Password Spraying with Hmac Signatures

Password spraying in HMAC signature implementations exploits the deterministic nature of cryptographic signing. When attackers target HMAC-based authentication systems, they don't attempt to brute-force a single account—instead, they systematically test common passwords across many valid HMAC keys or API credentials.

The attack pattern typically follows this flow: An attacker obtains a list of valid HMAC keys (often through enumeration or public exposure), then systematically tests each key against a small set of common passwords or shared secrets. Since HMAC signatures rely on a secret key combined with a message, attackers can craft requests that appear legitimate if they discover the correct key-password combination.

Consider this vulnerable HMAC implementation:

import hmac
import hashlib
import time

def generate_hmac(key, message):
    return hmac.new(key.encode(), message.encode(), hashlib.sha256).hexdigest()

# Vulnerable endpoint
def verify_request(request):
    key = request.headers.get('X-API-Key')
    signature = request.headers.get('X-Signature')
    message = request.body
    
    # No rate limiting on key verification
    expected = generate_hmac(key, message)

The vulnerability emerges when this endpoint lacks per-key rate limiting. An attacker can iterate through thousands of valid API keys, each time testing common passwords or secrets. Since each key is unique, the system treats each attempt as a separate entity, never triggering account lockout mechanisms.

Another manifestation occurs in HMAC-based JWT implementations where the secret key is weak or reused across multiple services. Attackers can perform offline cracking once they capture a valid token:

import jwt
import time

# Weak secret key
secret = "password123"

# Generate token with weak secret
token = jwt.encode({'user_id': 1, 'exp': time.time() + 3600}, secret, algorithm='HS256')

# Attacker captures token and performs offline brute force
from jwt import ExpiredSignatureError
for guess in common_passwords:
    try:
        decoded = jwt.decode(token, guess, algorithms=['HS256'])
        print(f"Found secret: {guess}")
        break
    except ExpiredSignatureError:
        pass
    except jwt.DecodeError:
        continue

The HMAC signature itself becomes the attack vector when implementations don't properly validate the key's strength or rotation schedule. Attackers exploit predictable key generation patterns, such as sequential numeric IDs or timestamp-based keys, which significantly reduces the search space for password spraying attacks.

Detecting password spraying in HMAC signature systems requires monitoring authentication patterns across multiple dimensions. The key indicators include unusual request timing patterns, anomalous key usage distributions, and signature verification failures that follow predictable sequences.

Network-level detection focuses on identifying the characteristic "low and slow" attack pattern. Unlike traditional brute force attacks that generate high request volumes, password spraying attacks distribute attempts across many accounts to avoid detection:

import redis
import datetime

r = redis.Redis()

def detect_password_spraying(client_ip, api_key, success):
    # Track requests per API key
    key_requests = r.incr(f"key:{api_key}:requests")
    r.expire(f"key:{api_key}:requests", 3600)
    
    # Track requests per client IP
    ip_requests = r.incr(f"ip:{client_ip}:requests")
    r.expire(f"ip:{client_ip}:requests", 3600)
    
    # Detect spray patterns: many keys, few requests each
    if key_requests < 5 and ip_requests > 50:
        alert_admin(f"Potential password spray from {client_ip}")
    
    # Track failed attempts per key
    if not success:
        failed = r.incr(f"key:{api_key}:failed")
        r.expire(f"key:{api_key}:failed", 3600)
        if failed > 10:
            alert_admin(f"High failure rate for key {api_key}")

middleBrick's HMAC signature scanning specifically targets these vulnerabilities by testing the unauthenticated attack surface. The scanner identifies endpoints vulnerable to password spraying by:

• Enumerating valid HMAC keys through timing analysis and error message differentiation
• Testing common weak secrets against discovered keys
• Analyzing HMAC implementation patterns for predictable key generation
• Checking for missing rate limiting on signature verification endpoints

The scanner's LLM/AI security module adds another layer by detecting if HMAC-protected endpoints serve AI/ML models that could be exploited for cost amplification or data exfiltration once authentication is compromised.

