Beyond Runtime Enforcement: Shield Synthesis as Defensibility Analysis for Adversarial Networks
arXiv:2606.13621v1 Announce Type: new Abstract: Shielded reinforcement learning is typically presented as a runtime safety mechanism that compiles temporal-logic specifications into automata restricting an agent's actions. We argue this is the wrong product. The same automata-theoretic machinery -- specification compilation, product game construction, attractor computation, and winning-region extraction -- is better read as a design-time analytical instrument whose outputs are structural insights about a system rather than runtime constraints on a deployed agent. We instantiate this through a
The increasing complexity and deployment of AI systems, particularly in critical applications, necessitates robust safety and reliability frameworks beyond mere runtime checks.
This research reframes AI safety from reactive runtime enforcement to proactive design-time analysis, offering a more fundamental approach to building defensible and trustworthy autonomous systems.
The focus shifts from preventing immediate failures in deployed AI to architecting systems with inherent strategic robustness and predictability against adversarial actions from inception.
- · AI safety researchers
- · Developers of critical AI systems
- · Cybersecurity sector
- · Regulators
- · Adversarial AI developers
- · Systems relying solely on runtime patching
AI systems will be designed with a deeper understanding of their vulnerabilities and potential exploit pathways.
This framework could lead to the development of new tools and methodologies for auditing and certifying the safety of AI models.
Increased public and institutional trust in AI, potentially accelerating its adoption in sensitive governmental and industrial sectors.
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Read at arXiv cs.AI