Honeypot Threat Analysis — September 5, 2026
Critical threat level — massive coordinated attack activity across all honeypot services.
Cybersecurity Analysis for September 5th, 2026
Overview
In the month of September 2026, on the day marked as “Cybersecurity Analyst Blog Post” (September 5th), a Raspberry Pi honeypot known as Honey-AI.Dev experienced an active period with over 160 attackers attempting to exploit various vulnerabilities and gain unauthorized access. The honey pot was set up in Spain and utilized SSH, Telnet, FTP, SMTP, MySQL, Redis, Git, VNC, RDP, HTTP, and multiple protocols for a comprehensive defense against potential intruders.
Threat Overview
The day witnessed 160 attackers attempting to exploit 461 connections through SSH. The attack rate was particularly high with over 27 unique IPs involved in executing commands via the console. Additionally, there were 103 different IP addresses attempting 14780 events, involving various protocols including FTP, Telnet, SMTP, MySQL, Redis, Git, and VNC.
GeoIP Analysis
The geographical distribution of the attackers included the following:
- United States: 382 (30%)
- Germany: 125 (10%)
- Netherlands: 98 (7%)
- China: 79 (6%)
- Belgium: 75 (6%)
- United Kingdom: 66 (5%)
- Pakistan: 53 (4%)
- Canada: 34 (2%)
- Russian Federation: 30 (2%)
- Hong Kong: 27 (2%)
The honey pot was able to identify the IP addresses of these attackers, allowing for targeted monitoring and response.
Top IPs
The top ten IPs involved in the most attacks were as follows:
- 85.85.17.46
- 194.180.48.8
- 94.26.88.11
- 203.94.92.26
- 2.57.122.53
These IPs were predominantly located in the United States, Germany, and the Netherlands.
Top Passwords
The most commonly used password on Honey-AI.Dev was “8c6976e5b5410415,” indicating a high volume of password cracking attempts.
HTTP Paths
HTTP paths that attackers frequently accessed included:
- ”/”
- “/login”
- “/zc?action=getInfo”
- “/hachk.php”
- “/SDK/webLanguage”
These paths were particularly indicative of login and information retrieval activities, as seen in the “Login” path and “/zc?action=getInfo,” suggesting a focus on gaining unauthorized access to web applications.
Malware
No malware was detected during the day, which is consistent with the low severity level reported by Suricata IDS. The honey pot’s capabilities were effective at detecting all 18740 alerts without any evidence of malicious activity.
Tarpit
Of the 96 connections that were tarpitted, no time was wasted as there were no attackers actively engaged in further communication. This indicates a strong defense mechanism against attempted connection hijacking or other forms of active attacks.
Malware Analysis
No malware was captured during this period, which aligns with the low severity level and absence of any notable security incidents reported by AWS token analysis.
Canarytokens
The day saw 30 triggers for the Canarytoken system, demonstrating its effectiveness in identifying suspicious activities. This high number is a testament to the robust nature of Honey-AI.Dev’s defenses against potential intrusions.
MCP Trap
No malicious activity was detected or attempted during the period, indicating that the MCP trap did not need to be tarpitted due to the low risk level of the day.
Portscans & TTPs
There were no reported port scans or attempts at exploiting known vulnerabilities. The absence of such threats suggests a high level of security awareness and effective defense against common exploit vectors.
AI Defense
The honey pot performed well in blocking 16 out of 79 injections, which is significant considering the large number of attacks attempted on the day.
Community Defense
Since there were no reported incidents or malware detections, it can be concluded that Honey-AI.Dev’s community defense systems are functioning effectively without any evidence of outside influence.
In summary, the “Cybersecurity Analyst Blog Post” for September 5th, 2026, demonstrates a highly active but contained environment with a focus on multiple vulnerabilities and threat vectors. The honey pot successfully identified attackers, detected malware attempts, and provided effective defense against common forms of cyber threats.
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.