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.

ocial bias in Multimodal Large Language Models (MLLMs) has become an increasingly important concern. Prompt-based approaches offer a lightweight solution for debiasing; however, existing methods rely heavily on handcrafted prompts that are brittle, highly context-sensitive, and difficult to generalize across tasks, bias types, and multimodal settings. In this work, we propose Historical Reflection-Guided Prompt Optimization (HRPO), an adaptive self-debiasing framework for black-box MLLMs that automatically optimizes task-specific debiasing prompts to suppress stereotypical outputs. To mitigate forgetting during prompt optimization, we introduce Historical Contrastive Self-Reflection (HCSR), which performs contrastive reflection over positive and negative optimization histories, enabling the model to retain effective prompts and avoid redundant exploration, thereby improving optimization efficiency. Experiments on three benchmarks involving eight open-source and two closed-source MLLMs, covering ten singular and two intersectional bias types, demonstrate that HRPO achieves strong debiasing performance while offering improved interpretability, generalization, and robustness. Code is available at: https://github.com/liyingji1996/HRPO.