Youngjin Jin

Youngjin Jin, Hanna Kim, Kwanwoo Kim et al.

Existing multi-expert LLM systems gather diverse perspectives but combine them through simple aggregation, obscuring which arguments drove the final decision. We introduce ARGORA, a framework that organizes multi-expert discussions into explicit argumentation graphs showing which arguments support or attack each other. By casting these graphs as causal models, ARGORA can systematically remove individual arguments and recompute outcomes, identifying which reasoning chains were necessary and whether decisions would change under targeted modifications. We further introduce a correction mechanism that aligns internal reasoning with external judgments when they disagree. Across diverse benchmarks and an open-ended use case, ARGORA achieves competitive accuracy and demonstrates corrective behavior: when experts initially disagree, the framework resolves disputes toward correct answers more often than it introduces new errors, while providing causal diagnostics of decisive arguments.

Jaehan Kim, Minkyoo Song, Minjae Seo et al.

Credential stuffing attacks have caused significant harm to online users who frequently reuse passwords across multiple websites. While prior research has attempted to detect users with reused passwords or identify malicious login attempts, existing methods often compromise usability by restricting password creation or website access, and their reliance on complex account-sharing mechanisms hinders real-world deployment. To address these limitations, we propose PassREfinder-FL, a novel framework that predicts credential stuffing risks across websites. We introduce the concept of password reuse relations -- defined as the likelihood of users reusing passwords between websites -- and represent them as edges in a website graph. Using graph neural networks (GNNs), we perform a link prediction task to assess credential reuse risk between sites. Our approach scales to a large number of arbitrary websites by incorporating public website information and linking newly observed websites as nodes in the graph. To preserve user privacy, we extend PassREfinder-FL with a federated learning (FL) approach that eliminates the need to share user sensitive information across administrators. Evaluation on a real-world dataset of 360 million breached accounts from 22,378 websites shows that PassREfinder-FL achieves an F1-score of 0.9153 in the FL setting. We further validate that our FL-based GNN achieves a 4-11% performance improvement over other state-of-the-art GNN models through an ablation study. Finally, we demonstrate that the predicted results can be used to quantify password reuse likelihood as actionable risk scores.