Java deserialization gadget chains are a well-researched critical software weakness. The vast majority of known gadget chains rely on gadgets from software dependencies. Furthermore, it has been shown that small code changes in dependencies have enabled these gadget chains. This makes gadget chain detection a purely reactive endeavor. Even if one dependency's deployment pipeline employs gadget chain detection, a gadget chain can still result from gadgets in other dependencies. In this work, we assess how likely small code changes are to enable a gadget chain. These changes could either be accidental or intentional as part of a supply chain attack. Specifically, we show that class serializability is a strongly fluctuating property over a dependency's evolution. Then, we investigate three change patterns by which an attacker could stealthily introduce gadgets into a dependency. We apply these patterns to 533 dependencies and run three state-of-the-art gadget chain detectors both on the original and the modified dependencies. The tools detect that applying the modification patterns can activate/inject gadget chains in 26.08% of the dependencies we selected. Finally, we verify the newly detected chains. As such, we identify dormant gadget chains in 53 dependencies that could be added through minor code modifications. This both shows that Java deserialization gadget chains are a broad liability to software and proves dormant gadget chains as a lucrative supply chain attack vector.

Fuzzing is a well-established technique for detecting bugs and vulnerabilities. With the surge of fuzzers and fuzzer platforms being developed such as AFL and OSSFuzz rises the necessity to benchmark these tools' performance. A common problem is that vulnerability benchmarks are based on bugs in old software releases. For this very reason, Magma introduced the notion of forward-porting to reintroduce vulnerable code in current software releases. While their results are promising, the state-of-the-art lacks an update on the maintainability of this approach over time. Indeed, adding the vulnerable code to a recent software version might either break its functionality or make the vulnerable code no longer reachable. We characterise the challenges with forward-porting by reassessing the portability of Magma's CVEs four years after its release and manually reintroducing the vulnerabilities in the current software versions. We find the straightforward process efficient for 17 of the 32 CVEs in our study. We further investigate why a trivial forward-porting process fails in the 15 other CVEs. This involves identifying the commits breaking the forward-porting process and reverting them in addition to the bug fix. While we manage to complete the process for nine of these CVEs, we provide an update on all 15 and explain the challenges we have been confronted with in this process. Thereby, we give the basis for future work towards a sustainable forward-ported fuzzing benchmark.

Abstract Detecting vulnerabilities in software is a constant race between development teams and potential attackers. While many static and dynamic approaches have focused on regularly analyzing the software in its entirety, a recent research direction has focused on the analysis of changes that are applied to the code. VCCFinder is a seminal approach in the literature that builds on machine learning to automatically detect whether an incoming commit will introduce some vulnerabilities. Given the influence of VCCFinder in the literature, we undertake an investigation into its performance as a state-of-the-art system. To that end, we propose to attempt a replication study on the VCCFinder supervised learning approach. The insights of our failure to replicate the results reported in the original publication informed the design of a new approach to identify vulnerability-contributing commits based on a semi-supervised learning technique with an alternate feature set. We provide all artefacts and a clear description of this approach as a new reproducible baseline for advancing research on machine learning-based identification of vulnerability-introducing commits.