Rate Limiting, Quotas & Abuse Protection · Abuse Protection and Operations
Abuse Signals and Detection
Detect abuse patterns rate limits can't catch.
Rate limits stop volume; abuse often hides below thresholds. Collect signals — failure ratios, new accounts per IP/device, velocity of sensitive actions (payment methods added, invites sent), geographic anomalies, disposable email domains — and feed them into rules or risk scores. Respond proportionally: step-up auth, review queues, shadow bans, or blocks. Treat limit hits and risk decisions as security events in your SIEM.
Abuse signals
| Signal | Indicates |
|---|---|
| High login failure ratio per IP/ASN | Credential stuffing |
| Many signups per device fingerprint | Fake account farms |
| Many card attempts with small amounts | Card testing (carding) |
| Sudden spike in invites/messages | Spam via compromised accounts |
| Impossible travel | Account takeover |
# Example risk rule (pseudo)
if failures_by_ip_15m > 50 and distinct_accounts_by_ip_15m > 20: challenge(ip); alert("stuffing")
if card_attempts_by_account_1h > 5 and avg_amount < 2.00: block_payments(account); review(account)
Common pitfalls
- No feedback loop from incidents to rules.
- Blocking whole countries/ASNs without considering real users.
- Signals not retained long enough for investigations.
Key points
- Signals beyond volume
- Proportional responses
- Security events in SIEM
Further reading: OWASP Automated Threats
Practice questions
What is card testing and how is it detected?
Answer
Fraudsters validate stolen cards with many small transactions; detect it via high attempt velocity, many cards per account/device and low amounts, then block and review.
Why respond to abuse proportionally rather than always blocking?
Answer
False positives hurt real users; graduated responses (challenge, step-up auth, review) limit damage while preserving legitimate access.
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