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Multiple case-controlled studies have shown that analyzing fragmentation patterns in plasma cell-free DNA (cfDNA) can distinguish individuals with cancer from healthy controls. However, there have been few studies that investigate various types of cfDNA fragmentomics patterns in individuals with other diseases. We therefore developed a comprehensive statistic, called fragmentation signatures, that integrates the distributions of fragment positioning, fragment length, and fragment end-motifs in cfDNA. We found that individuals with venous thromboembolism, systemic lupus erythematosus, dermatomyositis, or scleroderma have cfDNA fragmentation signatures that closely resemble those found in individuals with advanced cancers. Furthermore, these signatures were highly correlated with increases in inflammatory markers in the blood. We demonstrate that these similarities in fragmentation signatures lead to high rates of false positives in individuals with autoimmune or vascular disease when evaluated using conventional binary classification approaches for multicancer earlier detection (MCED). To address this issue, we introduced a multiclass approach for MCED that integrates fragmentation signatures with protein biomarkers and achieves improved specificity in individuals with autoimmune or vascular disease while maintaining high sensitivity. Though these data put substantial limitations on the specificity of fragmentomics-based tests for cancer diagnostics, they also offer ways to improve the interpretability of such tests. Moreover, we expect these results will lead to a better understanding of the process—most likely inflammatory—from which abnormal fragmentation signatures are derived.