Race Condition - Limit Bypass Scanner
Exploits race conditions to bypass rate limits and quotas.
What is Race Condition - Limit Bypass?
Rate Limit Bypass race conditions occur when rate limiting or quota enforcement isn't atomic. Attackers send many requests simultaneously, and before any request increments the counter, all requests pass the limit check. This allows exceeding intended limits for API calls, voting, coupon usage, or resource allocation.
Why is This Important?
Rate limits protect against abuse, brute force, and resource exhaustion. Bypassing them enables credential stuffing, vote manipulation, free tier abuse, coupon stacking, and denial of service. A system designed for 100 requests/minute might receive thousands, potentially causing financial loss or system compromise.
How It Works
1. Attack Surface Mapping
Identifies complex attack vectors including race conditions, desync points, and logic flaws in your application.
2. Advanced Exploitation
Executes sophisticated attack techniques that bypass traditional security controls and detection mechanisms.
3. Impact Assessment
Demonstrates real-world impact with detailed exploitation chains and business risk analysis.
Key Capabilities
Expert-level security testing for sophisticated vulnerabilities that evade traditional scanning tools.
- Race condition and timing attack detection
- Request smuggling and desync analysis
- Business logic flaw identification
- Chained exploit development
- Protocol-level vulnerability testing
Frequently Asked Questions
How do rate limit bypass attacks work?
The limit check happens: read counter → check if under limit → process request → increment counter. Attackers send parallel requests that all read the same (under limit) counter value simultaneously. Each passes the check before any increments the counter.
What techniques are used to exploit this?
Techniques: HTTP/1.1 pipelining, HTTP/2 multiplexing, multiple parallel connections, distributed requests from different IPs, precise timing synchronization, exploiting load balancer distribution, and targeting stateless rate limiters that don't share state between nodes.
How can I test for rate limit race conditions?
Testing approaches: use tools like Turbo Intruder for precisely-timed parallel requests, single-packet attacks in HTTP/2, script-based concurrent request flooding, testing across multiple IP addresses, and comparing actual vs. allowed request counts.
How do I implement race-proof rate limiting?
Solutions: use atomic increment-and-check operations (Redis INCR), implement sliding window algorithms with atomic counters, use distributed rate limiters with proper synchronization, apply limits before processing begins, and consider token bucket algorithms with atomic token consumption.
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