LLM agents accumulate valuable procedural knowledge during task execution, but these traces are session-bound, model-specific, and often entangled with sensitive content, making them unsafe to pool directly across users. Prior approaches to skill discovery often sacrifice cross-user generalization, iterative quality, or compliance-oriented sharing. We propose SkillLadder, a multi-user framework that distills local execution traces into a shared skill library without funneling raw data to a central server. The system evolves user-contributed traces into a multi-level hierarchy (L0–L3) using five quality gates that prevent regression below the strongest parent skill under a judge-defined score. To ensure safe sharing, SkillLadder distributes only level-≥1 skills that have undergone entity scrubbing and lineage sanitization. On 𝜏²-bench, where each isolated task execution simulates one contributing user—a deliberately conservative proxy for many small local contributors, not a model of longitudinal human users—evolved skills improve a fixed Claude Sonnet 4.6 agent in three of four domains and generalize to simulated users running different agent models. Compliance audits verify that entity retention reaches zero by L3 and source-attribution accuracy is roughly halved, substantially reducing measured sharing risks.

Cloud-hosted vision-language models (VLMs) may be silently fine-tuned, poisoned, compressed, or replaced after deployment, motivating black-box fingerprint verification. RESF tests response distributions but requires estimating the service temperature, making its reliability sensitive to estimation error. We propose Latent Visual Sensitive Fingerprinting (LVSF), based on a local Pearson-sensitivity decomposition of the conditional top-k distribution of the first generated token. This decomposition separates observable output shifts from input-dependent exposure to parameter changes. Modeling tampering-induced relative top-k logit shifts as isotropic, we show that a uniform top-k anchor maximizes average sensitivity. Accordingly, LVSF equalizes recorded top-k logits, maintains a margin from out-of-support tokens, and maximizes full-parameter Fisher sensitivity, yielding a reference distribution invariant to positive temperature scaling. These principles apply to both LLMs and VLMs. For VLMs, LVSF fixes a natural-language query and optimizes an image in a pretrained VAE latent space to produce natural-looking fingerprints. Repeated API queries detect excess out-of-support first tokens or shifts in conditional in-support frequencies. Across four VLMs and multiple tampering types, LVSF achieves consistently high detection rates with no observed false positives and substantially higher detection rates than evaluated baselines on Qwen2.5-VL-7B-Instruct.