Efficient Detection of Java Deserialization Gadget Chains via Bottom-up Gadget Search and Dataflow-aided Payload Construction

Java Object Injection (JOI) is a severe type of vulnerability affecting Java deserialization, which allows adversaries to inject a well-crafted, serialized object, thus triggering a series of chained internal methods (called gadgets) and then achieving attack consequences such as Remote Code Execution (RCE). Prior works studied the problem of detecting and chaining gadgets for JOI vulnerability using static search for possible gadget chains and dynamic construction of payload via fuzzing. However, prior works face two following challenges: (i) path explosion in static gadget search and (ii) a lack of fine-grained object relations connected via object fields in dynamic payload construction.In this paper, we design and implement a novel Java deserialization gadget detection framework, called JDD. On one hand, JDD solves the static path explosion problem by a bottom-up approach, which first looks for gadget fragments and then chains gadget fragments from sinks to sources. The approach reduces maximum static search time from exponential to polynomial, i.e., from O(eMn) to O(M2n3 + enM), where n is the number of dynamic function calls in a gadget chain, M is the average number of dynamic function call candidates, and e is the number of entry points. On the other hand, JDD constructs a so-called Injection Object Construction Diagram (IOCD), which models the dataflow dependencies between injection objects’ fields to facilitate dynamic fuzzing. Our evaluation of JDD upon six real-world Java applications reveals 127 zero-day, exploitable gadget chains with six Common Vulnerabilities and Exposures (CVE) identifiers assigned. We also responsibly reported these vulnerabilities to application developers and obtained their acknowledgments and confirmations.