Integer Overflow in Flask with Bearer Tokens

An integer overflow in a Flask API that uses Bearer tokens can occur when numeric claims inside a token (e.g., exp, nbf, custom IDs, or rate-limit counters) are parsed and used in arithmetic without validating size or type. For example, if a token carries a large integer in a custom field like quota_limit, and the server computes remaining_quota = quota_limit - used, an attacker supplying a very large quota_limit can trigger an overflow. In C-based extensions or when values are coerced into fixed-size integers, the result can wrap to a small or negative number, bypassing limits or causing logic errors.

In Flask, this often surfaces in middleware or resource-checking code that reads token payloads and performs arithmetic on numeric claims. Consider a route that enforces a per-user request budget using a numeric claim: if the claim is large enough to overflow a 32-bit integer during subtraction or comparison, the check can evaluate incorrectly, allowing excessive requests or exposing private data. The unauthenticated attack surface tested by middleBrick includes input validation and authorization checks, which can flag such logic flaws when spec definitions (OpenAPI 2.0/3.0/3.1) define numeric constraints that runtime behavior does not enforce.

When Bearer tokens are involved, the server must parse and validate the token before using any numeric field. If the token is untrusted and the parsed integer is used in calculations without range checks, an overflow can lead to authorization bypass (e.g., treating an excessive remaining quota as zero) or data exposure. middleBrick’s checks for BOLA/IDOR, Property Authorization, and Input Validation are designed to surface these risks by correlating spec definitions with runtime behavior, highlighting where arithmetic on token-derived values can become unsafe.

Real-world patterns mirror known attack vectors such as CVE-2022-29369 (an integer overflow in signature verification logic) and the OWASP API Top 10 A05:2023 (Broken Function Level Authorization), where boundary conditions in numeric processing lead to privilege escalation or data leakage. In Flask extensions that decode JWTs, ensure values are treated as arbitrary-precision integers (Python int) and checked against expected ranges before any arithmetic. middleBrick’s LLM/AI Security checks also probe for unsafe consumption patterns, which can include mishandling of large numeric fields in token-driven workflows.

Example risk scenario: a Flask route reads token["quota"] and computes remaining quota using subtraction. If token["quota"] is a large integer, the subtraction can overflow in underlying libraries, causing the check to incorrectly allow more requests than intended. By scanning with middleBrick’s OpenAPI/Swagger spec analysis (with full $ref resolution) alongside runtime tests, you can detect mismatches between declared integer constraints and actual behavior, reducing the window for exploitation.

Remediation focuses on validating and safely handling numeric fields from Bearer tokens. Always treat token claims as untrusted input, enforce strict ranges, and avoid arithmetic on raw values without checks. Use Python’s arbitrary-precision integers and explicit boundary validation before any computation.

from flask import Flask, request, jsonify
import jwt
from functools import wraps

app = Flask(__name__)
SECRET_KEY = 'your-secret-key'

# Safe decoding and validation helper
def validate_token_claims(claims):
    # Enforce ranges for numeric fields
    if 'quota' in claims:
        quota = claims['quota']
        if not isinstance(quota, int) or quota < 0 or quota > 10**6:
            raise ValueError('Invalid quota value')
    if 'exp' in claims:
        # Ensure exp is within acceptable skew window
        import time
        if abs(claims['exp'] - int(time.time())) > 300:
            raise ValueError('Token expired or skew too large')
    return claims

@app.route('/api/resource')
def access_resource():
    auth = request.headers.get('Authorization', '')
    if not auth.startswith('Bearer '):
        return jsonify({'error': 'missing_bearer_token'}), 401
    token = auth.split(' ', 1)[1]
    try:
        payload = jwt.decode(token, SECRET_KEY, algorithms=['HS256'])
        claims = validate_token_claims(payload)
        # Safe arithmetic: use Python int, validate before operating
        used = int(request.headers.get('X-Used-Quota', '0'))
        if not isinstance(used, int) or used < 0:
            raise ValueError('Invalid used quota')
        remaining = claims['quota'] - used  # Python int; no overflow
        if remaining < 0:
            return jsonify({'error': 'quota_exhausted'}), 403
        return jsonify({'remaining_quota': remaining})
    except jwt.ExpiredSignatureError:
        return jsonify({'error': 'token_expired'}), 401
    except (jwt.InvalidTokenError, ValueError) as e:
        return jsonify({'error': str(e)}), 400

if __name__ == '__main__':
    app.run(debug=False)

For continuous protection, integrate middleBrick’s CLI tool to scan from terminal with middlebrick scan <url>, or add the GitHub Action to your CI/CD pipeline to fail builds if security scores drop below your threshold. These workflows help catch regressions where token handling or arithmetic logic changes over time.

Additionally, leverage the Pro plan’s continuous monitoring to schedule regular scans and receive Slack/Teams alerts when risk scores degrade. The MCP Server enables scanning APIs directly from your AI coding assistant, helping you validate token handling patterns during development. Always pair these scans with code reviews focused on numeric validation and boundary conditions to reduce the likelihood of integer overflow issues in Flask APIs using Bearer tokens.