Tianchang Yang (Pennsylvania State University), Sathiyajith K S (Pennsylvania State University), Ashwin Senthil Arumugam (Pennsylvania State University), Syed Rafiul Hussain (Pennsylvania State University)

We present our work-in-progress on designing and implementing a black-box evolutionary fuzzer for REST APIs, specifically targeting 5G core networks that utilize a service-based architecture (SBA). Unlike existing tools that rely on static generation-based approaches, our approach progressively refines test inputs to explore deeper code regions in the target system. We incorporate a thorough analysis of the limited response message feedback available in black-box settings and employ a carefully crafted mutation method to generate effective state-aware test inputs. Evaluation of our current implementation has uncovered two previously unknown vulnerabilities in open-source 5G core network implementations, resulting in the assignment of two CVEs. Additionally, our approach already demonstrates superior performance compared to existing black-box fuzzing methods.

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Understanding reCAPTCHAv2 via a Large-Scale Live User Study

Andrew Searles (University of California Irvine), Renascence Tarafder Prapty (University of California Irvine), Gene Tsudik (University of California Irvine)

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Characterizing the Impact of Audio Deepfakes in the Presence...

Magdalena Pasternak (University of Florida), Kevin Warren (University of Florida), Daniel Olszewski (University of Florida), Susan Nittrouer (University of Florida), Patrick Traynor (University of Florida), Kevin Butler (University of Florida)

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DUMPLING: Fine-grained Differential JavaScript Engine Fuzzing

Liam Wachter (EPFL), Julian Gremminger (EPFL), Christian Wressnegger (Karlsruhe Institute of Technology (KIT)), Mathias Payer (EPFL), Flavio Toffalini (EPFL)

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