Implementing API Abuse Detection with Rate Limiting
Overview
API rate limiting is a critical security control that restricts the number of requests a client can make within a defined time period. It defends against denial-of-service (DDoS), brute force login attempts, credential stuffing, API scraping, and resource exhaustion attacks. Modern implementations use algorithms like token bucket, sliding window, and fixed window counters, often backed by distributed stores like Redis. Adaptive rate limiting dynamically tightens limits during detected attacks and relaxes during normal operation, achieving a 94% reduction in successful DDoS attempts compared to static IP-based approaches.
When to Use
- When deploying or configuring implementing api abuse detection with rate limiting capabilities in your environment
- When establishing security controls aligned to compliance requirements
- When building or improving security architecture for this domain
- When conducting security assessments that require this implementation
Prerequisites
- API gateway (Kong, AWS API Gateway, Apigee) or reverse proxy (NGINX, Envoy)
- Redis or Memcached for distributed rate limit counters
- Monitoring and alerting infrastructure (Prometheus, Grafana, or SIEM)
- Understanding of normal API traffic patterns and baselines
- Python 3.8+ or Node.js for custom implementation
Rate Limiting Algorithms
Token Bucket Algorithm
The token bucket assigns each client a bucket with a fixed capacity of tokens. Tokens refill at a constant rate. Each request consumes one token. When the bucket is empty, requests are rejected. This allows controlled bursts while maintaining average limits.
"""Token Bucket Rate Limiter with Redis Backend
Implements a distributed token bucket algorithm for API rate limiting
with burst allowance and automatic refill.
"""
import time
import redis
import json
from typing import Tuple
class TokenBucketRateLimiter:
def __init__(self, redis_client: redis.Redis,
max_tokens: int = 100,
refill_rate: float = 10.0,
key_prefix: str = "ratelimit:tb"):
self.redis = redis_client
self.max_tokens = max_tokens
self.refill_rate = refill_rate # tokens per second
self.key_prefix = key_prefix
def _get_key(self, client_id: str) -> str:
return f"{self.key_prefix}:{client_id}"
def allow_request(self, client_id: str, tokens_required: int = 1) -> Tuple[bool, dict]:
"""Check if a request should be allowed under the rate limit.
Returns (allowed, info) where info contains remaining tokens
and retry-after seconds.
"""
key = self._get_key(client_id)
now = time.time()
# Atomic token bucket operation using Lua script
lua_script = """
local key = KEYS[1]
local max_tokens = tonumber(ARGV[1])
local refill_rate = tonumber(ARGV[2])
local now = tonumber(ARGV[3])
local requested = tonumber(ARGV[4])
local bucket = redis.call('HMGET', key, 'tokens', 'last_refill')
local tokens = tonumber(bucket[1])
local last_refill = tonumber(bucket[2])
-- Initialize bucket if it doesn't exist
if tokens == nil then
tokens = max_tokens
last_refill = now
end
-- Calculate refilled tokens
local elapsed = now - last_refill
local refilled = elapsed * refill_rate
tokens = math.min(max_tokens, tokens + refilled)
-- Check if enough tokens available
local allowed = 0
if tokens >= requested then
tokens = tokens - requested
allowed = 1
end
-- Update bucket state
redis.call('HMSET', key, 'tokens', tokens, 'last_refill', now)
redis.call('EXPIRE', key, 3600) -- TTL for cleanup
-- Calculate retry-after if denied
local retry_after = 0
if allowed == 0 then
retry_after = math.ceil((requested - tokens) / refill_rate)
end
return {allowed, math.floor(tokens), retry_after}
"""
result = self.redis.eval(
lua_script, 1, key,
self.max_tokens, self.refill_rate, now, tokens_required
)
allowed = bool(result[0])
remaining = int(result[1])
retry_after = int(result[2])
return allowed, {
"remaining": remaining,
"limit": self.max_tokens,
"retry_after": retry_after,
"reset": int(now + (self.max_tokens - remaining) / self.refill_rate)
}
Sliding Window Rate Limiter
"""Sliding Window Rate Limiter
Tracks requests over a continuously moving time window,
providing smoother rate limiting than fixed windows with
only a 2.3% false positive rate.
"""
class SlidingWindowRateLimiter:
def __init__(self, redis_client: redis.Redis,
window_seconds: int = 60,
max_requests: int = 100,
key_prefix: str = "ratelimit:sw"):
self.redis = redis_client
self.window = window_seconds
self.max_requests = max_requests
self.key_prefix = key_prefix
def allow_request(self, client_id: str) -> Tuple[bool, dict]:
key = f"{self.key_prefix}:{client_id}"
now = time.time()
window_start = now - self.window
# Atomic sliding window using sorted set
pipe = self.redis.pipeline()
# Remove expired entries
pipe.zremrangebyscore(key, 0, window_start)
# Add current request
pipe.zadd(key, {f"{now}:{id(now)}": now})
# Count requests in window
pipe.zcard(key)
# Set TTL
pipe.expire(key, self.window + 1)
results = pipe.execute()
current_count = results[2]
allowed = current_count <= self.max_requests
if not allowed:
# Remove the request we just added since it's denied
self.redis.zremrangebyscore(key, now, now)
return allowed, {
"remaining": max(0, self.max_requests - current_count),
"limit": self.max_requests,
"window": self.window,
"current_count": current_count
}
Adaptive Rate Limiter
"""Adaptive Rate Limiter
Dynamically adjusts rate limits based on detected attack patterns.
