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cs.CR2026
CREBench: Evaluating Large Language Models in Cryptographic Binary Reverse Engineering
Baicheng Chen, Yu Wang, Ziheng Zhou +4
Reverse engineering (RE) is central to software security, particularly for cryptographic programs that handle sensitive data and are highly prone to vulnerabilities. It supports cr…
cs.CR2026
VirtualCrime: Evaluating the Criminal Potential and Agentic Behaviors of Large Language Models via Sandbox Simulation
Yilin Tang, Yu Wang, Lanlan Qiu +4
Large language models (LLMs) have shown strong capabilities in multi-step decision-making, and are increasingly integrated into real-world agentic applications. While existing safe…
cs.CR2025★ 1 cited
AICrypto: Evaluating Cryptography Capabilities of Large Language Models
Yu Wang, Yijian Liu, Liheng Ji +11
We build \textbf{AICrypto}, a comprehensive benchmark designed to evaluate the cryptography capabilities of large language models (LLMs). The benchmark comprises 135 multiple-choic…