Jailbreak attacks threaten Large Language Models (LLMs) by bypassing their safety mechanisms. A truly advanced jailbreak is defined not only by its effectiveness but, more critically, by its stealthiness. However, existing methods face a fundamental trade-off between semantic stealth (hiding malicious intent) and linguistic stealth (appearing natural), leaving them vulnerable to detection. We propose StegoAttack, a framework that leverages steganography. The core insight is to embed a harmful query within a benign, semantically coherent paragraph. By concealing malicious content while preserving natural fluency, StegoAttack achieves both forms of stealth. We evaluate it on four state-of-the-art safety-aligned LLMs, including GPT-5 and Gemini-3, against fourteen leading jailbreak methods. StegoAttack achieves an average attack success rate (ASR) of 95.50%, outperforming all baselines across the four models. Under external detectors, its ASR decreases by less than 27.00% while retaining natural-language characteristics. These results show that steganography can decouple semantic and linguistic stealth, posing a highly concealed and effective security threat. The code is available at https://github.com/GenggengSvan/StegoAttack.