AbuseIPDB - Data Source

Website	https://www.abuseipdb.com/
Category	Threat Intelligence

AbuseIPDB is a community-driven database where system administrators and security professionals report IP addresses engaged in abusive behavior. Each report includes an abuse category (SSH brute force, web attacks, spam, port scanning, etc.) and a description. AbuseIPDB calculates a confidence score from 0 to 100 for each IP based on report volume, diversity of reporters, and recency. We use AbuseIPDB data on robtex.com and rbls.org to show community-reported abuse activity for IP addresses.

Source:AbuseIPDB

What is AbuseIPDB?

AbuseIPDB functions as a crowdsourced threat intelligence platform. Its model relies on a global network of contributors who report abusive IPs they encounter on their own infrastructure. When a server administrator detects brute force attempts, web application attacks, or other abuse, they can submit a report to AbuseIPDB with the offending IP, the category of abuse, and an optional description.

The platform tracks abuse across numerous categories:

SSH brute force - Automated password guessing against SSH services
Web application attacks - SQL injection, XSS, path traversal, and exploitation attempts
Port scanning - Reconnaissance scanning across IP ranges
Spam - Unsolicited email and comment spam
DDoS attacks - Participation in distributed denial-of-service campaigns
Fraud - Phishing, credential stuffing, and fraudulent transactions
Bad web bot - Aggressive crawling, scraping in violation of robots.txt
IoT exploitation - Attacks targeting IoT devices and protocols

The confidence score reflects how likely an IP is to be genuinely abusive. It accounts for the number of distinct reporters (multiple independent reporters increase confidence), the recency of reports (recent reports weigh more heavily), and the total report count. An IP with a confidence score of 90+ has been reported by many different sources recently, making it very likely to be actively malicious.

AbuseIPDB provides both a free API tier and a web interface for lookups. The platform has grown to include millions of reported IPs, with thousands of new reports submitted daily by its community of contributors.

How We Use This Data

On IP lookup and reputation pages across robtex.com and rbls.org, we display AbuseIPDB data including the confidence score, total report count, and the most common abuse categories reported for that IP. This community perspective complements automated detection systems by adding human observations from real-world abuse encounters.

The confidence score provides a quick assessment: scores below 25 suggest isolated or possibly false reports, scores of 25-75 indicate moderate abuse activity worth monitoring, and scores above 75 represent IPs with extensive community-reported abuse history. The abuse categories help identify the type of threat, which is useful for targeted defensive measures.

We import the top reported IPs from AbuseIPDB into our database, focusing on those with the highest confidence scores and report volumes to surface the most actionable intelligence.

FAQ

How reliable is a community-reported database compared to automated detection?

Community reporting and automated detection complement each other. Automated systems detect known patterns efficiently but can miss novel attacks. Human reporters catch abuse that automated systems overlook, including sophisticated attacks and context-specific abuse. The confidence scoring mechanism in AbuseIPDB helps filter out false reports by requiring multiple independent reporters for high confidence scores. An IP reported by one person might be a false positive, but one reported by 50 independent administrators is almost certainly abusive.

Can someone maliciously report a legitimate IP on AbuseIPDB?

AbuseIPDB has mechanisms to mitigate false reporting, including rate limits, account reputation, and detection of coordinated false reports. However, no community platform is immune to abuse. The confidence score helps here: a legitimate IP that receives a few false reports will have a low confidence score because it lacks the volume and diversity of reporters that genuinely abusive IPs accumulate. Always consider the confidence score and report count together rather than treating any single report as definitive.

Why does an IP show a high AbuseIPDB score but no listings on other blocklists?

Different systems have different detection thresholds and update cycles. AbuseIPDB relies on active community reporting, which can flag new threats quickly. Automated blocklists may not have processed the same activity yet, or their detection criteria may differ. An IP engaged in targeted attacks against a small number of servers might generate many AbuseIPDB reports from those affected administrators while flying under the radar of broader automated detection systems.