AI Agent Smart Contract Exploit Generation (2507.05558v3)

Abstract: Smart contract vulnerabilities have led to billions in losses, yet finding actionable exploits remains challenging. Traditional fuzzers rely on rigid heuristics and struggle with complex attacks, while human auditors are thorough but slow and don't scale. LLMs offer a promising middle ground, combining human-like reasoning with machine speed. However, early studies show that simply prompting LLMs generates unverified vulnerability speculations with high false positive rates. To address this, we present A1, an agentic system that transforms any LLM into an end-to-end exploit generator. A1 provides agents with six domain-specific tools for autonomous vulnerability discovery, from understanding contract behavior to testing strategies on real blockchain states. All outputs are concretely validated through execution, ensuring only profitable proof-of-concept exploits are reported. We evaluate A1 across 36 real-world vulnerable contracts on Ethereum and Binance Smart Chain. A1 achieves a 63% success rate on the VERITE benchmark. Across all successful cases, A1 extracts up to \$8.59 million per exploit and \$9.33 million total. Through 432 experiments across six LLMs, we show that most exploits emerge within five iterations, with costs ranging \$0.01-\$3.59 per attempt. Using Monte Carlo analysis of historical attacks, we demonstrate that immediate vulnerability detection yields 86-89% success probability, dropping to 6-21% with week-long delays. Our economic analysis reveals a troubling asymmetry: attackers achieve profitability at \$6,000 exploit values while defenders require \$60,000 -- raising fundamental questions about whether AI agents inevitably favor exploitation over defense.

• The paper introduces A1, a novel framework that uses LLMs and real-time feedback to autonomously generate smart contract exploits.
• It evaluates A1 across 432 experiments on Ethereum and Binance Smart Chain, achieving a success rate of up to 63% and uncovering vulnerabilities worth ~$9.33M.
• The study offers an economic analysis model showing that attackers can profit at lower thresholds compared to defenders, emphasizing rapid exploit detection.

AI Agent Smart Contract Exploit Generation

Introduction

The research paper "AI Agent Smart Contract Exploit Generation" addresses the significant challenge posed by vulnerabilities in smart contracts employed in the DeFi ecosystem. These vulnerabilities have led to substantial monetary losses, urging the need for efficient and scalable solutions to discover actionable exploits. While traditional security measures like manual audits and static analysis tools are prevalent, the paper introduces a novel agentic system named A1, which leverages LLMs to autonomously generate exploits for smart contracts by integrating real-time feedback for validation.

A1 System Overview

System Design

A1 is an agent-based framework designed to transform LLMs into autonomous entities capable of detecting and exploiting smart contract vulnerabilities. The system equips the agent with six specialized tools, allowing it to seamlessly navigate the smart contract environment:

• Source Code Fetcher Tool: Identifies real implementations beyond proxy interfaces.
• Constructor Parameter Tool: Recovers initial deployment settings.
• State Reader Tool: Captures current state data from the blockchain.
• Code Sanitizer Tool: Removes non-executable code to streamline analysis.
• Concrete Execution Harness Tool: Validates exploit strategies.
• Revenue Normalizer Tool: Ensures comparable economic analysis across blockchains.

  Figure 1: Duration analysis across six LLMs for A1.

Agent Strategy Generation

The agent autonomously hypothesizes exploits by analyzing pre-collected data from domain-specific tools. The reasoning framework adapts its strategies iteratively based on execution feedback, refining hypotheses through:

• Binary profitability indicators.
• Transaction flow analyses.
• Feedback from preceding hypotheses. This feedback loop further ensures that the agent effectively constructs PoC exploits within the confines of a determined format, allowing consistent validation through the Forge testing tool, which is embedded within a real blockchain environment.

  Figure 2: CDF comparison between attack-window durations and exploit-generation runtimes for six language-model pipelines.

Evaluation

Dataset and Experiment Model

The paper evaluates the A1 system on 36 real-world contracts sourced from the Ethereum and Binance Smart Chain networks. These evaluations are conducted through 432 experiments across six LLM configurations. Key evaluation metrics include:

• Success rate across iterations and models.
• Economic feasibility and cost analysis of generated exploits.
• Timing constraints considering the execution efficiency and timeframe for exploit generation.

Results

Key findings from the evaluation demonstrate:

• A notable success rate of up to 63% across the benchmarked VERITE dataset, surpassing existing tools like ItyFuzz in both coverage and performance metric comparisons.
• A1's ability to autonomously discover vulnerabilities valued at approximately \$9.33 million in potential exploits.
• Variability in success rates influenced by different model capabilities, where more potent models like o3-pro exhibited superior success rates and speed in exploit generation.

  Figure 3: Token usage analysis across 432 experiments with a 16.8\% success rate.

Economic Analysis

A detailed analysis reveals crucial insights into the economic viability of deploying A1 in real-world scenarios. The paper presents an economic model for continuous security monitoring:

• Attackers attain profitability at lower thresholds of \$6,000 as compared to defenders' requirement of \$60,000 exploit values, considering similar operational expenses.
• Profitability is heavily dependent on rapid detection and the frequency of exploit occurrences within new smart contracts.

  Figure 4: Economic viability analysis across six LLMs, showing expected USD profit per analyzed contract.

Conclusion

The research introduces A1, a pioneering approach that extends the capabilities of LLMs from theoretical analyzers to proactive security agents capable of discovering and validating exploits in real smart contracts. Despite certain limitations, like dependency on prior data for memorization, A1 demonstrates considerable promise for scalability and adaptiveness within dynamic blockchain environments. As the system evolves, it stands to enhance the security auditing processes and propel the adoption of autonomous systems in safeguarding the DeFi ecosystem. Future directions might explore combining algorithmic and human intelligence to refine the detection accuracy and further mitigate the inherent asymmetries identified in the economic analysis.

Figure 5: Split violin plot comparing the distribution of source lines of code (left half) and comment lines in automatically generated exploit PoCs.