π₯Inside The Machine
Itsafe AI leverages advanced artificial intelligence to transform how blockchain contracts are audited β delivering unmatched speed, accuracy, and insight. At the core of the platform is a powerful AI engine that continuously learns from vast amounts of on-chain data, making every contract scan smarter than the last.
Hereβs how the machine works under the hood:
π§ AI-Driven Contract Intelligence
Itsafe AI doesnβt just scan code β it understands it. By analyzing millions of contracts and attack patterns, the AI engine performs intelligent, automated contract evaluations at scale:
Pattern Recognition The AI identifies recurring logic patterns and code structures across smart contracts. This helps flag suspicious functions, hidden minting or burning mechanics, or permission loopholes that often go unnoticed by traditional tools.
Anomaly Detection Trained on historical attack vectors and exploit records, the AI flags unusual behaviors and deviations from safe coding practices, detecting potential zero-day vulnerabilities before they are exploited.
Continuous Learning Every new contract scanned β every vulnerability confirmed β feeds back into the model. This continuous loop allows the AI to evolve and improve, becoming more accurate with each iteration.
π§Ύ Natural Language Processing (NLP) for Code Context
Smart contracts sometimes include comments or documentation within the code. Itsafe AI uses Natural Language Processing (NLP) to:
Interpret human-written comments and annotations.
Understand the intended logic behind functions.
Provide clearer, contextual explanations in its audit reports.
This means better insights, especially for non-technical users or teams trying to understand what a function is really doing.
π Automated Vulnerability Scanning
Itsafe AI automates detection of the most critical smart contract vulnerabilities β with advanced detection logic that mimics expert-level security analysis:
Reentrancy Attack Detection AI flags risky patterns where external calls could lead to recursive contract exploitation β one of the most common DeFi vulnerabilities.
Security Risk Assessments The system scans for:
Integer overflows and underflows
Uninitialized storage variables
Broken access control
Logic bombs and unchecked calls
β
Always Getting Smarter
Every vulnerability detected, every new smart contract scanned, and every user interaction helps train the system. This means Itsafe AI doesnβt just audit β it learns. With its growing intelligence, it continues to outperform manual reviews and static tools, staying ahead of new threats in the evolving Web3 space.
Code in Context: Smart Contract Function Analysis
Extracting and Analyzing All Functions in a Contract (Python/Pseudocode)
for func_name, func_code in functions.items():
issues = []
# Check for minting or burning logic
if "mint" in func_code or "burn" in func_code:
issues.append("Contains mint/burn logic β verify usage.")
# Check for ownership control keywords
if "onlyOwner" in func_code or "owner" in func_code:
issues.append("Function restricted to owner β potential centralized control.")
# Detect external calls which may be risky
if "call" in func_code or "delegatecall" in func_code:
issues.append("External call detected β could be exploited.")
# Identify token transfers and approvals
if "transfer" in func_code or "approve" in func_code:
issues.append("Handles transfers or approvals β review for security.")
analysis_results[func_name] = {
"function_code": func_code,
"flags": issues if issues else ["No immediate risks detected."]
}
return analysis_results
Reporting All Functions with Flags (Conceptual JSON Output)
{
"transfer": {
"function_code": "function transfer(address to, uint256 amount) public returns (bool) { ... }",
"flags": ["Handles transfers or approvals β review for security."]
},
"mintTokens": {
"function_code": "function mintTokens(uint256 amount) external onlyOwner { ... }",
"flags": ["Contains mint/burn logic β verify usage.", "Function restricted to owner β potential centralized control.
"flags": ["N
Continuous Model Refinement with User Feedback (Pseudocode)
def refine_ai_model_with_feedback(user_flagged_functions: list, model):
"""
Incorporate user feedback on flagged functions to improve AI analysis accuracy.
"""
training_data = prepare_training_samples(user_flagged_functions)
model.fine_tune(training_data)
return model
Smart Detection: ITAI Knows When a Risk Isnβt Real
β οΈ Situation:
A function in a contract is restricted to
onlyOwner
.That function could potentially harm the project (e.g., it can change tax, block trading, etc.).
However, ownership is renounced β meaning nobody can call
onlyOwner
functions anymore.Some bots might flag it as risky just because the function exists, even if it's not callable.
ITAI bot checks if the function is actually callable and confirms it's safe = β Green rating.
// SPDX-License-Identifier: MIT
pragma solidity ^0.8.0;
import "@openzeppelin/contracts/access/Ownable.sol";
contract MyToken is Ownable {
uint256 public tax = 5;
// Risky function but onlyOwner
function changeTax(uint256 newTax) public onlyOwner {
require(newTax <= 10, "Too high!");
tax = newTax;
}
// Ownership renounced
constructor() {
_transferOwnership(address(0));
}
}
π Explanation
changeTax()
is onlyOwner β meaning only the contract owner can change the tax.In this contract, the
owner
is renounced at deployment (address(0)
).So no one can ever call
changeTax()
again β even though the function exists.
β
How ITAI Handles This:
Other audit bots might flag this red/yellow just because
changeTax()
exists.But ITAI sees:
onlyOwner
function βOwner is renounced β
Function can't be called β
β So it's marked safe β because it's non-functional in practice.
Bot 1
Flags based on code patterns only
β Red
Bot 2
Flags based on code patterns only
π¨Yellow
ITAI
Checks function logic + live state
β Green
π» π» π»
π₯ITAI CompetitionLast updated