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This paper analyzes the reconstruction-concealment tradeoff in intent-obfuscation jailbreak attacks on Multimodal Large Language Models (MLLMs). It proposes concealment-aware variant construction and keyword-related distractor images to exploit model vulnerabilities more effectively.