TRACER: Temporal retrieval-augmented contextual evaluator for robustness in ML training

초록

Security vulnerabilities that arise during the training phase of machine learning models often emerge progressively and stealthily, making it difficult to fully analyze their causes and evolution through single-point performance evaluation alone. Existing security analysis frameworks primarily focus on one-off attack execution and quantitative metric reporting, which limits their ability to systematically interpret cumulative state changes and vulnerability manifestation patterns throughout the training process. To address this limitation, this study proposes an autonomous vulnerability analysis agent driven by a Large Language Model. The proposed framework employs an iterative analysis loop that integrates execution logs, step-wise analytical memory, and externally retrieved knowledge to track state transitions and anomalous signs during training, and to interpret the underlying mechanisms and causal relationships of vulnerability emergence. Experimental results across diverse training-phase attack scenarios show that the proposed agent is not restricted to a specific attack type, but can identify the distinct performance degradation mechanisms induced by different threat models and consistently infer their causal relationships based on execution logs and retrieved knowledge. In addition, quantitative reliability evaluations indicate that the generated reports are generally consistent with actual observations and support evidence-grounded, coherent analysis. Ultimately, the proposed framework provides an explainable and automated analytical foundation for security vulnerability analysis in the training phase of AI systems.

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