Stack Overflow in Flask

Stack overflow vulnerabilities occur when a program consumes more memory on the call stack than is available, typically due to unbounded recursion. While Python’s managed runtime and recursion limits make classic C-style stack overflows rare, Flask applications can still experience stack overflow conditions through recursive logic in application code or template rendering. These issues often manifest as denial-of-service (DoS) vulnerabilities when an attacker triggers excessive recursion.

Recursive View Functions and Decorators
Flask’s flexibility with decorators and before_request/after_request hooks can inadvertently create recursive call chains. Consider a scenario where a before_request function conditionally invokes the current view function again, or a decorator that wraps a view and calls the wrapped function multiple times without a termination condition. An attacker who can influence the condition (e.g., via a query parameter) could cause infinite recursion.

from flask import Flask, request, abort

app = Flask(__name__)

@app.before_request
def check_auth():
    if request.args.get('retry') == 'true':
        # Dangerous: re-invokes the current request handler
        return app.view_functions[request.endpoint]()

@app.route('/data')
def data():
    return 'secret'

In this example, accessing /data?retry=true causes the before_request hook to call the data view again, which re-triggers the hook, leading to infinite recursion and eventual stack overflow.

Recursive Processing of User Input
If an application processes nested data structures (e.g., JSON, XML) with a custom recursive function without depth limits, an attacker can supply deeply nested payloads to exhaust the stack. Flask itself uses iterative parsers for JSON, but developers sometimes write recursive traversals for business logic.

import json
from flask import Flask, request

app = Flask(__name__)

def sum_numbers(obj):
    if isinstance(obj, dict):
        return sum(sum_numbers(v) for v in obj.values())
    elif isinstance(obj, list):
        return sum(sum_numbers(item) for item in obj)
    elif isinstance(obj, (int, float)):
        return obj
    return 0

@app.route('/sum', methods=['POST'])
def sum_endpoint():
    data = json.loads(request.data)
    total = sum_numbers(data)  # Recursive; no depth limit
    return {'sum': total}

Sending a JSON object with thousands of nested arrays/dicts could exceed Python’s recursion limit (default ~1000), raising a RecursionError and crashing the worker.

Server-Side Template Injection (SSTI) Leading to Recursive Rendering
Flask’s default templating engine, Jinja2, supports template inheritance and macros that can be recursive. If an attacker can inject template code (via unsanitized user input in render_template_string or similar), they might craft a template that recursively includes itself until the stack overflows.

from flask import Flask, render_template_string

app = Flask(__name__)

@app.route('/hello')
def hello():
    name = request.args.get('name', 'World')
    # Vulnerable: direct rendering of user-controlled template
    template = f'{{% extends "{name}" %}}' if name else 'Hello'
    return render_template_string(template)

By setting name to the current endpoint’s name (or a circular chain), an attacker could cause Jinja2 to recursively resolve templates until stack overflow.

Detecting stack overflow vulnerabilities in Flask APIs requires testing for unbounded recursion in request handling and input processing. Since middleBrick performs black-box scanning without source code access, it identifies these issues by observing server behavior under stress.

Testing for Recursive Endpoints
middleBrick’s Input Validation check sends payloads designed to trigger recursion. For endpoints that accept structured data (JSON/XML), it submits increasingly nested payloads (e.g., JSON with 500, 1000, 2000 levels of nesting) and monitors for crashes, timeouts, or RecursionError responses. For potential recursive view patterns, it probes query parameters that might influence control flow (like the retry parameter in the earlier example) by sending repeated requests with crafted values and looking for error patterns indicative of stack exhaustion.

SSTI and Template Recursion
While middleBrick does not directly scan for SSTI (as it falls under Input Validation), its input fuzzing includes template-like syntax in parameters (e.g., {{ recursive }}) to see if the server responds with template errors or exhibits recursive behavior. If an endpoint reflects template syntax in responses or uses render_template_string with user input, middleBrick may flag it for further investigation.

Example Scan Output
After scanning a vulnerable Flask API, middleBrick’s report might include a finding like:

This indicates that the scanner successfully triggered a stack overflow condition, and the remediation should focus on adding recursion depth limits or iterative processing.

You can integrate middleBrick into your CI/CD pipeline using the middlebrick CLI or GitHub Action to automatically catch such regressions before deployment. For example, run the CLI on your staging environment:

middlebrick scan https://staging.api.example.com --output json

And fail the build if the risk score exceeds your threshold.

Remediating stack overflow vulnerabilities in Flask involves eliminating unbounded recursion and safely handling user-controlled data. Flask provides several native features and libraries to help.

Replace Recursive Algorithms with Iterative Ones
For functions that traverse nested data, use explicit stacks or queues instead of recursion. The earlier sum_numbers example can be rewritten iteratively:

def sum_numbers_iterative(obj):
    total = 0
    stack = [obj]
    while stack:
        current = stack.pop()
        if isinstance(current, dict):
            stack.extend(current.values())
        elif isinstance(current, list):
            stack.extend(current)
        elif isinstance(current, (int, float)):
            total += current
    return total

This approach uses a Python list as a stack and avoids recursion entirely, making it safe for arbitrarily deep structures.

Enforce Depth Limits in Custom Parsers
If you must use recursion (e.g., for a custom data structure), enforce a maximum depth parameter and track it manually:

def sum_numbers_limited(obj, max_depth=100, current_depth=0):
    if current_depth > max_depth:
        raise ValueError('Exceeded maximum depth')
    if isinstance(obj, dict):
        return sum(sum_numbers_limited(v, max_depth, current_depth+1) for v in obj.values())
    # ... similar for list and numbers

However, iterative solutions are generally safer and more scalable.

Secure Template Handling
Never pass user-controlled strings directly to render_template_string or render_template with dynamic template names. If you must use dynamic templates, whitelist allowed template names or use a sandboxed environment. Jinja2 provides a SandboxedEnvironment that restricts access to dangerous attributes and methods:

from jinja2 import SandboxedEnvironment

sandbox = SandboxedEnvironment()

@app.route('/hello')
def hello():
    name = request.args.get('name', 'default')
    if name not in ['default', 'greeting']:
        abort(400)
    template = sandbox.from_string(f'Hello {name}')
    return template.render()

Additionally, set a recursion_limit in Jinja2 to prevent recursive template attacks (Flask’s default Jinja2 environment already sets a recursion limit of 10).

Use Safe Parsing Libraries
For XML, always use defusedxml to prevent billion laughs and entity expansion attacks that could also lead to recursion:

from defusedxml.ElementTree import parse

@app.route('/parse-xml', methods=['POST'])
def parse_xml():
    tree = parse(request.stream)
    # Process tree safely

For JSON, the standard json module is iterative and safe, but you can also set a parse_constant to limit certain constructs.

General Flask Best Practices

• Avoid recursive calls in before_request, after_request, and decorators. If you need to re-invoke logic, factor it into a helper function instead of calling the view again.
• Set a global recursion limit if necessary (via sys.setrecursionlimit), but note this only masks the problem and can make crashes more severe. Prefer iterative designs.
• Use middleBrick’s continuous monitoring (available in Pro and Enterprise plans) to scan your APIs regularly and alert on new findings. The GitHub Action can block merges if a stack overflow risk is introduced.

By following these remediation steps, you can eliminate stack overflow risks in your Flask APIs and maintain a robust security posture.