3 citations · 6 across the 9 of their papers we have counts for
6 papers · 1 filter
Scam2Prompt: A Scalable Framework for Auditing Malicious Scam Endpoints in Production LLMs
Zhiyang Chen, Tara Saba, Xun Deng +2
Large Language Models have become critical to modern software development, but their reliance on uncurated web-scale datasets for training introduces a significant security risk: t…
Towards Repository-Level Program Verification with Large Language Models
Si Cheng Zhong, Xujie Si
Recent advancements in large language models (LLMs) suggest great promises in code and proof generations. However, scaling automated formal verification to real-world projects requ…
TypyBench: Evaluating LLM Type Inference for Untyped Python Repositories
Honghua Dong, Jiacheng Yang, Xun Deng +4
Type inference for dynamic languages like Python is a persistent challenge in software engineering. While large language models (LLMs) have shown promise in code understanding, the…
-Bench: Evaluating Conversational Agents in a Dual-Control Environment
Victor Barres, Honghua Dong, Soham Ray +2
Existing benchmarks for conversational AI agents simulate single-control environments, where only the AI agent can use tools to interact with the world, while the user remains a pa…
VerifyThisBench: Generating Code, Specifications, and Proofs All at Once
Xun Deng, Sicheng Zhong, Barış Bayazıt +3
Large language models (LLMs) have demonstrated remarkable progress in code generation, but many existing benchmarks are approaching saturation and offer little guarantee on the tru…
RAG-Verus: Repository-Level Program Verification with LLMs using Retrieval Augmented Generation
Sicheng Zhong, Jiading Zhu, Yifang Tian +1
Scaling automated formal verification to real-world projects requires resolving cross-module dependencies and global contexts, which are challenges overlooked by existing function-…