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Java (de)serialization is prone to causing security-critical vulnerabilitiesthat attackers can invoke existing methods (gadgets) on the application'sclasspath to construct a gadget chain to perform malicious behaviors. Severaltechniques have been proposed to statically identify suspicious gadget chainsand dynamically generate injection objects for fuzzing. However, due to theirincomplete support for dynamic program features (e.g., Java runtimepolymorphism) and ineffective injection object generation for fuzzing, theexisting techniques are still far from satisfactory.  In this paper, we first performed an empirical study to investigate thecharacteristics of Java deserialization vulnerabilities based on our manuallycollected 86 publicly known gadget chains. The empirical results show that 1)Java deserialization gadgets are usually exploited by abusing runtimepolymorphism, which enables attackers to reuse serializable overridden methods;and 2) attackers usually invoke exploitable overridden methods (gadgets) viadynamic binding to generate injection objects for gadget chain construction.Based on our empirical findings, we propose a novel gadget chain miningapproach, \emph{GCMiner}, which captures both explicit and implicit methodcalls to identify more gadget chains, and adopts an overriding-guided objectgeneration approach to generate valid injection objects for fuzzing. Theevaluation results show that \emph{GCMiner} significantly outperforms thestate-of-the-art techniques, and discovers 56 unique gadget chains that cannotbe identified by the baseline approaches.

Improving Java Deserialization Gadget Chain Mining via Overriding-Guided Object Generation