Excessive Data Exposure in Chi with Jwt Tokens

Excessive Data Exposure occurs when an API returns more information than necessary for a given operation. In Chi, this commonly manifests through JWT token handling where sensitive claims are included in responses or where token introspection endpoints expose payload data without appropriate filtering. Chi routes often pass decoded JWT claims through handlers that inadvertently serialize entire token payloads into JSON responses.

For example, a Chi endpoint that authenticates using JWT middleware might decode the token to extract a user identifier but then return the full decoded claims map to the client. This can include administrative flags, email addresses, or internal identifiers that should remain restricted. An attacker authenticating with a low-privilege token could leverage this excessive exposure to infer the structure of internal systems or identify other accounts.

JWT tokens in Chi are frequently used for session management and authorization. When endpoints construct responses by directly embedding the claims obtained from jwt.decode or similar operations, they risk exposing fields such as exp, iat, custom roles, or even debug information present in development tokens. This becomes particularly dangerous when error messages or logging mechanisms also reflect these values, providing additional reconnaissance data to an attacker.

The combination of Chi’s pattern of route-specific handlers and the widespread use of JWT for stateless authentication increases the likelihood of inconsistent data handling. A developer might secure a primary endpoint but overlook related endpoints such as user profile or status endpoints that return decoded JWT data. Because Chi does not enforce a global response schema, it is easy for individual routes to leak information through JSON fields derived from token payloads.

middleBrick scans identify this category of risk under Data Exposure and Inventory Management checks, correlating runtime behavior defined in OpenAPI specifications with observed responses. When a scan detects endpoints returning fields commonly found in JWT payloads without explicit authorization, it highlights the need for output normalization. This helps teams understand where token-derived data is being exposed and supports alignment with OWASP API Top 10 A01:2023, which addresses broken object level authorization and data exposure concerns.

Real-world scenarios include endpoints that return user profiles with roles embedded in JWT claims, or debugging routes that echo token contents for troubleshooting purposes. Without consistent filtering or schema validation, these patterns create a broad attack surface. middleBrick’s LLM/AI Security checks further evaluate whether token handling logic could be abused through prompt injection or output extraction techniques that rely on exposed data paths.

Remediation focuses on ensuring that responses never reflect raw JWT payloads and that only necessary claims are used for authorization. Developers should explicitly select required fields and sanitize outputs. Below are concrete examples demonstrating secure handling within Chi handlers.

First, use JWT decoding to extract only the necessary identifier and avoid passing the entire claims map downstream:

import 'dart:convert';
import 'package:chi/chi.dart';
import 'package:jwt_decode/jwt_decode.dart';

Future<void> handler(RequestContext context) async {
  final token = context.request.headers['authorization']?.replaceFirst('Bearer ', '');
  if (token == null) {
    context.response.code = 401;
    return;
  }

  final decoded = Jwt.decode(token, verify: false);
  final userId = decoded['sub'];
  final role = decoded['role'];

  // Explicitly use only required claims
  context.response.body = jsonEncode({'userId': userId, 'role': role});
  context.response.headers['Content-Type'] = 'application/json';
}

This approach ensures that only selected fields are exposed, avoiding leakage of sensitive claims such as email or permissions that may be present in the token.

For user profile endpoints, construct a dedicated response object instead of reflecting token data:

class UserProfile {
  final String id;
  final String name;

  UserProfile(this.id, this.name);

  Map<String, dynamic> toJson() => {
    'id': id,
    'name': name,
  };
}

Future<void> profileHandler(RequestContext context) async {
  final token = context.request.headers['authorization']?.replaceFirst('Bearer ', '');
  if (token == null) {
    context.response.code = 401;
    return;
  }

  final decoded = Jwt.decode(token, verify: false);
  final userId = decoded['sub'];

  // Fetch user data from a secure data source
  final profile = UserProfile(userId, 'Example User');
  context.response.body = jsonEncode(profile.toJson());
  context.response.headers['Content-Type'] = 'application/json';
}

When using Chi middleware for JWT validation, ensure that the middleware does not automatically inject decoded claims into response contexts. Middleware should focus on authentication and early termination, not data propagation.

For error handling, avoid including token details in error messages:

try {
  // Some operation
} catch (e) {
  context.response.code = 400;
  context.response.body = jsonEncode({'error': 'invalid_request'});
}

These patterns align with the principle of least privilege and help prevent Excessive Data Exposure. middleBrick’s CLI tool can be used locally to verify that endpoints no longer reflect sensitive token fields, and the GitHub Action can enforce these standards in CI/CD pipelines by failing builds if scans detect exposed JWT-related fields.

Organizations following compliance frameworks such as OWASP API Top 10, SOC2, and GDPR will find that these code practices reduce the risk of exposing personally identifiable information or privilege indicators through token handling.