AgentGuard detects package confusion attacks via multi-agent hybrid name search plus fused metadata-content ML analysis, raising precision 12-49% and cutting false positives 11-35% versus baselines on ConfuDB and NeupaneDB.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
AML outperforms cross-validated baselines including CNNs on 50-2000 example image datasets and is comparable to XGBoost/LightGBM on tabular data using only training data and no task-dependent hyperparameters.
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AgentGuard: A Multi-Agent Framework for Robust Package Confusion Detection via Hybrid Search and Metadata-Content Fusion
AgentGuard detects package confusion attacks via multi-agent hybrid name search plus fused metadata-content ML analysis, raising precision 12-49% and cutting false positives 11-35% versus baselines on ConfuDB and NeupaneDB.
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Algebraic Machine Learning for Small-to-Medium Datasets Is Competitive against Strong Standard Baselines
AML outperforms cross-validated baselines including CNNs on 50-2000 example image datasets and is comparable to XGBoost/LightGBM on tabular data using only training data and no task-dependent hyperparameters.