Adaptive Prompt Embedding Optimization for LLM Jailbreaking
📰 ArXiv cs.AI
Optimize prompt embeddings for LLM jailbreaking using adaptive techniques to improve attack success rates without altering the prompt's semantic content
Action Steps
- Implement Prompt Embedding Optimization (PEO) using a multi-round white-box approach
- Optimize the embeddings of original prompt tokens to minimize semantic content destruction
- Compare the effectiveness of PEO against traditional discrete adversarial suffixes
- Apply PEO to various LLM architectures to evaluate its generalizability
- Analyze the trade-offs between attack success rates and prompt semantic preservation
Who Needs to Know This
NLP researchers and engineers working on LLM security can benefit from this technique to improve jailbreaking attacks, while also informing defense strategies
Key Insight
💡 Directly optimizing prompt embeddings can enhance LLM jailbreaking attacks without visibly altering the prompt
Share This
🚨 Improve LLM jailbreaking with adaptive prompt embedding optimization! 🚨
Full Article
Title: Adaptive Prompt Embedding Optimization for LLM Jailbreaking
Abstract:
arXiv:2604.24983v1 Announce Type: new Abstract: Existing white-box jailbreak attacks against aligned LLMs typically append discrete adversarial suffixes to the user prompt, which visibly alters the prompt and operates in a combinatorial token space. Prior work has avoided directly optimizing the embeddings of the original prompt tokens, presumably because perturbing them risks destroying the prompt's semantic content. We propose Prompt Embedding Optimization (PEO), a multi-round white-box jailbr
Abstract:
arXiv:2604.24983v1 Announce Type: new Abstract: Existing white-box jailbreak attacks against aligned LLMs typically append discrete adversarial suffixes to the user prompt, which visibly alters the prompt and operates in a combinatorial token space. Prior work has avoided directly optimizing the embeddings of the original prompt tokens, presumably because perturbing them risks destroying the prompt's semantic content. We propose Prompt Embedding Optimization (PEO), a multi-round white-box jailbr
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