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This paper introduces a new security paradigm for AI agents, Proof-Constrained Action (ePCA), that leverages the limitations of logical reasoning to enforce safety. ePCA forces agents to formalize intentions into first-order logic constraints before execution, bypassing reliance on semantic guardrails vulnerable to decoupling attacks. Empirical evaluations in 2D dynamic adversarial systems demonstrate ePCA achieves zero attack success and false positive rates with low latency, offering a formally verifiable defense.
Semantic guardrails can't stop AI agents from going rogue, but formal logic constraints might.
As large language models transition from bounded generative engines to agents with expansive execution privileges, AI going out of control precipitates a fundamental crisis in artificial intelligence security. Existing defense architectures heavily rely on empirical semantic guardrails and probabilistic large model adjudicators, mechanisms that fail to provide deterministic security lower bounds when facing complex semantic symbol decoupling attacks. To overcome this empirical semantic guardrail dilemma, this paper proposes a new security paradigm for agents based on the fundamental limitations of logical reasoning. Based on this paradigm, we further introduce an executable Proof-Constrained Action (ePCA) framework with a neural symbolic isolation architecture. This framework abandons semantic trust in natural language, forcing agents to losslessly formalize their intentions into first-order logical mathematical constraints before performing physical operations. Empirical evaluations of macroscopic and microscopic two-dimensional dynamic adversarial systems demonstrate that our formal verification mechanism achieves zero attack success rate and zero false positive rate across the evaluated scenarios, with extremely low computational latency. This research provides a conditional formal foundation under explicit system assumptions and an engineering paradigm for constructing the underlying defense foundation for future intelligent systems.
