8 citations · 17 across the 16 of their papers we have counts for
16 papers
Vero: Can AI Agents Build Formally Verified Software Repositories?
Zhe Ye, Hantao Lou, Yuechun Sun +8
AI agents are increasingly used for programming, but do not provide any guarantee on the correctness of generated code. Verified code generation, in which an agent produces both an…
Agent Security Needs Redefinition through a Holistic Framework
Vincent Siu, Jingxuan He, Kyle Montgomery +3
Agent security is widely treated as a question about action content. Defenses ask whether an instruction looks malicious. Benchmarks ask whether an agent performs a harmful soundin…
CyberGym-E2E: Scalable Real-World Benchmark for AI Agents' End-to-End Cybersecurity Capabilities
Tianneng Shi, Robin Rheem, Dongwei Jiang +13
AI has the potential to transform cybersecurity by enabling systems that can autonomously detect, analyze, and remediate software vulnerabilities. However, existing cybersecurity e…
Generative Compilation: On-the-Fly Compiler Feedback as AI Generates Code
Niels Mündler-Sasahara, Hristo Venev, Dawn Song +2
Languages with rich static semantics, such as Rust, provide stronger guarantees for AI-generated code, but their strictness makes generation more difficult. Off-the-shelf compilers…
ExploitGym: Can AI Agents Turn Security Vulnerabilities into Real Attacks?
Zhun Wang, Nico Schiller, Hongwei Li +13
AI agents are rapidly gaining capabilities that could significantly reshape cybersecurity, making rigorous evaluation urgent. A critical capability is exploitation: turning a vulne…
SecPI: Secure Code Generation with Reasoning Models via Security Reasoning Internalization
Hao Wang, Niels Mündler, Mark Vero +3
Reasoning language models (RLMs) are increasingly used in programming. Yet, even state-of-the-art RLMs frequently introduce critical security vulnerabilities in generated code. Pri…
A Framework for Formalizing LLM Agent Security
Vincent Siu, Jingxuan He, Kyle Montgomery +4
Security in LLM agents is inherently contextual. For example, the same action taken by an agent may represent legitimate behavior or a security violation depending on whose instruc…
OpenSage: Self-programming Agent Generation Engine
Hongwei Li, Zhun Wang, Qinrun Dai +11
Agent development kits (ADKs) provide effective platforms and tooling for constructing agents, and their designs are critical to the constructed agents' performance, especially the…
SecCodePRM: A Process Reward Model for Code Security
Weichen Yu, Ravi Mangal, Yinyi Luo +4
Large Language Models are rapidly becoming core components of modern software development workflows, yet ensuring code security remains challenging. Existing vulnerability detectio…
PoseGen: In-Context LoRA Finetuning for Pose-Controllable Long Human Video Generation
Jingxuan He, Busheng Su, Finn Wong
Generating temporally coherent, long-duration videos with precise control over subject identity and movement remains a fundamental challenge for contemporary diffusion-based models…
CyberGym: Evaluating AI Agents' Real-World Cybersecurity Capabilities at Scale
Zhun Wang, Tianneng Shi, Jingxuan He +3
AI agents have significant potential to reshape cybersecurity, making a thorough assessment of their capabilities critical. However, existing evaluations fall short, because they a…
Type-Constrained Code Generation with Language Models
Niels Mündler, Jingxuan He, Hao Wang +3
Large language models (LLMs) have achieved notable success in code generation. However, they still frequently produce uncompilable output because their next-token inference procedu…
VERINA: Benchmarking Verifiable Code Generation
Zhe Ye, Zhengxu Yan, Jingxuan He +3
Large language models (LLMs) are increasingly integrated in software development, but ensuring correctness in LLM-generated code remains challenging and often requires costly manua…
Reasoning Models Can Be Effective Without Thinking
Wenjie Ma, Jingxuan He, Charlie Snell +3
Recent LLMs have significantly improved reasoning capabilities, primarily by including an explicit, lengthy Thinking process as part of generation. In this paper, we question wheth…
Progent: Securing AI Agents with Privilege Control
Tianneng Shi, Jingxuan He, Zhun Wang +4
AI agents interact with external environments through tool calls, exposing them to attacks like indirect prompt injection that can trigger unauthorized actions. Securing these agen…
Formal Mathematical Reasoning: A New Frontier in AI
Kaiyu Yang, Gabriel Poesia, Jingxuan He +4
AI for Mathematics (AI4Math) is not only intriguing intellectually but also crucial for AI-driven discovery in science, engineering, and beyond. Extensive efforts on AI4Math have m…