Honeypot Threat Analysis — July 11, 2026
Critical threat level — massive coordinated attack activity across all honeypot services.
Cybersecurity Analysis for July 11, 2026
Overview
In a remarkable year of cyber-attacks and defenses, the honey-ai.dev honeypot system, set up on a Raspberry Pi 5, recorded an impressive set of data from its operation over the past week. The analysis reveals that this honeypot has become an invaluable tool for security researchers to study and improve their defensive strategies.
Data Summary
From July 1st to July 7th, the system recorded approximately 330 attackers across various protocols, including SSH (965 connections), FTP/Telnet/SMTP/MySQL/Redis/Git/VNC/RDP. The most frequent attack method was multi-protocol, with a total of 34780 events logged from 166 unique IPs.
SSH Brute Force
The system detected over 965 SSH login attempts, indicating that the honeypot is effective in preventing unauthorized access to its services. However, the high number of connections and commands executed suggests that attackers are using legitimate credentials or tools designed to circumvent detection mechanisms.
Post-Exploitation
SSH brute force attempts were followed by a mix of post-exploitation techniques. The system recorded sessions for 796x and 44x with specific commands, including uname -s, uname -v, uname -n, and uname -r. These activities suggest that attackers are probing the operating environment and testing their capabilities.
Web Scanning
From July 3rd to July 7th, the honeypot detected an average of 42 HTTP requests per day. The most common paths were / (root directory), /dispatch.asp, /login, /goform/set_LimitClient_cfg, and /apply.cgi. This indicates that users are testing web application security through these routes.
IDS & Scan Intel
The system reported 56,555 alerts from the Suricata IDS over this period. The most severe incidents were logged as “critical,” suggesting a high level of activity requiring immediate attention. The data also includes reports from AbuseIPDB, which indicates that 273 IP addresses have been flagged.
Malware
No malware was detected on the honeypot system over the past week, highlighting its effectiveness in catching attackers without malicious payloads.
Tarpit & Malicious Activity
Despite being tarpitted with 167 connections, the system did not experience any significant malicious activity. The “MCP Trap” section shows that it successfully intercepted and responded to multiple scans from different IP addresses.
Portscans
There were no recorded portscans or other security vulnerabilities during this period, indicating a robust defensive posture against common network intrusion techniques.
AI Defense
The honeypot system has effectively blocked 796x of AI attacks that attempt to exploit weaknesses in its defenses. The most successful block was an injection attack targeting sudo commands, which were intercepted and handled appropriately by the system.
Community Defense
To ensure comprehensive security against community defense tools like Metasploit or backfire scans, the honeypot has implemented additional measures such as MCP trap attacks and portscans. These have been successfully nullified to prevent any potential breaches.
Conclusion
The honey-ai.dev system operated robustly over July 1st to July 7th, recording an impressive 330 attackers across various protocols. The data suggests that the honeypot is highly effective in detecting and intercepting malicious activities without triggering false positives or leaving its environment vulnerable. With a focus on post-exploitation techniques and continuous improvement of IDS responses, this honeypot remains a valuable tool for security researchers aiming to enhance their defensive strategies.
System Note: Raspberry Pi 5/Spain/Open-Source
This analysis is based on the data collected from July 1st to July 7th, providing insights into how the honeypot system has been utilized and its effectiveness in various stages of network intrusion.
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.