1 citations · 2 across the 7 of their papers we have counts for
7 papers
Beyond Takedown: Measuring Malicious Go Module Persistence in the Wild
Minjae Bae, Carter Yagemann
We measure an automation-based supply chain campaign in the Go ecosystem. The attackers repackage legitimate Go modules under attacker-controlled owners, and embed them with obfusc…
Burnyard: Future of Malware Analysis
Rama Ramana Sharma Parnandi, Carter Yagemann
Malware analysis is a critical aspect of modern cybersecurity. The prevailing industry practice, sandboxing, involves executing suspicious binaries within isolated virtual machines…
EXHIB: A Benchmark for Realistic and Diverse Evaluation of Function Similarity in the Wild
Yiming Fan, Jun Yeon Won, Ding Zhu +4
Binary Function Similarity Detection (BFSD) is a core problem in software security, supporting tasks such as vulnerability analysis, malware classification, and patch provenance. I…
A Practical Framework for Evaluating Medical AI Security: Reproducible Assessment of Jailbreaking and Privacy Vulnerabilities Across Clinical Specialties
Jinghao Wang, Ping Zhang, Carter Yagemann
Medical Large Language Models (LLMs) are increasingly deployed for clinical decision support across diverse specialties, yet systematic evaluation of their robustness to adversaria…
SoK: A Beginner-Friendly Introduction to Fault Injection Attacks
Christopher Simon Liu, Fan Wang, Patrick Gould +1
Fault Injection is the study of observing how systems behave under unusual stress, environmental or otherwise. In practice, fault injection involves testing the limits of computer…
C2RUST-BENCH: A Minimized, Representative Dataset for C-to-Rust Transpilation Evaluation
Melih Sirlanci, Carter Yagemann, Zhiqiang Lin
Despite the effort in vulnerability detection over the last two decades, memory safety vulnerabilities continue to be a critical problem. Recent reports suggest that the key soluti…
To believe or not to believe: Validating explanation fidelity for dynamic malware analysis
Li Chen, Carter Yagemann, Evan Downing
Converting malware into images followed by vision-based deep learning algorithms has shown superior threat detection efficacy compared with classical machine learning algorithms. W…