Security-Augmented Use Case Flow Refinement for LLM-Based Agentic Systems
Unlike conventional software systems and large language models (LLMs) evaluated in isolation, LLM-based agentic systems introduce a compositional, workflow-level attack surface. Existing security analysis approaches provide limited support for identifying security omissions implicit in functional use case flows and deriving corresponding flow refinements for direct incorporation into use case specifications. We formulate the Security-Augmented Use Case Flow Refinement Task and propose MA-SAFR, a multi-agent framework that coordinates Red Team, Blue Team, and Critic agents for threat anchoring, security-branch construction, and validation. EvoGraphRAG adapts relation-aware retrieval through validation feedback, while Risk-Specific Security Guidance (RSSG) provides risk- and role-specific constraints. To support systematic evaluation of this task, we construct SAFR-Bench from OWASP and MITRE ATLAS, comprising 157 cases and 235 source-grounded threat–refinement pairs. Across three generation models, MA-SAFR improves all three metrics on average compared with the strongest corresponding baselines; pipeline recall and pipeline precision increase by 63.44% and 54.96%, respectively. Controlled analyses show that RSSG and feedback-based graph evolution within gEvoGraphRAG improve downstream threat-to-defense conversion. Despite remaining challenges in implicit-threat identification, multi-threat coverage, and defense alignment, the results show that MA-SAFR can support the construction of valid security-augmented use case flows.