SnapBack

AI Detection

SnapBack monitors your coding session in real-time to detect AI tool activity and analyze code risks. This dual approach ensures protection is triggered when AI makes substantial changes or introduces common risks.

How It Works

AI Tool Detection

SnapBack detects when AI coding assistants are active and tracks their contribution to your code:

🤖

Tool Detection

Identifies which AI tools are running: Cursor, GitHub Copilot, Claude Code, Windsurf, and others.

~89% accuracy

📊

Contribution Tracking

Measures how much of your recent work came from AI suggestions vs. manual typing.

Adapts over time

Guardian AI: Code Analysis

Separately, Guardian AI scans your code for risks that AI tools sometimes introduce:

🔑

Secrets Detection

Identifies API keys, JWT tokens, database strings, and other sensitive information.

🎭

Mocks Detection

Catches test artifacts and mock data that accidentally make it into production code.

👻

Phantom Dependencies

Detects missing package.json entries for modules used in your code.

Two Systems Working Together:
AI tool detection tells SnapBack when to pay attention. Guardian AI tells it what risks to look for. Together, they decide when to protect your work.

Real-Time Analysis

Guardian AI performs real-time analysis on every save, providing immediate feedback:

System Online
Guardian AI Detection Rate~94% Accuracy

For secrets, mocks, and phantom dependencies

Detection Speed

Both AI tool detection and Guardian AI operate in real-time:

  • AI Tool Detection: Instant (passive monitoring)
  • Guardian AI Scan: <50ms average
  • Complex Pattern Analysis: <200ms

What Gets Detected

Secrets

Guardian AI identifies various types of secrets that should never be committed:

// ❌ Detected as secret risk
const config = {
  apiKey: "sk-abcdefghijklmnopqrstuvwxyz1234567890", // API Key
  dbPassword: "supersecretpassword123", // Database Password
  jwtSecret: "my-jwt-secret-key", // JWT Secret
  awsAccessKey: "AKIAIOSFODNN7EXAMPLE", // AWS Access Key
};

Mocks

AI assistants sometimes generate test artifacts that shouldn’t be in production code:

// ❌ Detected as mock risk
const userData = {
  id: 1,
  name: "John Doe",
  email: "john.doe@example.com", // Realistic but fake data
  role: "admin"
};

// This looks like mock data that shouldn't be in production
function processPayment() {
  console.log("Processing payment of $100.00"); // Mock payment
  return { success: true, transactionId: "txn_1234567890" }; // Fake transaction
}

Phantom Dependencies

Missing dependencies that are used in code but not declared in package.json:

// ❌ Detected as phantom dependency risk
import { format } from 'date-fns'; // Used but not in package.json
import lodash from 'lodash'; // Used but not declared

export function formatDate(date) {
  return format(date, 'yyyy-MM-dd');
}

export function deepClone(obj) {
  return lodash.cloneDeep(obj);
}

False Positive Handling

We understand that not every detection is a real risk. Guardian AI is designed to minimize false positives while maintaining high accuracy:

💡 False Positive Management: If Guardian AI flags something that isn’t actually a risk, you can:

  • Add it to your ignore list in .snapbackignore
  • Adjust sensitivity levels for specific file patterns
  • Provide feedback through the VS Code extension

Configuration

You can customize Guardian AI’s behavior to match your project’s needs:

Sensitivity Levels

// .snapbackconfig
{
  "aiDetection": {
    "sensitivity": "high", // low, medium, high
    "ignorePatterns": [
      "**/*.test.js",
      "**/mocks/**"
    ],
    "customRules": [
      {
        "pattern": "API_KEY",
        "type": "secret",
        "severity": "high"
      }
    ]
  }
}

VS Code Integration

In the VS Code extension, you can:

  1. View real-time detection results in the Problems panel
  2. See inline decorations for detected risks
  3. Configure detection settings through the UI
  4. Provide feedback on detections

Accuracy Metrics

Our continuous testing shows Guardian AI maintains high accuracy across different codebases:

Precision

96%

Of detections are actual risks requiring attention

Recall

92%

Of actual risks are successfully detected

Best Practices

🔍 Review Detections Promptly

Don’t ignore Guardian AI warnings. Take time to understand what’s being flagged and why.

⚙️ Customize for Your Project

Adjust sensitivity levels and ignore patterns based on your specific project needs.

📚 Educate Your Team

Make sure all team members understand what Guardian AI detects and how to respond.

🔄 Provide Feedback

Help us improve by reporting false positives or missed detections.

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