AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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LLM agents now act as delegated communication intermediaries: they read private context, draft messages, and send externally visible emails on behalf of a user. Existing checker-repair defenses can reduce inappropriate disclosure, but they rely on repeated calls to a remote action LLM (the model that drafts and repairs...
Reliable recovery of missing measurements is important for monitoring and analysis in energy time-series systems, where fine-grained measurements may contain sensitive temporal information. Diffusion models trained with differentially private stochastic gradient descent (DP-SGD) provide a promising framework for privac...
The interface is implemented with a lightweight representation-level pre-encoder gate that assigns sigmoid scores to representation units, and the usage penalty reduces average admission scores while keeping forecasting errors close to the unpenalized TimeXer setting.
CIPL (Channel Inversion for Privacy Leakage), a channel-aware evaluation framework for black-box privacy leakage in LLM agents, is presented and provides a common framework for comparing how internal sensitive dependence is realized as externally recoverable leakage across heterogeneous agent pipelines.
This work presents PAPC, a platform-mediated mechanism that intercepts information-moving events before they update shared state or external channels and positions event-level mediation as a platform-governance primitive for agent-mediated online work.
Privacy exposure displacement, the mismatch between a local evaluation proxy and target-grounded session exposure, and ASLEval, an authorization-aware framework that pre-registers a hidden target set, measures all declared visible exits, and reserves internal traces for diagnosis are introduced.
This work proposes Trustworthy Virtual Memory (TVM), a closed-loop runtime that keeps exact-value state local while presenting a protected view to the remote model, preserving surrounding task-relevant context.
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