Shielded Analysis: Certification and Characterization of Defensibility in Systems under Adversarial Interaction
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Abstract (arXiv)
Formal safety analysis determines whether a system admits a safe defense; adaptive evaluation characterizes the operating quality sustained under adversarial interaction. Both answers matter because systems with the same safety verdict can impose very different operational burdens. We introduce shielded analysis, a design-time framework that derives these answers from one encoded system while keeping the safety requirement and admissible threat model independently variable. It returns a defensibility certificate and a four-axis defensibility fingerprint spanning structural margin, shield latitude, and adaptive operating quality. Each axis is informative in its own right; their relationships show whether formal and operational assessments agree, diverge, or respond differently to system changes. We instantiate the framework for network defense on a reference segment and four controlled perturbations spanning topology, safety requirements, and adversary capabilities. Every configuration is certified defensible, yet two topology variants with nearly identical structural profiles sustain mean clean-host fractions of 22.7% and 80.7% under adaptive pressure. Shielded analysis turns a safety-game solution into a comparative instrument: it determines whether a defense exists, characterizes what that defense requires, and identifies which system changes strengthen it.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 33 references that are in the map. First posted on arXiv 11 Jun 2026.
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References · 9
- 2017Safe Reinforcement Learning via Shieldingcited by 449
- 2021Safe Multi-Agent Reinforcement Learning via Shieldingcited by 61
- 2021CybORG: A Gym for the Development of Autonomous Cyber Agentscited by 60
- 2022Safe Reinforcement Learning via Shielding under Partial Observabilitycited by 56
- 2015Shield Synthesis: Runtime Enforcement for Reactive Systemscited by 42
- 2025Probabilistic Shielding for Safe Reinforcement Learningcited by 12
- 2023Model-based Dynamic Shielding for Safe and Efficient Multi-Agent Reinforcement Learningcited by 11
- 2021Regstar: Efficient Strategy Synthesis for Adversarial Patrolling Gamescited by 2
- 2023Dynamic Adversarial Resource Allocation: the dDAB Gamecited by 1
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