The paper investigates how AI agents that generate smart contracts from plain‑English descriptions can lose intended behavior, a critical issue because smart contracts are immutable once deployed. The authors design an execution‑based evaluation that replays transactions on both the AI‑written contract and the original, measuring agreement and identifying discrepancies. They find that common grading tools miss many mistakes, and propose an elicitation‑first approach where the agent asks a short checklist of questions before coding, which significantly improves correctness across multiple AI models.
By Abhinav Goel, Agostino Capponi, Alfio Gliozzo, Chaitya Shah
arXiv:2606. 26216v1 Announce Type: cross Abstract: We present CyberChainBench, a benchmark for evaluating LLM-based agents on smart contract security across three complementary tasks: vulnerability detection, exploit generation, and patch synthesis.
By Jintao Huang, Fengqing Jiang, Radha Poovendran, Zhiqiang Lin
arXiv:2603. 26270v2 Announce Type: replace-cross Abstract: Smart contracts govern billions of dollars in decentralized finance (DeFi), yet automated vulnerability detection remains challenging because many vulnerabilities are tightly coupled with project-specific business logic.
By Ziqiao Kong, Wanxu Xia, Chong Wang, Yue Xue, Yi Lu, Pan Li, Shaohua Li, Zong Cao, Yang Liu
The paper presents a method that employs Large Language Models to automatically inject known vulnerabilities into Solidity smart contracts. Using a multi-step validation pipeline, the authors generate nearly 1,000 candidate contracts from real-world sources, ultimately confirming 32 vulnerable variants across 25 vulnerability types. These validated contracts are then used to evaluate the coverage of three static analysis tools, highlighting both complementary strengths and gaps in current detection approaches.
By Luca Migliaccio, Roberto Natella, Naghmeh Ivaki, Nuno Laranjeiro, Marco Vieira
AgentXploit is a two‑role auditing system that separates repository‑level attack‑path discovery from runtime exploitation for AI agents. The Analyzer Agent traces attacker‑controlled inputs to sensitive operations and records candidate attack paths, while the Exploiter Agent turns these paths into concrete attacks and refines them using runtime feedback. The system is evaluated on AgentXploit‑Bench, a benchmark of 72 reproducible vulnerabilities across 12 open‑source AI‑agent systems, achieving 59.3% end‑to‑end success compared to 38.4% for Codex, and 79.2% attack success on AgentDojo versus 52.7% for AgentVigil.
By Weida Liang, Shi Qiu, Zhun Wang, Simon Sure, Xiaoyuan Liu, Tianneng Shi, Zhaorun Chen, Wenbo Guo, Dawn Song
arXiv:2608.22103v1 Announce Type: new
Abstract: As agents grow more capable and autonomous, their tendency to reward hack, satisfying a task's checks while violating its intent, becomes an increasing...
By Amit Roth, Ivan Bercovich, Yonathan Efroni
arXiv:2610.14350v2 Announce Type: cross
Abstract: Having demonstrated a strong potential for automating vulnerability discovery, AI agents can also be used to attack software systems at scale. Today'...
By Jie Zhu, Ronik Bhaskar, Heather Zheng, Ben Y. Zhao, Kexin Pei
The paper surveys the growing use of AI agents that modify external state via the Model Context Protocol (MCP) ecosystem, noting an increase from 27% to 65% of tool use. It argues that when such agents operate on public blockchains, the blockchain execution layer’s properties—irreversibility, signing authority, continuous autonomy, and sequence-level composition—reshape the threat model, making failures irreversible. The authors organize existing MCP-security literature into an attack-surface taxonomy, present a Web3 risk-mapping matrix linking attack classes to amplified impacts and mitigations, and conclude that current defenses are inadequate, stopping fewer than 30% of attacks and less than 3% of model-level safety failures.
whyItMatters":"The study highlights that AI agents acting on Web3 introduce irreversible risks that conventional software security cannot address, underscoring the need for stronger, blockchain-aware safeguards."
By Rabimba Karanjai (Larry), Yang Lu (Larry), Nour Diallo (Larry), Wujie Xiong (Larry), Lei Xu (Larry), Weidong (Larry), Shi
arXiv:2606. 02302v1 Announce Type: cross Abstract: Autonomous LLM agents increasingly operate in stateful environments where they access tools, files, memory, and external services.
By Hao Cheng, Changtao Miao, Tianle Song, Yin Wu, He Liu, Erjia Xiao, Junchi Chen, Xiaoyu Shi, Yichi Wang, Jing Yang, Taowen Wang, Jinhao Duan, Mengshu Sun, Peiyan Dong, Xuan Shen, Yang Cao, Renjing Xu, Kaidi Xu, Jindong Gu, Bo Zhang, Jize Zhang, Chenhao Lin, Philip Torr, Chao Shen
PatchBench introduces a benchmark to evaluate AI agents on realistic vulnerability patching tasks, addressing two key threats to validity: patch memorization and surface-level fixes that merely suppress crashes. The study finds that 25% of agent patches resemble historical developer patches, and that PoC-only validation inflates success rates by 1.83× on average. PatchBench mitigates these issues by selecting vulnerabilities whose true fixes lie outside the crash stack, migrating historical vulnerabilities into new contexts, and employing rigorous validation for security and semantic correctness.
By Chihao Shen, Jiacheng Li, Aastha Mahajan, Jeffery Siyuan Tian, Yonghwi Kwon, Yizheng Chen
arXiv:2606. 03128v1 Announce Type: cross Abstract: Smart contracts face critical security challenges that require thorough auditing in decentralized web services.
By Bagus Rakadyanto Oktavianto Putra, Muhamad Risqi Utama Saputra, Widyawan, Guntur Dharma Putra
arXiv:2608. 19680v1 Announce Type: new Abstract: Smart contract vulnerability detection with Large Language Models (LLMs) faces three causally linked challenges.
By Tenghui Huang, Jiawen Kang, Dongning Liu, Changyan Yi, Chengjun Cai, Anjia Yang, Li Li, Dong In Kim