arXiv:2605.29251v1 Announce Type: new Abstract: 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 paradig
As large language models transition from generative engines to agents with execution privileges, the security implications of autonomous AI are becoming a pressing concern.
This research addresses a fundamental crisis in AI security by proposing a new paradigm for 'provably secure' agent guardrails, moving beyond currently insufficient empirical methods.
The shift from probabilistic to deterministic security mechanisms for AI agents would fundamentally alter the trust model and deployment potential of autonomous AI systems.
- · AI developers
- · Cybersecurity industry
- · Critical infrastructure
- · AI-powered automation
- · Malicious actors
- · Systems relying on empirical guardrails
- · Bad actors exploiting AI vulnerabilities
Enterprise and governmental adoption of AI agents accelerates due to increased security assurances.
New regulatory frameworks emerge, mandating provably secure guardrails for AI systems in sensitive applications.
The development of 'AI security as a service' becomes a major market segment, offering verified security solutions for diverse AI agent deployments.
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Read at arXiv cs.AI