AbuseIPDB

@AbuseIPDB check the reputation of 203.0.113.42 over the last 90 days and tell me if we should block it

The IP 203.0.113.42 has an abuse confidence score of 87% based on 23 reports in the last 90 days. Categories include SSH brute-force (18 reports), port scanning (4 reports), and web application attacks (1 report). Last reported 3 days ago. Given the high confidence score and recent activity, blocking this IP is recommended for SSH and web-facing services.

This example shows the Check IP Reputation tool paired with AI reasoning to make a blocking recommendation. Requires an API key with read access. The abuse confidence score is AbuseIPDB's proprietary metric — higher scores indicate more reliable abuse signals, but you should still correlate with your own logs before taking action.

@AbuseIPDB report 198.51.100.7 for SSH brute-force attempts we logged in the last hour, include our server logs as context

Report submitted successfully. IP 198.51.100.7 has been flagged under category 18 (SSH brute-force). Your report ID is 4829371. This IP now has 12 total reports in AbuseIPDB's database, raising its abuse confidence score from 64% to 71%. Note: reports are public and contribute to the community threat database.

This demonstrates the reporting side-effect: your submission becomes part of AbuseIPDB's public dataset and affects the IP's reputation score. Requires an API key with write permissions. Be certain of abuse before reporting — false positives pollute the shared database and can trigger rate-limit penalties on your account.

@AbuseIPDB pull the top 500 most-reported IPs from the last 7 days and draft a firewall rule set blocking any with confidence scores above 90%

Retrieved 500 IPs from AbuseIPDB's blacklist (7-day window). 143 IPs exceed 90% confidence: 89 flagged for SSH attacks, 31 for web exploits, 23 for port scanning. Proposed iptables rules block these 143 IPs on ports 22, 80, and 443. Rule set includes comments with abuse categories and report counts. Recommend testing in log-only mode first — two IPs (198.51.100.15, 203.0.113.99) belong to known CDN ranges and may cause false positives for legitimate traffic.

This example chains the Retrieve IP Blacklist tool with AI synthesis to generate actionable firewall config. The blacklist endpoint has strict rate limits (1 request per day on free tier, more on paid plans). Always validate IPs against your own traffic patterns — blocking CDN or cloud provider ranges can break third-party integrations.

When AbuseIPDB speeds up breach investigation at 5-person startups

A 5-person startup gets a spike in failed login attempts from three IP ranges. The security lead needs to decide in under 10 minutes whether to block the IPs or wait for more data. AbuseIPDB's Check IP Reputation and Check Block tools pull abuse history instantly—if the IPs show up in 20+ reports across multiple categories in the last 30 days, the call is easy. The API key setup takes 2 minutes. The limitation: if you're investigating a sophisticated attacker using fresh IPs, the database won't have history yet, so you'll need to cross-reference with your own logs. For teams under 10 people without a dedicated SOC, this MCP turns a 40-minute research task into a 3-minute lookup. If you're triaging more than 5 incidents a week, the time savings justify the seat.

How Retrieve IP Blacklist fits into nightly blocklist refresh jobs

A 12-person SaaS team runs a nightly cron job to update their edge firewall rules. They pull the top 10,000 most-reported IPs from AbuseIPDB using Retrieve IP Blacklist, then merge that list with their own deny-list from application logs. The MCP's Bulk Report tool also lets them push back IPs they've seen hammering their API, contributing to the community dataset. This works cleanly when your infrastructure is simple—one or two ingress points, a single firewall vendor. If you're running multi-cloud with regional WAFs, the integration gets messier because you'll need to translate the flat IP list into vendor-specific rule formats. For teams shipping customer-facing apps without a full-time security engineer, this MCP automates 80% of the blocklist hygiene work. Set it up once, forget it until the next incident.

When Get Abuse Reports helps support teams spot coordinated attacks

A 7-person customer support team at a fintech startup notices three user accounts created from different IPs all attempting the same fraudulent transaction flow. The support lead uses Get Abuse Reports to pull the full history for each IP, filtering by the last 90 days and sorting by category. Two of the IPs show up in phishing and fraud reports from other organizations; the third is clean. That pattern—mixed clean and dirty IPs—suggests a coordinated campaign using both compromised and fresh infrastructure. The MCP doesn't replace fraud detection tooling, but it gives the support team enough context to escalate to engineering in 5 minutes instead of 2 hours. If your support volume is under 50 tickets a day, this kind of ad-hoc lookup is faster than building a custom integration. Beyond that scale, you'll want the data piped into your ticketing system automatically.