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InfoResearchPeer-reviewed
RLAgent-GSSTI: Automated Grey-Box SSTI Vulnerability Detection Based on Reinforcement Learning Agent
- Published
- Record updated
Summary
RLAgent-GSSTI is a grey-box framework for detecting Server-Side Template Injection (SSTI) that combines a Rainbow-and-CodeQL risk prediction method with hierarchical reinforcement learning agents that generate attack payloads. In testing, its static risk prediction reached a 28.10% false negative rate, while several advanced static analysis tools reached 100.00%. The injection detection method outperformed advanced scanners in filtered environments and enabled automated sandbox escapes for remote code execution, with an FNR as low as 11.28%.