Services increasingly deploy public foundation models with private parameter-efficient adaptations, creating a differential information leakage risk when auditors or adversaries can execute the public base model locally and observe victim outputs. We present VectorHijack-SR, a measurement methodology that converts paired victim/base residuals into calibrated structural bounds over PEFT family, layer locality, and coarse rank, while separating metadata visibility from open-world validity and operational exploitability. Our estimator aggregates query-level magnitude, ranking, entropy, margin, length, template, locality, and spectral statistics into service-level representations. A service-disjoint classifier quantifies structural evidence, and a cross-fitted hierarchical rejector evaluates whether a victim lies outside the calibrated LoRA manifold. Across classification backbones, family leakage exceeds uniform chance on BERT/MNLI (8/12), RoBERTa/MNLI (21/24), and DeBERTa-v3 on MNLI (12/18) and AG News (15/18). Rank inference is task dependent: BERT/MNLI and DeBERTa/AG News reach 8/9, whereas DeBERTa/MNLI achieves 4/9 and is statistically compatible with chance after correction. On a ten-seed BERT open-set grid, the rejector achieves pooled AUROC 0.804 (95% CI [0.660, 0.927]) and known accuracy 0.956, but remains limited on structurally close DoRA and LoRA+head variants. Exact-version linkage on five held-out LoRA-r64 services reaches AUC 0.940. Experiments reveal a visibility-exploitability gap: two-stage recovery provides no fair-budget query savings, posterior-selected PEFT underperforms distill-then-convert PEFT (0.356 vs. 0.517), and free-running generation remains near chance. These results show that known-base, rich-output PEFT services can leak actionable structural and version information, while closed-set confidence alone does not establish universal adapter recovery.

翻译：暂无翻译