The adoption of smart contracts has surged remarkably. Believe not? In the first quarter of 2022, 1.45 million Ethereum smart contracts came into existence. That’s a notable 24.7% leap from the previous quarter, which saw 1.16 million smart contracts created. 

This not only underscores the current prevalence but also hints at the tremendous growth anticipated in the smart contract landscape.

However, amid the utilization of smart contracts in executing and automating blockchain transactions, vulnerabilities within these contracts pose a significant challenge. In 2023, these vulnerabilities led to more than $204.55 million loss across 103 hack incidents. It is no surprise that these smart contract vulnerabilities emerged as the second-most occurring attack type in 2023.

In addressing these vulnerabilities, auditing has proven to be paramount. It is a crucial checkpoint in ensuring the security and integrity of smart contracts. Also, with the growing complexity of smart contracts, there’s an increased demand for more efficient, comprehensive, and timely audits.

That’s where automated smart contract auditing finds its role in beginner-level screening. Automated audits streamline the auditing process and are embedded with advancements to enhance its accuracy and coverage. 

Feel like diving deep into understanding the details of automated smart contract auditing? You’ve arrived at the right place. 

Read on to uncover insights into the advancements, functionalities, and significance of automated audits in Web3 Security.

The Science Behind Automated Smart Contract Auditing

Traditional smart contract audits involve human experts manually diving into code, looking for potential bugs or loopholes. While nothing can match the quality of human efforts in smart contract auditing, there are also times when humans can miss stuff, and it’s time-consuming.

And so, smart contract auditing involves using a suite of tools to help with the process. With that said, automated smart contract auditing operates through three key mechanisms:

• Checking for the code match involves extracting and abstracting potentially malicious code. It goes through the lines of code, looking for specific patterns that might signal a flaw. This approach quickly finds a matching piece in the code’s source. All of this is facilitated provided the smart contracts are open-source.

• The formal verification approach involves converting code language into a more structured formal model, allowing auditors to assess if there are logical issues in the code. It’s like double-checking the logic of the code using a specific language to represent the code’s behaviour.

• Symbolic Execution and Symbolic Abstraction: This deals with digging into the data using symbolic execution and abstraction to decipher the code. However, it requires human acknowledgement to confirm findings. This is quite laborious as it needs manual confirmation for accuracy.

Slither, Echidna, and Mythril are tools used for the automated auditing of contracts.

How does AI-powered automation work in smart contract auditing?

AI brings a whole new game to auditing by covering the major aspects of auditing, as stated below.

• Static Analysis: AI-assisted auditing tool looks at smart contract code structures in the smartest way possible. It spots patterns, compares code against known vulnerabilities, and flags potential risks at an unmatchable pace. This means auditors can use it to get a head start in identifying issues without spending ages staring at code.

• Machine Learning does the job: Another underpinning aspect is that by learning from tons of smart contract data, AI picks up on patterns. It identifies common coding mistakes hackers generally exploit, like reentrancy bugs or integer overflows. As it learns more, it gets even better at spotting these red flags.

• NLP for Summarizing Reports: Now, using Natural Language Processing (NLP) helps AI pull out the important bits from reports. It quickly summarises the security status, pinpoints critical issues, and recommends suggestions for fixing them. 

• Dynamic Analysis and Test Coverage: AI in automated auditing also creates different scenarios to test smart contracts. Doing this catches hidden bugs that might slip through during manual testing. These tests cover all the bases, ensuring the contract bugs don’t slip away from the catch.

What qualities should a good automated smart contract tool possess?

• Optimal Efficiency: A proficient tool should operate swiftly, minimizing audit durations without compromising thoroughness. It is crucial that the tool saves time so auditors can do thorough checks without dragging things out.

• Precision & Accuracy: A reliable tool is defined by its ability to conduct a security audit with a minimal false positive rate. Ensuring accuracy in detecting actual vulnerabilities without flooding users with unnecessary alerts is pivotal for trust and effectiveness.

• Seamless Automation: The tool needs to be fully or semi-automatic. Developers/Auditors should be able to effortlessly upload the contract source code or the token address, initiating an automatic scan for vulnerabilities and offering periodic scheduling options to enable regular audits.

• Continuous Improvement: AI-assisted smart contract tools should be constantly trained to keep up with new risks and learnings from them, always staying on top of the latest threats.

• Risk-Free Assessments: Any security audit using an automated tool should not alter the original contract’s functionality. 

What’s The Trickiest Part Of Relying On The Automated Smart Contract Auditing Tools?

1. False Positives/Negatives: While striving for accuracy, these tools often face the risk of either raising unnecessary alarms (false positives) or missing genuine vulnerabilities (false negatives). 

2. Handling Complexity: Auditing tools must navigate intricate code structures, diverse functions, and varying contract designs. Managing this complexity effectively during comprehensive audits is a persistent challenge.

3. Context Window Constraints: In AI-driven smart contract auditing, the context window, similar to a large language model’s memory, sets limits on code analysis. This restriction becomes a challenge with intricate blockchain projects containing interconnected smart contracts that might exceed the window limits. 
4. Continuous learning: AI models are trained on existing data and known vulnerabilities. However, it leaves out new issues emerging because of insufficient data for effective training.

5. Detecting Complex Issues: The current advanced models like ChatGPT4 and others often struggle to identify complex bugs in smart contracts. 

Despite their limitations, AI-powered smart contract auditing tools are built by various firms by acknowledging the limitations and improvising them.

That said, QuillAI, developed by QuillAudits, stands out by harnessing the power of Large Language Models (LLMs) in smart contract auditing. Understanding intricate DeFi contract semantics, this tool is designed to report advanced vulnerabilities often missed by standard tools. Currently in its beta stage, QuillAI’s AI-powered static analysis delivers precise, code-specific recommendations, surpassing the limitations of false positives and missing complex attack vectors.

Wrapping Up,

While these AI-powered tools aid in understanding contracts and describing issues, their limitations necessitate a balanced approach, combining AI’s strengths with human vigilance and understanding.