Honeypot Threat Analysis — September 13, 2026
Critical threat level — massive coordinated attack activity across all honeypot services.
2026-09-13 Honey-AI.Dev Blog
Geographic Distribution and User Behavior
On September 13, 2026, the honeypot at Honey-AI.Dev experienced a surge in activities from multiple locations globally. The primary geographical distribution shows that most attacks originated from the United States (796 users), Germany (233 users), Belgium (163 users), Netherlands (157 users), and China (148 users). In contrast, there were significantly fewer incidents from other regions like Pakistan (131 users) and Hong Kong (59 users).
The user behavior indicates a mix of common attack methods such as SSH brute force attempts, HTTP requests for unauthorized access, and the use of popular shell commands. This data suggests that while China continues to be a significant hub for cyber attacks, others are following suit with increasing activity.
Top IPs and User Actions
Among the top 20 IPs involved in this period, “182.127.179.79” was one of the most active, likely indicating a persistent threat actor or botnet operation targeting this IP extensively. Other notable IPs include “103.213.112.192,” which could indicate an automated scanning tool or a compromised machine being used to initiate attacks.
The top passwords used for authentication were primarily common and easily guessed, such as “8c6976e5b5410415.” This suggests that many users are using simple passwords without proper security practices. The presence of these common passwords indicates areas where stronger password policies should be enforced to mitigate against brute force attacks.
Threat Overview
The honeypot received a significant number of SSH connections (35) and logins (31), along with 4 commands executed on the system. This high level of interaction suggests that there are multiple actors actively probing for vulnerabilities within the network, indicating an evolving threat landscape. The total number of unique IPs involved in these activities stands at around 200.
Post-Exploitation and Malware
Given the critical severity of the alerts reported by the IDS (7322), it’s clear that multiple attackers have been attempting to exploit vulnerabilities. However, no malware has been captured or deployed during this period, suggesting a successful attempt to maintain stealth while engaging in reconnaissance.
Tarpit and Backfire Scans
Despite the tarpitting of 156 connections from 74 IPs, there were no reports of backfire scans targeting Honey-AI.Dev. This suggests that the honeypot is effectively catching and mitigating these types of attacks, thereby minimizing potential damage to its environment.
Portscans and AI Defense
While no malware was captured, a few attempts at portscans (46/21) were noted. The lack of successful backfire scans indicates effective monitoring and blocking mechanisms in place. Additionally, the low number of injections blocked by the AI defense system suggests that such attempts were either unsuccessful or easily countered.
Community Defense
The honeypot also saw a few instances of tools being used (3 IPs), which are commonly seen as part of an attacker’s toolkit but often indicate potential for defensive action against those using these tools. There was no reported leakage of sensitive information, suggesting that the community defense system is effective in blocking such attempts.
Summary
The 2026-09-13 incident at Honey-AI.Dev highlights a complex and dynamic threat landscape where multiple actors are actively probing for vulnerabilities within an open environment. The high number of critical alerts by IDS points to ongoing reconnaissance efforts, while the low level of malware suggests that these attacks may be aimed at gaining footholds without causing significant disruption.
By implementing robust security measures such as strong password policies, effective tarpitting and monitoring systems, and continuous improvement in AI-based defenses, organizations can effectively manage this threat landscape. The honeypot serves as a valuable tool for both attackers and defenders to understand the evolving threats and develop more resilient strategies against them.
This analysis was generated by qwen2.5:1.5b running locally on the Raspberry Pi 5 honeypot lab. All data comes from real attacks captured in the last 24 hours by HoneyAI. View the raw data report for complete metrics.