From the 1 of 6 linked papers with an AI index.
6 papers
HoF-Bench: Rediscovering Real AI-Discovered CVEs Without Frontier Models
Petr Simecek, Elnaz Babayeva, Jiri Balhar +23
The paper presents HoF-Bench, a benchmark of 95 AI‑discovered CVEs from open‑source projects, and evaluates several LLM‑based vulnerability detectors, finding that only a minimal a…
EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability
Andrea Ponte, Daniel Gibert, Matous Kozak +5
Due to the lack of systematic evaluations, we are not yet able to determine which AI-based Windows malware detector to deploy in production, since existing evaluations (i) differ i…
Quality-Assured Fuzz Harness Generation via the Four Principles Framework
Ze Sheng, Dmitrijs Trizna, Luigino Camastra +3
Fuzz testing is the dominant technique for finding memory-safety vulnerabilities in C/C++ software, yet its effectiveness hinges on the quality of fuzz harnesses -- the programs th…
Updating Windows Malware Detectors: Balancing Robustness and Regression against Adversarial EXEmples
Matous Kozak, Luca Demetrio, Dmitrijs Trizna +1
Adversarial EXEmples are carefully-perturbed programs tailored to evade machine learning Windows malware detectors, with an ongoing effort to develop robust models able to address…
SLIFER: Investigating Performance and Robustness of Malware Detection Pipelines
Andrea Ponte, Dmitrijs Trizna, Luca Demetrio +3
As a result of decades of research, Windows malware detection is approached through a plethora of techniques. However, there is an ongoing mismatch between academia -- which pursue…
Robust Synthetic Data-Driven Detection of Living-Off-the-Land Reverse Shells
Dmitrijs Trizna, Luca Demetrio, Battista Biggio +1
Living-off-the-land (LOTL) techniques pose a significant challenge to security operations, exploiting legitimate tools to execute malicious commands that evade traditional detectio…