Agentic AI Security Audits for Autonomous Web3 Systems

    Traditional audits miss AI-specific attack surfaces. We assess prompt injection, tool misuse, RAG poisoning, and autonomous on-chain execution risks.

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    What Is Agentic AI in Web3?

    Agentic AI systems are autonomous AI agents that execute on-chain actions — managing wallets, interacting with smart contracts, making governance decisions, and processing transactions without direct human approval for each operation.

    These systems introduce entirely new categories of security risk that traditional smart contract audits don't address: prompt injection affecting transaction execution, AI misinterpretation of governance rules, RAG poisoning influencing contract interactions, and autonomous wallet abuse.

    Risks Traditional Audits Miss

    AI agents create attack surfaces that don't exist in standard smart contract code.

    Prompt Injection & Chain Interactions

    Malicious prompts that manipulate AI agents into executing unauthorized on-chain transactions or bypassing access controls.

    AI-Driven Wallet Automation Risks

    Autonomous wallet operations vulnerable to manipulation, draining, or unintended high-value transactions.

    RAG Security in Smart Contract Operations

    Retrieval-augmented generation poisoning that feeds false context to AI agents interacting with contracts.

    Untrusted AI Outputs in On-Chain Transactions

    AI-generated parameters, function calls, or transaction data that bypass validation before on-chain execution.

    Our Methodology

    Our Hybrid Agentic AI Audit Approach

    Step 01

    AI Behavior Mapping

    We model the agent's decision tree, tool usage patterns, and on-chain interaction flows to identify all attack surfaces.

    Step 02

    Prompt & Tool Misuse Analysis

    Testing for prompt injection, jailbreaking, tool misuse, and adversarial inputs that cause unintended actions.

    Step 03

    Contract Interaction Testing

    Simulating how AI agent outputs interact with smart contracts — verifying parameter validation, access control, and state changes.

    Step 04

    Expert Red Team Simulation

    Security engineers simulate real-world adversarial scenarios targeting the AI agent's autonomy and on-chain permissions.

    Step 05

    Findings & Remediation

    Severity-ranked report covering all agent-specific vulnerabilities with actionable mitigation strategies.

    Who Needs an Agentic AI Audit?

    AI-Integrated dApps

    Applications using LLMs to interact with smart contracts or process on-chain data.

    Autonomous Trading Bots

    AI-powered trading systems executing transactions based on market analysis or signals.

    LLM Wallet Systems

    Wallet interfaces using natural language for transaction creation and signing.

    AI Governance Platforms

    DAO tools using AI for proposal analysis, voting recommendations, or automated execution.

    Frequently Asked Questions

    We map the agent's behavior, tool usage, and on-chain interactions. Then we test for prompt injection, tool misuse, adversarial inputs, and RAG poisoning. Our experts simulate real-world attack scenarios targeting the agent's autonomy and permissions.

    Yes. AI agents that interact with smart contracts introduce unique attack surfaces including prompt injection leading to unauthorized transactions, RAG poisoning affecting contract interaction context, and tool misuse enabling privilege escalation.

    Extensively. We test direct injection, indirect injection via external data sources, jailbreak attempts, and adversarial inputs designed to manipulate the agent's on-chain behavior.

    Agentic AI refers to autonomous AI systems that can take actions on-chain — executing transactions, managing wallets, interacting with smart contracts, or making governance decisions without direct human approval for each action.

    Standard audits focus on contract code. Agentic AI audits additionally assess the AI's decision-making logic, input handling, tool usage patterns, and the interface between AI outputs and on-chain execution.

    Secure Your AI Agents Before They Go Live

    Don't let AI-specific vulnerabilities become your protocol's biggest risk.

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