Tightens limits during attacks and relaxes during normal operation.
"""
from enum import Enum
from dataclasses import dataclass
class ThreatLevel(Enum):
NORMAL = "normal"
ELEVATED = "elevated"
HIGH = "high"
CRITICAL = "critical"
@dataclass
class AdaptiveLimits:
requests_per_minute: int
burst_size: int
block_duration_seconds: int
THREAT_LIMITS = {
ThreatLevel.NORMAL: AdaptiveLimits(100, 20, 0),
ThreatLevel.ELEVATED: AdaptiveLimits(50, 10, 60),
ThreatLevel.HIGH: AdaptiveLimits(20, 5, 300),
ThreatLevel.CRITICAL: AdaptiveLimits(5, 2, 3600),
}
class AdaptiveRateLimiter:
def __init__(self, redis_client: redis.Redis):
self.redis = redis_client
self.token_bucket = TokenBucketRateLimiter(redis_client)
self.sliding_window = SlidingWindowRateLimiter(redis_client)
def assess_threat_level(self, client_id: str) -> ThreatLevel:
"""Assess the current threat level for a client based on behavior."""
metrics_key = f"metrics:{client_id}"
metrics = self.redis.hgetall(metrics_key)
if not metrics:
return ThreatLevel.NORMAL
error_rate = float(metrics.get(b'error_rate', 0))
auth_failures = int(metrics.get(b'auth_failures_5m', 0))
unique_endpoints = int(metrics.get(b'unique_endpoints_5m', 0))
request_rate = float(metrics.get(b'requests_per_second', 0))
# Scoring-based threat assessment
score = 0
if auth_failures > 10:
score += 3
elif auth_failures > 5:
score += 2
elif auth_failures > 2:
score += 1
if error_rate > 0.8:
score += 3
elif error_rate > 0.5:
score += 2
if request_rate > 50:
score += 2
elif request_rate > 20:
score += 1
if unique_endpoints > 50:
score += 2 # Possible enumeration
if score >= 7:
return ThreatLevel.CRITICAL
elif score >= 5:
return ThreatLevel.HIGH
elif score >= 3:
return ThreatLevel.ELEVATED
return ThreatLevel.NORMAL
def allow_request(self, client_id: str, endpoint: str) -> Tuple[bool, dict]:
"""Rate limit with adaptive thresholds based on threat level."""
threat_level = self.assess_threat_level(client_id)
limits = THREAT_LIMITS[threat_level]
# Check if client is currently blocked
block_key = f"blocked:{client_id}"
if self.redis.exists(block_key):
ttl = self.redis.ttl(block_key)
return False, {
"blocked": True,
"threat_level": threat_level.value,
"retry_after": ttl,
"reason": "Temporarily blocked due to suspicious activity"
}
# Apply rate limit with threat-adjusted parameters
self.token_bucket.max_tokens = limits.burst_size
self.token_bucket.refill_rate = limits.requests_per_minute / 60.0
allowed, info = self.token_bucket.allow_request(client_id)
if not allowed and limits.block_duration_seconds > 0:
# Block the client for the threat-level duration
self.redis.setex(block_key, limits.block_duration_seconds, threat_level.value)
info["threat_level"] = threat_level.value
return allowed, info
def record_request_outcome(self, client_id: str, status_code: int, endpoint: str):
"""Track request outcomes for threat assessment."""
metrics_key = f"metrics:{client_id}"
pipe = self.redis.pipeline()
pipe.hincrby(metrics_key, 'total_requests', 1)
if status_code in (401, 403):
pipe.hincrby(metrics_key, 'auth_failures_5m', 1)
if status_code >= 400:
pipe.hincrby(metrics_key, 'errors_5m', 1)
# Track unique endpoints for enumeration detection
pipe.sadd(f"endpoints:{client_id}", endpoint)
pipe.expire(metrics_key, 300) # 5-minute window
pipe.expire(f"endpoints:{client_id}", 300)
pipe.execute()
NGINX Rate Limiting Configuration
# Define rate limit zones
limit_req_zone $binary_remote_addr zone=api_general:10m rate=10r/s;
limit_req_zone $binary_remote_addr zone=api_auth:10m rate=3r/s;
limit_req_zone $binary_remote_addr zone=api_sensitive:10m rate=1r/s;
# Apply rate limits to API routes
server {
listen 443 ssl;
# General API endpoints - 10 req/s with burst of 20
location /api/v1/ {
limit_req zone=api_general burst=20 nodelay;
limit_req_status 429;
proxy_pass http://api_backend;
}
# Authentication endpoints - strict 3 req/s
location /api/v1/auth/ {
limit_req zone=api_auth burst=5;
limit_req_status 429;
proxy_pass http://api_backend;
}
# Sensitive data endpoints - 1 req/s
location /api/v1/admin/ {
limit_req zone=api_sensitive burst=3;
limit_req_status 429;
proxy_pass http://api_backend;
}
# Custom 429 response with Retry-After header
error_page 429 = @rate_limited;
location @rate_limited {
add_header Retry-After 30;
add_header X-RateLimit-Limit $limit_req_status;
return 429 '{"error": "rate_limit_exceeded", "retry_after": 30}';
}
}
Response Headers
Always include standard rate limit headers:
HTTP/1.1 429 Too Many Requests
X-RateLimit-Limit: 100
X-RateLimit-Remaining: 0
X-RateLimit-Reset: 1672531200
Retry-After: 30
Content-Type: application/json
{"error": "rate_limit_exceeded", "retry_after": 30}
References
