> ## Documentation Index
> Fetch the complete documentation index at: https://docs.bevor.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Labeling Security Data

> How to tag findings to strengthen BevorAI models

Help improve BevorAI's accuracy and effectiveness by contributing high-quality labeled security data. Your expert knowledge directly enhances the AI models that power security analysis for the entire ecosystem.

<Note>
  Data labeling will be eligible for compensation in the future. If you're an auditor interested in becoming an official labeling partner, reach out to [contact@bevor.io](mailto:contact@bevor.io) to be considered.
</Note>

## Why Data Labeling Matters

BevorAI's effectiveness depends on high-quality training data. When security experts like you label findings, you're:

* **Improving Model Accuracy**: Teaching AI to recognize subtle security patterns
* **Reducing False Positives**: Helping models distinguish between real threats and benign code
* **Expanding Coverage**: Contributing to detection of new vulnerability types
* **Building Community Knowledge**: Sharing expertise to benefit all users

## How to Label Findings

### Through the Dashboard

1. **Access the Labeling Interface**
   * Open [app.bevor.io](https://app.bevor.io)
   * Navigate to the Dashboard
   * Enable "Expert Mode" in settings

2. **Review and Label Findings**
   * Run security analysis on contracts
   * Review each finding in the results panel
   * Use the labeling interface to mark findings as:
     * ✅ **True Positive**: Confirmed vulnerability
     * ❌ **False Positive**: Incorrect detection
     * ⚠️ **Missed Vulnerability**: Missed a human-identified vulnerability

3. **Add Detailed Annotations**
   * Provide specific vulnerability classifications
   * Add severity assessments (Critical, High, Medium, Low)
   * Include remediation suggestions
   * Note any special context or edge cases

## Labeling Quality Guidelines

### Be Specific and Accurate

* Use precise vulnerability classifications
* Provide clear, actionable descriptions
* Include relevant code snippets or line numbers
* Reference established security standards (e.g., SWC registry)

### Consider Context

* Account for intended behavior vs. actual vulnerabilities
* Consider the broader protocol context
* Note any assumptions or prerequisites
* Identify edge cases and boundary conditions

### Maintain Consistency

* Use standardized terminology
* Follow established severity classifications
* Apply consistent labeling criteria
* Document any special cases or exceptions

## Recognition and Rewards

Contributors to BevorAI's security dataset receive:

* **Recognition**: Public acknowledgment in our contributor hall of fame
* **Labeling Rewards**: Earn for high-quality contributions
* **Early Access**: Priority access to new features and capabilities
* **Community Status**: Special roles in our Discord community

## Best Practices

<Tip>
  Focus on quality over quantity. A few well-labeled, thoroughly analyzed findings are more valuable than many hastily labeled ones.
</Tip>

### Before Labeling

* Understand the contract's intended functionality
* Review the broader protocol context
* Check for existing similar vulnerabilities
* Verify your analysis with multiple approaches

### During Labeling

* Be thorough in your analysis
* Document your reasoning process
* Include relevant references and sources
* Consider multiple attack vectors

### After Labeling

* Review your labels for consistency
* Update labels if you discover new information
* Share insights with the community
* Track the impact of your contributions