Memory Leak in Flask with Hmac Signatures

A memory leak in a Flask application that uses Hmac Signatures typically arises when signature verification logic or request processing retains references to request data, byte buffers, or intermediate objects on each request. Because Hmac verification operates on the raw request body and often on headers, patterns such as accumulating data in global caches, storing entire request payloads for debugging, or repeatedly signing large payloads without releasing references can cause unbounded memory growth over time.

In Flask, developers sometimes compute Hmac signatures by reading request.get_data() and then keep a reference to the data or to the request context in a module-level dictionary for logging or replay detection. If the dictionary is keyed by something that does not get cleaned up (e.g., timestamps or raw headers), each request adds entries that are never removed, leading to a memory leak. Large JSON or multipart payloads exacerbate this because the full body remains in memory while the application processes the signature and performs business logic.

Another scenario involves repeated use of cryptographic helpers that allocate buffers for Hmac operations but fail to release them promptly due to long-lived objects or circular references. For example, creating a new hmac.new() instance per request is usually fine, but attaching those instances or their internal state to a global pool without clearing it will retain memory. This becomes a practical denial-of-service vector under sustained traffic, where memory usage grows until the process is restarted or the container is killed.

Flask’s development server is single-threaded by default, but in production you often run with threaded workers. Without proper scoping, global caches or module-level variables shared across threads can accumulate entries faster than any periodic cleanup, and the leak is compounded by the volume of requests and the size of payloads being signed. The leak may not be immediately visible in small tests, but under continuous load it manifests as increasing resident memory and eventual performance degradation or process restarts.

Because middleBrick scans the unauthenticated attack surface and includes checks such as Input Validation and Unsafe Consumption, it can surface indicators that an API endpoint processes large payloads with Hmac Signatures while exhibiting signs of resource handling issues. Although middleBrick detects and reports such patterns with severity and remediation guidance, it does not fix the leak; developers must review request handling code and ensure that references to request data are released promptly.

Remediation centers on ensuring that per-request data is not retained beyond the request lifecycle and that cryptographic operations do not create long-lived references. Use request-scoped storage, avoid global accumulators, and explicitly release large buffers when they are no longer needed.

Below are concrete, working examples of correct Hmac signature handling in Flask.

Example 1: Minimal, request-scoped Hmac verification without global state

import hashlib
import hmac
from flask import Flask, request, jsonify

app = Flask(__name__)
SECRET_KEY = b'super-secret-key'

def verify_hmac_signature(data: bytes, signature: str) -> bool:
    computed = hmac.new(SECRET_KEY, data, hashlib.sha256).hexdigest()
    return hmac.compare_digest(computed, signature)

@app.route('/api/order', methods=['POST'])
def create_order():
    data = request.get_data()
    signature = request.headers.get('X-Signature')
    if not signature or not verify_hmac_signature(data, signature):
        return jsonify({'error': 'invalid signature'}), 401
    # process order without keeping references to data
    return jsonify({'status': 'ok'})

Example 2: Avoiding module-level caches; using local variables only

import hashlib
import hmac
from flask import Flask, request, jsonify

app = Flask(__name__)
SECRET_KEY = b'super-secret-key'

def compute_signature(payload: bytes, key: bytes) -> str:
    return hmac.new(key, payload, hashlib.sha256).hexdigest()

@app.route('/api/resource', methods=['PUT'])
def update_resource():
    payload = request.get_data()
    incoming_sig = request.headers.get('X-Api-Signature')
    expected = compute_signature(payload, SECRET_KEY)
    if not hmac.compare_digest(expected, incoming_sig):
        return jsonify({'error': 'bad signature'}), 403
    # do not store payload or signature anywhere globally
    result = {'id': 'resource-123', 'verified': True}
    return jsonify(result)

Example 3: Streaming large payloads with size limits to reduce memory pressure

from flask import Flask, request, jsonify
import hashlib
import hmac

app = Flask(__name__)
SECRET_KEY = b'super-secret-key'
MAX_PAYLOAD = 1024 * 1024  # 1 MB

def verify_in_stream(key: bytes, stream, expected_sig: str) -> bool:
    h = hmac.new(key, digestmod=hashlib.sha256)
    chunk = stream.read(8192)
    while chunk:
        h.update(chunk)
        if h.digest_size > 1024 * 1024:  # defensive guard
            break
        chunk = stream.read(8192)
    return hmac.compare_digest(h.hexdigest(), expected_sig)

@app.route('/api/upload', methods=['POST'])
def upload():
    # Enforce a max content length at the framework level
    request.environ['CONTENT_LENGTH'] = request.content_length or '0'
    if int(request.content_length or 0) > MAX_PAYLOAD:
        return jsonify({'error': 'payload too large'}), 413
    expected_sig = request.headers.get('X-Signature')
    if not expected_sig or not verify_in_stream(SECRET_KEY, request.stream, expected_sig):
        return jsonify({'error': 'invalid signature'}), 401
    return jsonify({'status': 'accepted'})

Best practices to prevent leaks with Hmac in Flask

• Never append request bodies or signatures to global lists or dicts for logging; use structured logging with bounded retention instead.
• Prefer request.get_data(cache=False) if you need the body only once and want to avoid caching by Flask’s input stream wrapper.
• Use hmac.compare_digest to prevent timing attacks when comparing signatures.
• Set explicit MAX_CONTENT_LENGTH in Flask config to prevent unbounded memory consumption from large payloads.
• Ensure cryptographic helpers are local to the request and not stored on long-lived objects or class attributes.

These patterns reduce the risk of memory leaks and ensure Hmac verification remains both secure and memory-efficient. The changes can be validated by load testing the endpoints and monitoring memory usage over time.