most citedWhy Are Web AI Agents More Vulnerable Than Standalone LLMs? A Security Analysis

1 citations · 1 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CR2026

PatchBench: Evaluating AI Agents for Vulnerability Patching

Chihao Shen, Jiacheng Li, Aastha Mahajan +3

AI agents have recently demonstrated strong performance in automated vulnerability patching. However, existing evaluations often validate a patch only by testing whether the provid…

cs.CR2026

Symbolon: Symbolic Execution by Learning Code Transformation

Jie Zhu, Penghui Li, Zhongxuan Li +4

Symbolic execution is a powerful program analysis technique with broad applications, such as vulnerability detection, security testing, and malware analysis. However, this techniqu…

cs.CV2026

Flash-BoN: Instant Drafts for Inference-Time Scaling in Diffusion Models

Ruchit Rawal, Reza Shirkavand, Sayak Paul +5

Inference-time scaling for text-to-image generation has progressed from simple Best-of- (BoN) sampling to guided search methods that verify and steer candidate trajectories at i…

cs.SE2025

Benchmarking Correctness and Security in Multi-Turn Code Generation

Ruchit Rawal, Jeffrey Yang Fan Chiang, Chihao Shen +4

AI coding assistants powered by large language models (LLMs) have transformed software development, significantly boosting productivity. While existing benchmarks evaluate the corr…

cs.CR2025

Locus: Agentic Predicate Synthesis for Directed Fuzzing

Jie Zhu, Chihao Shen, Ziyang Li +3

Directed fuzzing aims to find program inputs that lead to specified target program states. It has broad applications, such as debugging system crashes, confirming reported bugs, an…

cs.LG2025★ 1 cited

Why Are Web AI Agents More Vulnerable Than Standalone LLMs? A Security Analysis

Jeffrey Yang Fan Chiang, Seungjae Lee, Jia-Bin Huang +2

Recent advancements in Web AI agents have demonstrated remarkable capabilities in addressing complex web navigation tasks. However, emerging research shows that these agents exhibi…