Stack Overflow in Flask with Cockroachdb

A Stack Overflow in Flask with Cockroachdb typically arises when unbounded or recursive data structures are constructed in application logic, for example by traversing hierarchical rows (e.g., org_unit → parent_id references) without depth limits. In Flask, view functions that issue recursive or iterative SQL queries can enter infinite recursion or consume excessive memory when the data graph contains cycles or very deep paths, leading to process exhaustion. Cockroachdb, while resilient to node failures, does not inherently prevent application-level recursion from running away; it simply stores and serves rows as requested.

Consider a Flask endpoint that fetches an entire tree by recursively querying children. If the tree is deep or contains cycles (e.g., a row pointing to an ancestor as its parent), the recursion in Python can overflow the call stack. Because the scan is unauthenticated and tests input validation among 12 parallel checks, middleBrick can surface indicators such as missing depth controls or missing cycle detection. Unsafe Consumption and Property Authorization checks may further reveal endpoints where user-supplied identifiers traverse relationships without caps, enabling an attacker to force resource growth that manifests as a Stack Overflow symptom.

Example of a vulnerable pattern in Flask with Cockroachdb:

import logging
from flask import Flask, request, jsonify
import psycopg2

app = Flask(__name__)

def get_db():
    return psycopg2.connect(
        host="localhost",
        port=26257,
        dbname="company",
        user="app_user",
        password="secret"
    )

@app.route("/org/")
def org_tree(org_id):
    conn = get_db()
    cur = conn.cursor()
    nodes = []
    seen = set()

    def walk(node_id):
        if node_id in seen:
            return
        seen.add(node_id)
        cur.execute("SELECT id, name, parent_id FROM org_units WHERE id = %s", (node_id,))
        row = cur.fetchone()
        if row is None:
            return
        nodes.append(row)
        cur.execute("SELECT id FROM org_units WHERE parent_id = %s", (node_id,))
        for (child,) in cur.fetchall():
            walk(child)  # unbounded recursion; cycles cause Stack Overflow

    walk(org_id)
    cur.close()
    conn.close()
    return jsonify(nodes)

This pattern is susceptible because walk() recurses without depth limits and does not guard against cycles beyond the seen set (which may grow large). In a CI/CD pipeline, the GitHub Action can be configured to fail the build if risk scores exceed your threshold, preventing such endpoints from deploying.

Additionally, unauthenticated LLM endpoints—if present—could be probed by the LLM/AI Security checks for output exfiltration or prompt injection, but the primary concern here is the recursive traversal that can manifest as a Stack Overflow under malicious input conditions. The findings will include remediation guidance emphasizing depth capping and iterative traversal.

Remediation focuses on replacing unbounded recursion with controlled, iterative traversal and enforcing explicit depth and cycle limits. Use an iterative stack or breadth-first approach, and cap the number of visited nodes to prevent resource exhaustion. Below is a secure version of the org tree endpoint using iterative traversal with a configurable max_depth.

import logging
from flask import Flask, request, jsonify
import psycopg2

app = Flask(__name__)
MAX_DEPTH = 20

def get_db():
    return psycopg2.connect(
        host="localhost",
        port=26257,
        dbname="company",
        user="app_user",
        password="secret"
    )

@app.route("/org/")
def org_tree_safe(org_id):
    conn = get_db()
    cur = conn.cursor()
    nodes = []
    visited = set()
    stack = [(org_id, 0)]  # (node_id, depth)

    while stack:
        node_id, depth = stack.pop()
        if node_id in visited:
            continue
        if depth > MAX_DEPTH:
            logging.warning(f"Depth limit exceeded at node {node_id}")
            continue
        visited.add(node_id)
        cur.execute("SELECT id, name, parent_id FROM org_units WHERE id = %s", (node_id,))
        row = cur.fetchone()
        if row is None:
            continue
        nodes.append(row)
        cur.execute("SELECT id FROM org_units WHERE parent_id = %s", (node_id,))
        for (child,) in cur.fetchall():
            if child not in visited:
                stack.append((child, depth + 1))

    cur.close()
    conn.close()
    return jsonify(nodes)

This approach bounds recursion depth, avoids call-stack growth, and mitigates Stack Overflow risks. The iterative stack ensures constant call-stack usage regardless of tree depth. You can further harden the endpoint by validating org_id format (e.g., integer or UUID regex) to prevent injection-style traversal abuse.

For continuous protection, add the middleBrick CLI to your scripts: middlebrick scan <url>, or integrate the GitHub Action to fail builds if the security score drops below your chosen threshold. The MCP Server allows AI coding assistants to scan APIs directly from your IDE, surfacing issues early during development.

Mapping to compliance frameworks such as OWASP API Top 10 helps prioritize fixes; here, the primary relevant category is Broken Object Level Authorization (BOLA) / IDOR and Unsafe Consumption, where missing depth and cycle controls can lead to excessive resource usage. Remediation guidance from middleBrick will reference these checks and provide prioritized steps.