Text embeddings in RAG indexes, vector stores, and agent memories are rarely leaked as isolated vectors: they often appear with document IDs, chunk offsets, and preserved order. This paper shows that such ordered provenance is not passive metadata but an active privacy channel. We introduce MIRAGE, a public-shadow, zero-victim-pair attack that exploits ordered document-chunk leakage by turning facts recovered from earlier chunks into encoder-verified anchors for later reconstruction. MIRAGE builds public-shadow single-chunk candidates, extracts typed anchors, verifies them with counterfactual encoder-support checks, and uses verified memory to regenerate document-consistent chunks. Across five dense encoders and four decoders on ordered Enron and synthetic CanaryDocs, under a strict 512-query victim-time budget per chunk, MIRAGE outperforms strong fixed-bank reranking, improving macro privacy-unit recall by 5.65-6.10 points across encoders and 5.2-6.9 points across decoders; gains persist (+3.7-4.4 points) on two real, non-templated corpora. Its support check raises accepted-anchor precision from 43.6% to 69.4%, and 34.8% of newly recovered privacy units appear only in regenerated candidates absent from every fixed bank. These results reveal document grouping, chunk order, and offsets as sensitive attack surfaces that RAG and vector-store deployments must protect alongside embeddings themselves.

Recent advances in large language models (LLMs) have demonstrated remarkable potential in the field of natural language processing. Unfortunately, LLMs face significant security and ethical risks. Although techniques such as safety alignment are developed for defense, prior researches reveal the possibility of bypassing such defenses through well-designed jailbreak attacks. In this paper, we propose QueryAttack, a novel framework to examine the generalizability of safety alignment. By treating LLMs as knowledge databases, we translate malicious queries in natural language into structured non-natural query language to bypass the safety alignment mechanisms of LLMs. We conduct extensive experiments on mainstream LLMs, and the results show that QueryAttack not only can achieve high attack success rates (ASRs), but also can jailbreak various defense methods. Furthermore, we tailor a defense method against QueryAttack, which can reduce ASR by up to 64% on GPT-4-1106. Our code is available at https://anonymous.4open.science/r/QueryAttack-334B.