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Java deserialization vulnerability is a severe threat in practice.Researchers have proposed static analysis solutions to locate candidatevulnerabilities and fuzzing solutions to generate proof-of-concept (PoC)serialized objects to trigger them. However, existing solutions have limitedeffectiveness and efficiency. In this paper, we propose a novel hybrid solutionODDFUZZ to efficiently discover Java deserialization vulnerabilities. First,ODDFUZZ performs lightweight static taint analysis to identify candidate gadgetchains that may cause deserialization vulner-abilities. In this step, ODDFUZZtries to locate all candidates and avoid false negatives. Then, ODDFUZZperforms directed greybox fuzzing (DGF) to explore those candidates andgenerate PoC testcases to mitigate false positives. Specifically, ODDFUZZapplies a structure-aware seed generation method to guarantee the validity ofthe testcases, and adopts a novel hybrid feedback and a step-forward strategyto guide the directed fuzzing. We implemented a prototype of ODDFUZZ andevaluated it on the popular Java deserialization repository ysoserial. Resultsshow that, ODDFUZZ could discover 16 out of 34 known gadget chains, while twostate-of-the-art baselines only identify three of them. In addition, weevaluated ODDFUZZ on real-world applications including Oracle WebLogic Server,Apache Dubbo, Sonatype Nexus, and protostuff, and found six previouslyunreported exploitable gadget chains with five CVEs assigned.

ODDFUZZ: Discovering Java Deserialization Vulnerabilities via Structure-Aware Directed Greybox Fuzzing