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Recurring vulnerabilities widely exist and remain undetected in real-world systems, which are often resulted from reused code base or shared code logic. However, the potentially small differences between vulnerable functions and their patched functions as well as the possibly large differences between vulnerable functions and target functions to be detected bring challenges to clone-based and function matching-based approaches to identify these recurring vulnerabilities, i.e., causing high false positives and false negatives. In this paper, we propose a novel approach to detect recurring vulnerabilities with low false positives and low false negatives. We first use our novel program slicing to extract vulnerability and patch signatures from vulnerable function and its patched function at syntactic and semantic levels. Then a target function is identified as potentially vulnerable if it matches the vulnerability signature but does not match the patch signature. We implement our approach in a tool named MVP. Our evaluation on ten open-source systems has shown that, i) MVP significantly outperformed state-of-the-art clone-based and function matching-based recurring vulnerability detection approaches; ii) MVP detected recurring vulnerabilities that cannot be detected by general-purpose vulnerability detection approaches, i.e., two learning-based approaches and two commercial tools; and iii) MVP has detected 97 new vulnerabilities with 23 CVE identifiers assigned.