arXiv:2605.28617v1 Announce Type: new Abstract: LLM agents increasingly act by writing code, yet a split persists between the runtime that drives the agent and the code the model writes. The runtime owns the loop, context, and control flow, and the model has little say over any of them. Letting model-written code shape the runtime itself would make agents more expressive, but it would also sharpen safety problems. A model can be diverted by a prompt injection, call the wrong tool, or fail partway and leave an inconsistent state, and each such failure reaches further when the code shapes the ru
The rapid advancement and deployment of LLM agents across various applications necessitate robust solutions for safety, control, and reliability as their capabilities expand.
The proposed LACUNA system directly addresses the critical challenge of ensuring safety and control in increasingly autonomous AI agents, fostering greater trust and enabling broader adoption.
Agents can now incorporate model-written code shaping the runtime itself, moving towards more expressive but potentially riskier AI systems that require new safety frameworks.
- · AI Agent Developers
- · Companies adopting AI Agents
- · AI Safety Researchers
- · Malicious actors
- · Systems vulnerable to prompt injection
- · Undermoderating AI platforms
Increased expressivity and capability of AI agents, potentially leading to more complex and powerful autonomous systems.
New standards and regulatory frameworks for AI agent safety become necessary as agents gain more control over their operating environments.
The development of 'meta-agents' capable of self-modifying their core operational logic, requiring advanced oversight mechanisms.
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Read at arXiv cs.AI