# Beyond Detection: Agentic Attack Synthesis and Simulation for Smart Contracts

arXiv: https://arxiv.org/abs/2607.15673 · HTML: https://mlsanity.com/paper/2607.15673/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 28 of its 66 references that are in the map.
- First posted on arXiv 17 Jul 2026.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 28 of 28)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [An Explanation of In-context Learning as Implicit Bayesian Inference](https://mlsanity.com/paper/2111.02080/) (2021)
- [CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challenges](https://mlsanity.com/paper/2401.07339/) (2024)
- [Self-collaboration Code Generation via ChatGPT](https://mlsanity.com/paper/2304.07590/) (2023)
- [MapCoder: Multi-Agent Code Generation for Competitive Problem Solving](https://mlsanity.com/paper/2405.11403/) (2024)
- [Structured Chain-of-Thought Prompting for Code Generation](https://mlsanity.com/paper/2305.06599/) (2023)
- [Self-planning Code Generation with Large Language Models](https://mlsanity.com/paper/2303.06689/) (2023)
- [Slither: A Static Analysis Framework For Smart Contracts](https://mlsanity.com/paper/1908.09878/) (2019)
- [GPTScan: Detecting Logic Vulnerabilities in Smart Contracts by Combining GPT with Program Analysis](https://mlsanity.com/paper/2308.03314/) (2023)
- [Securify: Practical Security Analysis of Smart Contracts](https://mlsanity.com/paper/1806.01143/) (2018)
- [Combining Fine-Tuning and LLM-based Agents for Intuitive Smart Contract Auditing with Justifications](https://mlsanity.com/paper/2403.16073/) (2024)
- [Manticore: A User-Friendly Symbolic Execution Framework for Binaries and Smart Contracts](https://mlsanity.com/paper/1907.03890/) (2019)
- [PropertyGPT: LLM-driven Formal Verification of Smart Contracts through Retrieval-Augmented Property Generation](https://mlsanity.com/paper/2405.02580/) (2024)
- [Empirical Review of Automated Analysis Tools on 47,587 Ethereum Smart Contracts](https://mlsanity.com/paper/1910.10601/) (2019)
- [ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection](https://mlsanity.com/paper/1807.03932/) (2018)
- [SoK: Decentralized Finance (DeFi) Attacks](https://mlsanity.com/paper/2208.13035/) (2022)
- [A Survey on Ethereum Systems Security: Vulnerabilities, Attacks and Defenses](https://mlsanity.com/paper/1908.04507/) (2019)
- [Harvey: A Greybox Fuzzer for Smart Contracts](https://mlsanity.com/paper/1905.06944/) (2019)
- [SAILFISH: Vetting Smart Contract State-Inconsistency Bugs in Seconds](https://mlsanity.com/paper/2104.08638/) (2021)
- [Smart Contract and DeFi Security Tools: Do They Meet the Needs of Practitioners?](https://mlsanity.com/paper/2304.02981/) (2023)
- [Oracle-Supported Dynamic Exploit Generation for Smart Contracts](https://mlsanity.com/paper/1909.06605/) (2019)
- [ItyFuzz: Snapshot-Based Fuzzer for Smart Contract](https://mlsanity.com/paper/2306.17135/) (2023)
- [Prompt to Pwn: Automated Exploit Generation for Smart Contracts](https://mlsanity.com/paper/2508.01371/) (2025)
- [Turn the Rudder: A Beacon of Reentrancy Detection for Smart Contracts on Ethereum](https://mlsanity.com/paper/2303.13770/) (2023)
- [A Systematic Literature Review on Automated Exploit and Security Test Generation](https://mlsanity.com/paper/2502.04953/) (2025)
- [Steroids for DOPed Applications: A Compiler for Automated Data-Oriented Programming](https://mlsanity.com/paper/2007.02308/) (2020)

## Abstract (arXiv)

Smart contract vulnerabilities pose severe financial risks, yet existing security tools largely stop at vulnerability detection, offering limited support for explaining whether reported flaws are exploitable, how attacks unfold, and what concrete damage they cause. To bridge this gap, we propose KASS (Knowledge-Augmented Attack Synthesis and Simulation), a multi-agent framework for executable smart contract exploit verification. KASS decomposes automated exploit generation into planning, generation, and testing stages, and integrates three complementary mechanisms: retrieval-augmented planning over real-world audit knowledge, formal generation and validation constraints that bind attack plans to executable proof-of-concept tests, and a hierarchical dual-loop refinement process that repairs code-level errors while triggering strategy-level replanning when attack assumptions fail. We evaluate KASS on 104 SmartBugs-Curated contracts across four vulnerability categories. Experimental results show that KASS successfully generates executable exploits for 94.23% of tested contracts; this rate is higher than previously reported results for REX and AdvSCanner on comparable SmartBugs-Curated subsets, and higher than our reproduced Claude Code baseline under the same evaluation protocol. On 11 real-world CVE-tagged contracts, KASS successfully validates 9 cases. Beyond exploit generation, KASS produces structured attack plans that document exploitation flows, quantify potential asset losses, and serve as semantic false positive filters for static analysis tools.