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20242026
most citedLanguage model developers should report train-test overlap

3 citations · 6 across the 7 of their papers we have counts for

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6 papers · 1 filter

cs.CR2026

MobileCybench: Evaluating Agent Vulnerability Discovery via Executable Probes

Andy K. Zhang, Ava Huang, Joey Ji +21

AI agents now report vulnerabilities faster than maintainers can review them. Reports often depend on security properties specific to the application, and require considerable huma…

cs.CR2026

CTF for education

Yi Lyu, Luke Dotson, Nic Draves +1

In this paper, we take a close look at how CTF can be used in cybersecurity education. We divide the CTF competitions into four different categories, which are attack-based CTFs, d…

cs.CR2025

SeedAIchemy: LLM-Driven Seed Corpus Generation for Fuzzing

Aidan Wen, Norah A. Alzahrani, Jingzhi Jiang +5

We introduce SeedAIchemy, an automated LLM-driven corpus generation tool that makes it easier for developers to implement fuzzing effectively. SeedAIchemy consists of five modules…

cs.CR2025

BountyBench: Dollar Impact of AI Agent Attackers and Defenders on Real-World Cybersecurity Systems

Andy K. Zhang, Joey Ji, Celeste Menders +31

AI agents have the potential to significantly alter the cybersecurity landscape. Here, we introduce the first framework to capture offensive and defensive cyber-capabilities in evo…

cs.CR2025★ 1 cited

Frontier AI's Impact on the Cybersecurity Landscape

Yujin Potter, Wenbo Guo, Zhun Wang +6

The impact of frontier AI (i.e., AI agents and foundation models) in cybersecurity is rapidly increasing. In this paper, we comprehensively analyze this trend through multiple aspe…

cs.CR2024★ 1 cited

Cybench: A Framework for Evaluating Cybersecurity Capabilities and Risks of Language Models

Andy K. Zhang, Neil Perry, Riya Dulepet +24

Language Model (LM) agents for cybersecurity that are capable of autonomously identifying vulnerabilities and executing exploits have potential to cause real-world impact. Policyma…