Stack Overflow in Flask with Bearer Tokens

A Flask API that uses Bearer token authentication can be exposed through a stack overflow when user-controlled input (such as a parameter, header, or JSON field) is reflected in error messages or logs. If a token is accepted via an Authorization header like Authorization: Bearer and the application processes it in a way that triggers deep call stacks—such as recursive parsing, deeply nested JWT claims, or unchecked string operations—a stack overflow can occur. This may happen, for example, when a maliciously crafted token or a manipulated input field causes the application to enter infinite recursion or consume excessive stack space during validation.

In a black-box scan, middleBrick tests the unauthenticated attack surface and checks whether responses reveal stack traces, memory errors, or timing differences that indicate resource exhaustion. When a stack overflow occurs, the server may return 500 errors or crash behavior, which can be leveraged to bypass authentication, cause denial of service, or obscure other attacks. Because Bearer tokens are often processed before route authorization, an overflow in token handling can prevent proper authentication checks from completing, leading to unauthorized access paths.

Consider a Flask route that decodes a JWT from the Bearer token and recursively validates nested claims without depth limits:

import jwt
def decode_token(token):
    # Unsafe: no depth limit on claims processing
    data = jwt.decode(token, options={"verify_signature": False})
    return process_claims_recursive(data)
def process_claims_recursive(obj, depth=0):
    if depth > 1000:
        raise ValueError("Depth limit exceeded")
    if isinstance(obj, dict):
        for k, v in obj.items():
            process_claims_recursive(v, depth + 1)
    elif isinstance(obj, list):
        for item in obj:
            process_claims_recursive(item, depth + 1)

An attacker can supply a token with deeply nested structures that trigger a stack overflow before signature verification is enforced, bypassing intended security checks. middleBrick’s checks for Input Validation, Authentication, and Unsafe Consumption are designed to surface these classes of risk by probing token handling paths and monitoring for abnormal error states or missing rate limits that could enable abuse.

To mitigate stack overflow risks when using Bearer tokens in Flask, validate and sanitize all inputs that influence token parsing and claim processing. Apply strict limits on recursion and avoid processing untrusted data in deep call stacks. Use iterative approaches instead of recursive ones, and enforce size and depth constraints on JWT payloads before decoding.

Below are concrete, secure code examples for Flask that replace unsafe recursive claim processing with safe validation and iterative handling:

import jwt
from flask import Flask, request, jsonify

app = Flask(__name__)
MAX_DEPTH = 10
MAX_TOKEN_SIZE = 4096  # reasonable limit in bytes

def safe_process_claims(obj, max_depth=MAX_DEPTH):
    stack = [(obj, 0)]
    while stack:
        current, depth = stack.pop()
        if depth > max_depth:
            raise ValueError("Claim processing depth limit exceeded")
        if isinstance(current, dict):
            for v in current.values():
                stack.append((v, depth + 1))
        elif isinstance(current, list):
            for item in current:
                stack.append((item, depth + 1))
    return True

@app.route("/protected")
def protected():
    auth = request.headers.get("Authorization")
    if not auth or not auth.startswith("Bearer "):
        return jsonify({"error": "missing_bearer_token"}), 401
    token = auth.split(" ", 1)[1].strip()
    if len(token.encode("utf-8")) > MAX_TOKEN_SIZE:
        return jsonify({"error": "token_too_large"}), 400
    try:
        data = jwt.decode(token, options={"verify_signature": False})
    except Exception:
        return jsonify({"error": "invalid_token"}), 401
    try:
        safe_process_claims(data)
    except ValueError:
        return jsonify({"error": "claim_processing_error"}), 400
    return jsonify({"status": "ok"})

Additional remediation steps include:

• Enforce request size limits in Flask using MAX_CONTENT_LENGTH to prevent large payloads that could exacerbate stack usage.
• Validate token structure before decoding: reject tokens with unexpected characters or excessive length in header or payload segments.
• Use iterative traversal for any user-influenced data structures, and set explicit recursion or iteration caps aligned with your security profile.
• Log suspicious patterns (e.g., repeated oversized tokens) without exposing stack traces to the client, and ensure error responses are generic.

These changes reduce the attack surface specific to Bearer token handling and help ensure that stack overflow conditions do not lead to authentication bypass or denial of service. middleBrick’s scans can verify that these mitigations are reflected in runtime behavior by checking for missing input validation, missing rate limiting, and unsafe consumption patterns.