Active Session Defense (ASD) is a field‑defining safety architecture designed to protect users during the most vulnerable state of device operation: active, unlocked sessions. Unlike content‑layer safety systems, ASD operates at the session layer, using non‑content, privacy‑preserving, on‑device inference to detect harmful, coercive, or hostile possession conditions in real time.
This Unified Master Paper establishes the field, defines its theoretical foundations, specifies the Engines of Protection, and documents the ten‑module architecture that operationalizes ASD.
Active Session Defense (ASD) is a session‑layer safety architecture focused on:
Continuous Possession Assurance
Hostile‑Possession Response
Session‑Level Protection
Exposure Minimization
Privacy‑Bounded Inference
Early Harm‑State Detection
ASD is not a product, feature, or app.
It is a new field of mobile safety research, formally published, timestamped, and indexed across OSF, Zenodo, and ResearchGate.
ASD is powered by three Engines of Protection:
Establishes a session‑layer behavioral baseline using non‑content signals.
Detects harm‑state deviation — measurable divergence from the user’s normal session‑layer behavior.
Executes privacy‑preserving, user‑protective responses when hostile‑possession conditions are detected.
These engines operate entirely on‑device, without cloud inference, content analysis, or data retention.
Ensures the session is controlled by the legitimate user.
Detects coercion, duress, or hostile possession conditions.
Reduces sensitive‑data exposure during high‑risk sessions.
Creates a privacy‑bounded session‑layer baseline.
Identifies early signals of unsafe or coerced device use.
Coordinates protective actions based on deviation severity.
Ensures ASD never analyzes content or retains user data.
All inference occurs locally, preserving user privacy.
Implements safety actions without interrupting device function.
Provides protection without logs, traces, or forensic artifacts.
ASD anticipates harm by detecting pre‑harm deviation signals before the user is fully compromised.
A measurable divergence from the user’s normal session‑layer behavior that indicates rising risk.
ASD uses non‑content signals only, ensuring:
No message scanning
No cloud inference
No data retention
No user profiling
ASD operates below content, focusing on:
Possession
Control
Coercion
Safety state
This makes ASD compatible with all platforms and all content‑layer systems.
Can session‑layer behavioral deviation be used to detect hostile‑possession conditions?
Can on‑device, non‑content inference provide early warning signals of harm?
Can a safety architecture operate without collecting or analyzing user content?
A privacy‑preserving, session‑layer architecture can detect harm‑state deviation earlier and more safely than content‑layer systems.
This project is a conceptual and architectural framework, not an empirical study.
No data are collected, measured, analyzed, or transformed.
The study design consists of:
Conceptual modeling
Architectural specification
Theoretical analysis
Standards formation
No human subjects. No datasets. No experiments.
None — ASD is non‑experimental.
None — no data are collected.
None — no composite measures exist.
None — no participants or datasets.
None — no data to transform.
None — no statistical inference is performed.
Not applicable.
Not applicable.
None — ASD is conceptual and architectural.
10.17605/OSF.IO/GT9UC
10.5281/zenodo.20083573
10.13140/RG.2.2.16702.04169
https://www.researchgate.net/profile/Kurt-Sparks
Sparks, K. (2026). Active Session Defense: Unified Master Paper (Field Definition & Architecture). OSF. https://doi.org/10.17605/OSF.IO/GT9UC
ActiveSessionDefense.com
The authoritative home of the ASD field, research program, and public‑safety mission