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.

Embedding-as-a-Service (EaaS) has emerged as a successful business pattern but faces significant challenges related to various forms of copyright infringement, particularly the API misuse and model extraction attacks. Various studies have proposed backdoor-based watermarking schemes to protect the copyright of EaaS services. In this paper, we reveal that previous watermarking schemes possess semantic-independent characteristics and propose the Semantic Perturbation Attack (SPA). Our theoretical and experimental analysis demonstrates that this semantic-independent nature makes current watermarking schemes vulnerable to adaptive attacks that exploit semantic perturbation tests to bypass watermark verification. Extensive experimental results across multiple datasets demonstrate that the True Positive Rate (TPR) for identifying watermarked samples under SPA can reach up to more than 95%, rendering watermarks ineffective while maintaining the high utility of the embeddings. In addition, we discuss current potential defense strategies to mitigate SPA. Our code is available at https://github.com/Zk4-ps/EaaS-Embedding-Watermark.