AI audit, check for you

Nowadays, in the field of blockchain, the mentality of investors has quietly changed: in the past, everyone was worried about poor project returns, but now they are even more afraid of unwarranted damage to assets. DeFi protocols are attacked, token projects run away, cross-chain bridge security incidents occur frequently, and various security incidents occur frequently, making security issues a top priority in the Web3 industry.

When many projects are launched, smart contracts have hidden dangers: backdoors can be reserved to issue additional tokens at will, administrators who lose control of their rights can freeze and transfer user assets, and subtle code loopholes can become attack channels for hackers. However, smart contracts have large amounts of code and complex logic. Auditing tens of thousands of lines of code only with the naked eye and manual inspection is not only time-consuming and costly, but also makes it easy to miss hidden risks.

In this context, AI technology has officially entered the blockchain security audit. It is like an exclusive intelligent security radar that can quickly scan codes in batches, screen for vulnerabilities, monitor anomalies throughout the project cycle, and predict runout risks, building a new line of security for project parties and ordinary investors.

Part 01 - What contract risks can AI audit uncover?

Many people mistakenly believe that "online means security", but hidden vulnerabilities and backdoors in contract codes are the main source of Web3 security incidents. The core value of AI auditing is to quickly identify these high-frequency risk points through automated scanning.

1. Additional issuance and minting permission loopholes

Some contract codes hide undisclosed minting/additional issuance permissions. Projects can mint tokens at will, dilute user positions, or even directly harvest the market through large-scale selling. AI audit can quickly locate permission control logic and mark high-risk permissions with unlimited issuance and no time lock.

2. Risk of administrator privilege abuse

If the administrator role in the contract has too much authority, he can directly call the function to transfer the user's pledged assets and freeze the account balance, causing the user's assets to "streaking". AI auditing will focus on scanning permission control-related codes to identify whether there are super administrator permissions that can be abused.

3. Common logic and execution vulnerabilities

In response to problems such as re-entrancy vulnerabilities, flash loan attacks, and logic overflows that are common in DeFi projects, AI auditing can quickly scan the code execution path, identify potential fund manipulation risks, and provide accurate directions for subsequent manual audits.

? It can be said that AI audit is like a "comprehensive physical examination" for the contract, which can effectively screen out more than 80% of known high-frequency vulnerabilities and prevent the project from going online with "fatal bugs".

Part 02 - AI Audit vs Human Audit: Who is More Reliable?

Many people will ask: "AI is so efficient, can it completely replace manual auditing?" In fact, the two are not substitutes, but complementary and synergistic. The core differences between the two can be found in the following table:

AI auditing is good at “wide coverage, high efficiency” preliminary screening, while manual auditing focuses on “deep mining, full-scenario” logic verification.

To give a real case: Before a certain DeFi project went online, it used AI auditing to mark three high-risk points related to permissions. Based on further analysis, the manual auditor found a business logic vulnerability that was not identified by AI, thus avoiding heavy losses from hackers after going online.

Part 03 - Be wary of the limitations and misunderstandings of AI auditing

Although AI auditing is efficient, it is by no means a gold medal for "safety once tested". Both project parties and users need to avoid these cognitive misunderstandings:

1. Reject “AI only theory”

AI has limited ability to identify new and complex business logic vulnerabilities. Launching a project solely relying on AI audit is tantamount to leaving a "backdoor" for hackers. Formal projects must be paired with manual in-depth audits to cover scenario risks that AI cannot identify.

2. Treat false positives and false negatives rationally

AI may misjudge the emergency pause and whitelist mechanisms set by the project team as high-risk permissions, or may miss special logic loopholes due to algorithm limitations. These require manual auditors to make review judgments based on business scenarios.

3. Not only look at the risk level, but also read the details

When you get the AI audit report, you should not just focus on the “high/medium/low risk” labels, but also read the vulnerability description carefully. For example, labels such as "Administrators can transfer user tokens" need to be combined with the project mechanism to determine the purpose of permissions to avoid misjudgment of compliance functions.

4. Complex projects require manual auditing

DeFi projects with complex logic such as cross-chain bridges and aggregators have long business links and many interaction scenarios. AI auditing can only be used as a preliminary screening tool, and core risks must be analyzed in depth by professional auditors.

Part 04 - How can ordinary users use AI auditing to avoid pitfalls?

As an ordinary user, there is no need to master complex contract code knowledge. You only need to learn to use AI audit results to quickly identify high-risk projects and protect your own on-chain assets.

1. Check the contract first, then discuss investment

When you see a new token or DeFi project, first scan the contract code through the public AI audit platform, focusing on checking whether there are high-risk vulnerabilities such as additional issuance and asset transfer by administrators.

2. Be wary of “out of control” projects

If the audit report shows that the project party has unlimited permission to mint coins and transfer user assets, it will be directly determined as a high-risk project and decisively avoid lightning.

3. Reject the "Pure AI Audit" project

If the project only provides AI audit reports without manual audits, it is most likely that the project party is trying to save costs and avoid strict review. The risk of such projects running away is extremely high.

4. Check the authenticity and timeliness of the report

Some projects will use old audit reports to pass off as the latest reports, or even falsify audit results. It is necessary to check the release time of the report and the audit agency information to avoid being misled by false reports.

Conclusion

AI auditing is not omnipotent, and it cannot completely replace the in-depth reasoning and confrontational thinking of human experts. But it can become the first line of defense for project parties and investors, quickly screening out those fatal basic risks at a very low cost.

For ordinary investors, there is no need to be a coding expert. They only need to learn to focus on the three elements of the audit report: Whether there are high-risk unfixed vulnerabilities, whether the administrator has excessive authority, and whether it has been manually audited by an authoritative agency.

A little more professionalism, a little less blood loss. Zero Hour Technology's security team has long been engaged in smart contract auditing, AI security detection and on-chain risk tracing, providing project parties with a one-stop security solution of "AI initial screening + manual in-depth auditing", and also providing investors with free contract risk consultation. Security is the most reliable moat for the Web3 ecosystem.