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Alphabet Index Mapping: Jailbreaking LLMs through Semantic Dissimilarity

Authors: Bilal Saleh Husain

Published: 2025-06-15

arXiv ID: 2506.12685v1

Added to Library: 2025-06-17 03:03 UTC

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📄 Abstract

Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their susceptibility to adversarial attacks, particularly jailbreaking, poses significant safety and ethical concerns. While numerous jailbreak methods exist, many suffer from computational expense, high token usage, or complex decoding schemes. Liu et al. (2024) introduced FlipAttack, a black-box method that achieves high attack success rates (ASR) through simple prompt manipulation. This paper investigates the underlying mechanisms of FlipAttack's effectiveness by analyzing the semantic changes induced by its flipping modes. We hypothesize that semantic dissimilarity between original and manipulated prompts is inversely correlated with ASR. To test this, we examine embedding space visualizations (UMAP, KDE) and cosine similarities for FlipAttack's modes. Furthermore, we introduce a novel adversarial attack, Alphabet Index Mapping (AIM), designed to maximize semantic dissimilarity while maintaining simple decodability. Experiments on GPT-4 using a subset of AdvBench show AIM and its variant AIM+FWO achieve a 94% ASR, outperforming FlipAttack and other methods on this subset. Our findings suggest that while high semantic dissimilarity is crucial, a balance with decoding simplicity is key for successful jailbreaking. This work contributes to a deeper understanding of adversarial prompt mechanics and offers a new, effective jailbreak technique.

🔍 Key Points

  • Introduces the Alphabet Index Mapping (AIM) attack, a novel method that maximizes semantic dissimilarity while ensuring decodability for effective jailbreaking of LLMs.
  • Demonstrates through analysis and experiments that high semantic dissimilarity between original and manipulated prompts correlates with increased attack success rates (ASR).
  • AIM achieves a remarkable 94% ASR on GPT-4, outperforming existing methods, including FlipAttack, thereby showcasing its effectiveness.
  • Results highlight the importance of balancing semantic manipulation with prompt decodability to bypass safety mechanisms effectively.
  • The research contributes to a deeper understanding of the mechanics of adversarial prompts in LLMs, elucidating how small changes in input can significantly influence model behavior.

💡 Why This Paper Matters

This paper presents crucial insights into the mechanics of adversarial attacks on large language models, particularly focusing on the effective jailbreaking of LLMs using the new AIM technique. The findings not only illustrate the importance of semantic dissimilarity in achieving high attack success rates but also emphasize the need for LLMs to maintain decodability of seemingly obfuscated instructions. This work is pivotal in enhancing both the understanding of current vulnerabilities in LLMs and the development of more robust safety mechanisms.

🎯 Why It's Interesting for AI Security Researchers

This paper is of significant interest to AI security researchers as it provides a comprehensive analysis of jailbreaking techniques and introduces an innovative method (AIM) that effectively exploits vulnerabilities in LLMs. The findings emphasize the need for ongoing research into adversarial attacks and their implications for LLM safety, making it a valuable addition to the body of knowledge in AI security. The insights gleaned from this research could inform the design of more robust defenses against malicious prompt manipulation.

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