arXiv:2605.27488v1 Announce Type: cross Abstract: Agentic systems increasingly run user-authored orchestration code that invokes tools, spawns subtasks, and delegates work across machines and clouds. Although this high agency is productive, it creates a security problem: identity, authorization, provenance, and delegation are often pushed into application code, where they become difficult to enforce consistently and difficult to audit. We present \emph{Grimlock}, an \emph{Agent Guard} that restores separation of concerns by moving trust enforcement into the sandbox substrate while leaving agen
The proliferation of high-agency AI systems, particularly in enterprise and critical infrastructure, necessitates robust security measures that traditional application-level controls cannot adequately provide.
This development addresses a critical vulnerability in the expanding deployment of AI agents, ensuring secure and auditable operations for sophisticated and autonomous AI systems.
Security enforcement for AI agents moves from application code into the underlying system substrate, providing stronger guarantees and better auditability.
- · AI developers
- · Cloud providers
- · Cybersecurity firms
- · Enterprises adopting AI agents
- · Attackers targeting AI agents
- · Legacy security vendors
Enhanced trust and broader adoption of AI agent systems in sensitive applications.
Increased competition among security solutions for AI agent environments, leading to standardized secure agent architectures.
The development of 'security-first' AI agent frameworks that abstract away low-level enforcement details, accelerating AI deployment across critical sectors.
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Read at arXiv cs.AI