Rate limiting is often treated as a security control in mobile systems. In practice, it behaves more like a signal generator.
Why the Model Breaks
Attackers can distribute traffic across IPs, tokens, and devices, keeping each source under thresholds while overwhelming the system in aggregate.
The Architectural Shift
Effective systems bind limits to backend-observed behavior, cost, and outcomes rather than client-supplied identity.
A Real Incident
A production fraud spike stayed under every configured limit. The limiter worked as designed. The assumptions were wrong.
Read the full analysis on Medium.
https://medium.com/@vaibhav.shakya786/how-mobile-api-rate-limiting-fails-in-real-attacks-c0917cf15527
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