Researchers have developed a new framework for evaluating the security risks of agentic AI systems, which are increasingly being deployed in production environments. This black-box approach, named SAGE-RT, uses a taxonomy of seven risk domains to automatically generate adversarial scenarios. When tested on CrewAI and AutoGen architectures with various base models, the framework revealed significant governance and privacy risks, with agent behavior vulnerabilities reaching up to 85%. The system's effectiveness was validated by human evaluators and LLM judges, demonstrating its capability to identify critical vulnerabilities without requiring privileged access. AI
IMPACT This research provides a scalable method for identifying critical vulnerabilities in agentic AI, potentially accelerating the safe deployment of these systems.
RANK_REASON The cluster contains an academic paper detailing a new framework for evaluating AI systems. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- arXiv
- AutoGen
- CatalyzeX
- CrewAI
- DagsHub
- Gotit.pub
- Hugging Face
- Influence Flower
- SAGE-RT
- ScienceCast
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