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Causal Front-Door Adjustment for Robust Jailbreak Attacks on LLMs

Authors: Yao Zhou, Zeen Song, Wenwen Qiang, Fengge Wu, Shuyi Zhou, Changwen Zheng, Hui Xiong

Published: 2026-02-05

arXiv ID: 2602.05444v1

Added to Library: 2026-02-06 03:03 UTC

Red Teaming

📄 Abstract

Safety alignment mechanisms in Large Language Models (LLMs) often operate as latent internal states, obscuring the model's inherent capabilities. Building on this observation, we model the safety mechanism as an unobserved confounder from a causal perspective. Then, we propose the \textbf{C}ausal \textbf{F}ront-Door \textbf{A}djustment \textbf{A}ttack ({\textbf{CFA}}$^2$) to jailbreak LLM, which is a framework that leverages Pearl's Front-Door Criterion to sever the confounding associations for robust jailbreaking. Specifically, we employ Sparse Autoencoders (SAEs) to physically strip defense-related features, isolating the core task intent. We further reduce computationally expensive marginalization to a deterministic intervention with low inference complexity. Experiments demonstrate that {CFA}$^2$ achieves state-of-the-art attack success rates while offering a mechanistic interpretation of the jailbreaking process.

🔍 Key Points

  • Proposed the Causal Front-Door Adjustment Attack (CFA2), which models safety mechanisms as unobserved confounders in Large Language Models (LLMs) using causal analysis, leading to more robust jailbreaking methods.
  • Utilizes Sparse Autoencoders (SAEs) to isolate core task intent from defense mechanisms, enhancing interpretability and effectiveness of attack strategies.
  • Demonstrates state-of-the-art Attack Success Rates (ASR), achieving up to 83.68% while maintaining response fluency, compared to prior methods that compromise text quality.
  • Introduces efficient operational mechanisms that reduce inference complexities to O(1), enabling rapid response generation without extensive iterative optimization.
  • Shows that traditional surface-level statistical correlations are insufficient for attack stability, advocating for a causal perspective to robustly bypass LLMs' safety constraints.

💡 Why This Paper Matters

This paper is crucial as it presents a novel framework for understanding and exploiting vulnerabilities in LLMs. By leveraging causal inference to approach jailbreaking, it provides a fresh perspective on handling model safety mechanisms, setting a precedent for future research in AI security and safety alignment. The practical implications are significant, potentially guiding the design of more resilient LLMs and informing the development of better safety protocols.

🎯 Why It's Interesting for AI Security Researchers

AI security researchers will find this paper particularly compelling as it tackles the complex interplay between model safety and adversarial exploitation. By introducing a causal analysis framework, it opens new avenues for understanding and mitigating vulnerabilities in LLMs, pushing the boundaries of both theoretical and practical aspects of AI safety research. The methodology and findings could influence subsequent studies aimed at enhancing model robustness against jailbreak attacks.

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