Java deserialization vulnerabilities (JDVs) enable attackers to execute arbitrary code by crafting malicious serialized objects that trigger sequences of method calls (gadget chains) leading to dangerous operations. Existing detection approaches face a fundamental trade-off: static analysis achieves scalability but suffers from high false positives due to infeasible paths and imprecision with dynamic features like reflection; dynamic validation reduces false positives but incurs prohibitive costs and fails to explore deep exploitation chains.

We present GadgetHunter, a neuro-symbolic JDV detector that combines scalable static analysis with targeted LLM reasoning and JDV exploitation-oriented constraint solving. Our approach partitions gadget chains into regions based on analyzability: statically resolvable segments are processed via interprocedural taint analysis, while dynamic boundaries are delegated to LLMs for semantic validation. We then extract critical constraints from each gadget and compose them into SMT formulas to determine chain feasibility through satisfiability solving. Evaluation on the ysoserial benchmark demonstrates that GadgetHunter reduces false negatives by up to 32% and false positives by 12-85% compared to state-of-the-art tools, while discovering 197 previously unknown gadget chains and rediscovering 4 recent CVEs. Our results show that combining symbolic reasoning with semantic understanding achieves both precision and practical impact in vulnerability detection.