arXiv:2606.08531v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly evolving from simple text-based interaction systems into LLM agents that can maintain memory, use tools, access external environments, and execute tasks. As their capabilities and autonomy expand, the safety risks they face also become more diverse. Existing evaluations often rely on manually written scenarios, static prompts, or final-output judgments, making it difficult to capture the diverse risks that agents may face during task execution. We introduce VESTA, a fully automated scenario generation
As LLMs evolve into more autonomous agents maintaining memory and interacting with environments, the complexity and diversity of safety risks have increased beyond what manual evaluations can address.
The development of automated safety evaluation frameworks like VESTA is critical for ensuring the safe and robust deployment of increasingly capable AI agents across various domains.
The ability to automatically generate diverse and dynamic scenarios for testing LLM agents will allow for more comprehensive safety assessments, moving beyond static prompts and manual scenario creation.
- · AI Safety Researchers
- · LLM Agent Developers
- · AI-reliant Industries
- · Under-tested AI Agents
- · Manual Testing Paradigms
VESTA enables more rigorous and scalable safety testing of advanced LLM agents.
Improved safety frameworks could accelerate the responsible deployment and public acceptance of autonomous AI agents.
Standardisation around automated safety evaluation might influence regulatory approaches and certification requirements for AI agent systems.
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Read at arXiv cs.AI