct the original harmful instruction within its completion. This approach exploits biases inherent in the fine-tuning process of large language models.
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