Password Spraying in Flask with Hmac Signatures

Password spraying is an authentication technique where an attacker uses a small number of common passwords across many accounts to avoid account lockouts. In Flask applications that rely on HMAC signatures for request authentication, password spraying can be relevant when the signature is derived from or compared against a user-supplied password or when the HMAC verification logic introduces timing inconsistencies that leak whether a user exists.

Consider a Flask route that authenticates requests using an HMAC signature. The client computes an HMAC over a canonical string (e.g., timestamp + request path + body) using a shared secret derived from the user’s password. If the server retrieves the user record by username, computes the expected HMAC, and compares it with the client-provided signature using a naive string comparison, subtle timing differences can expose whether the username is valid. This user enumeration enables an attacker to iteratively test usernames while spraying common passwords, combining two weaknesses: a password spraying attack and an HMAC verification side-channel.

Additionally, if the application falls back to a default or dummy user when the username is not found, the HMAC verification may still proceed and fail slowly, further masking enumeration. Flask applications that accept JSON payloads with username and signature fields are particularly at risk if the HMAC comparison is not constant-time and the password-based key derivation is weak (e.g., using a fast hash without salt). In such cases, an attacker can automate password spraying across discovered usernames, observing whether requests succeed or fail in a way that hints at valid accounts, without triggering typical rate-limiting mechanisms that are applied per endpoint rather than per username.

Another subtle exposure arises when the HMAC scope includes the username. If usernames are predictable (e.g., email addresses), an attacker can precompute HMACs for common passwords and validate them against real users. Even without knowing the password, the attacker can identify which username-password pairs produce valid signatures through online probing, effectively coupling password spraying with HMAC validation logic. This is compounded if the server does not enforce strong rate limits on authentication attempts globally or per user, allowing iterative testing that would otherwise be throttled in a pure password-based system.

To mitigate risks when using HMAC signatures in Flask, use constant-time comparison, avoid user enumeration, and ensure robust key derivation. Below are concrete, secure patterns and code examples.

1. Constant-time HMAC verification

Always compare HMAC signatures using a constant-time routine to prevent timing leaks that enable username enumeration during password spraying.

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

def verify_hmac_signature(message: str, received_signature: str, secret: bytes) -> bool:
    expected = hmac.new(secret, message.encode('utf-8'), hashlib.sha256).hexdigest()
    return hmac.compare_digest(expected, received_signature)

@app.route('/api/action', methods=['POST'])
def api_action():
    data = request.get_json(force=True)
    username = data.get('username')
    signature = data.get('signature')
    # Retrieve user-specific secret securely; avoid early exit based on username existence
    user_secret = get_user_secret(username)  # returns a fixed-length secret or a dummy of same length
    if not verify_hmac_signature(build_canonical_string(request), signature, user_secret):
        return jsonify({'error': 'invalid signature'}), 401
    # Proceed with business logic
    return jsonify({'status': 'ok'}), 200

2. Avoid user enumeration in user lookup

Ensure that the flow for missing users is indistinguishable from valid users by using a dummy secret of the same length and computational cost.

import secrets

def get_user_secret(username: str) -> bytes:
    # In production, fetch the per-user secret from a secure store
    real_secret = user_store.get(username)
    if real_secret is None:
        # Return a dummy secret with the same length and properties to prevent timing leaks
        return secrets.token_bytes(32)
    return real_secret

3. Use salted key derivation for password-based HMAC keys

Do not use passwords directly as HMAC keys. Instead, derive a key using a memory-hard function with a unique salt per user.

import hashlib
import os
import binascii
from hashlib import pbkdf2_hmac

def derive_key(password: str, salt: bytes) -> bytes:
    # Use a sufficient iteration count (e.g., 600_000+ for modern security)
    return pbkdf2_hmac('sha256', password.encode('utf-8'), salt, 600000, dklen=32)

# During user setup:
salt = os.urandom(16)
stored_key = derive_key(user_password, salt)
# Store salt and derived key securely

4. Enforce global rate limiting and secure logging

Apply rate limits that are not username-dependent for authentication endpoints and ensure logs do not reveal whether a username exists.

# Example using Flask-Limiter (configure globally)
from flask_limiter import Limiter
limiter = Limiter(app, key_func=lambda: request.headers.get('X-Forwarded-For', request.remote_addr))

@app.route('/api/action', methods=['POST'])
@limiter.limit("5/minute;100/hour")
def api_action_limited():
    # ... verification as above