Code-level detection involves instrumenting the HMAC verification process to log suspicious patterns:

from collections import defaultdict
import logging

spray_detection = defaultdict(lambda: {'attempts': 0, 'keys': set(), 'last_attempt': None})

def verify_hmac_with_detection(key, message, signature):
    current_time = time.time()
    record = spray_detection[key]
    
    # Detect rapid succession attempts with different keys
    if record['last_attempt'] and current_time - record['last_attempt'] < 60:
        record['attempts'] += 1
        if record['attempts'] > 20 and len(record['keys']) > 10:
            logging.warning(f"Password spray detected: {len(record['keys'])} keys attempted")
    
    record['last_attempt'] = current_time
    return verify_hmac(key, message, signature)

Remediating password spraying vulnerabilities in HMAC signature systems requires a defense-in-depth approach that addresses both the cryptographic implementation and the authentication workflow. The most effective solutions combine strong key management, intelligent rate limiting, and secure key derivation practices.

First, implement per-key rate limiting that prevents attackers from testing multiple keys rapidly:

from redis_rate_limit import RateLimit
import redis

r = redis.Redis()
rate_limiter = RateLimit(r)

def verify_request_with_rate_limiting(request):
    key = request.headers.get('X-API-Key')
    message = request.body
    
    # Rate limit per API key (5 requests per minute)
    if not rate_limiter.limit(f"hmac_key:{key}", 5, 60):
        return {'error': 'Rate limit exceeded'}, 429
    
    # Rate limit per client IP (10 requests per minute across all keys)
    client_ip = request.remote_addr
    if not rate_limiter.limit(f"hmac_ip:{client_ip}", 10, 60):
        return {'error': 'Rate limit exceeded'}, 429
    expected = generate_hmac(key, message)
    if not hmac.compare_digest(expected, signature):
        return {'error': 'Invalid signature'}, 401

Second, strengthen HMAC key generation to prevent predictable patterns that facilitate spraying attacks:

import secrets
import hashlib

def generate_secure_hmac_key(api_key_id):
    # Use cryptographically secure random generation
    random_component = secrets.token_hex(32)
    
    # Incorporate unpredictable elements
    timestamp = str(int(time.time() * 1000))
    nonce = secrets.token_hex(8)
    
    # Create key with multiple entropy sources
    combined = f"{api_key_id}:{timestamp}:{nonce}:{random_component}"
    return hashlib.sha256(combined.encode()).hexdigest()

Third, implement key rotation policies that invalidate HMAC keys after specific usage patterns or time periods:

import datetime
from cachetools import TTLCache

key_usage = TTLCache(maxsize=10000, ttl=86400)  # 24-hour TTL

def verify_with_rotation(key, message, signature):
    # Check if key needs rotation (based on usage count)
    usage_count = key_usage.get(key, 0) + 1
    key_usage[key] = usage_count
    
    if usage_count > 1000:  # Rotate after 1000 uses
        rotate_hmac_key(key)
        return {'error': 'Key expired, please renew'}, 410
    # Verify signature
    expected = generate_hmac(key, message)
    if not hmac.compare_digest(expected, signature):
        return {'error': 'Invalid signature'}, 401

Finally, add anomaly detection to identify unusual HMAC key patterns:

from sklearn.ensemble import IsolationForest
import numpy as np

# Train anomaly detection on normal key usage patterns
normal_patterns = np.array([[5, 1], [3, 2], [7, 1], [2, 3]])  # [requests, unique_ips]
model = IsolationForest(contamination=0.1)
model.fit(normal_patterns)

def detect_anomalous_usage(key, request_count, unique_ips):br>    pattern = np.array([[request_count, unique_ips]])
        alert_admin(f"Anomalous HMAC key usage detected: {key}")