Honeypot Threat Analysis — October 13, 2026
Critical threat level — massive coordinated attack activity across all honeypot services.
Cybersecurity Analysis for 2026-10-13
Overview:
In the early morning hours of October 13, 2026, our honeypot in Spain experienced a significant number of cyber attacks. A total of 5311 unique IP addresses were involved across various attack vectors including SSH, HTTP, and multiple protocols such as FTP, Telnet, SMTP, MySQL, Redis, Git, VNC, RDP, and more.
Threat Analysis
Top Attackers:
The most active attackers on this date were from the following IPs: 94.72.122.111, 94.243.8.210, 217.160.21.137, 94.243.13.162, and 179.43.153.10.
Top Passwords:
The most commonly used passwords were: “8c6976e5b5410415”, “@@@asas111!!!”, “1ZQA2xws”, “1zqa2xws5btg6nyh”, and “1zqa2xwsw5btg6nyh”.
HTTP Paths:
The most frequently targeted paths included: ”/”, “/SDK/webLanguage”, “/favicon.ico”, “/hachk.php”, and “/login”.
Geographic Analysis
Top Geographical Distribution:
- United States: 1,607 (30%)
- China: 426 (8%)
- Germany: 384 (7%)
- Belgium: 329 (6%)
- United Kingdom: 285 (5%)
- Pakistan: 255 (4%)
- Netherlands: 209 (3%)
- Hong Kong: 151 (2%)
- Korea, Republic of: 147 (2%)
Geographic Distribution
The honeypot was particularly active in the United States with a significant number of attackers from this region. The top geographies also included Germany and Belgium, indicating that these countries were targeted by multiple IP addresses.
Tarpit:
Unfortunately, no tarpits were observed on this date, leading to wasted hours without any potential threats detected.
Malware Activity
No malware was captured during the day of October 13th, 2026.
Threat Overview
The honeypot faced a total of 5311 unique IP addresses. The majority of these attacks were directed towards SSH (291 connections), followed by HTTP and various protocols like FTP, Telnet, SMTP, MySQL, Redis, Git, VNC, RDP, and more.
AbuseIPDB
The honeypot was reported to have 166 unique IP addresses that had been abused. This indicates a higher risk of unauthorized access or malicious activities coming from these IPs.
Malware
Threats Detected:
Despite the lack of malware on this day, it is essential to be vigilant against all types of threats. The honeypot could still detect other types of attacks such as SQL injection, cross-site scripting, and more sophisticated phishing attempts that may not directly result in malware but can compromise systems.
AI Defense
Threats Blocked:
The AI defense system on the honeypot successfully blocked 41 canary tokens. Additionally, an additional 11 leaked files were detected and prevented from being accessed by unauthorized users.
Community Defense
No community defense measures were taken or triggered during this period.
Post-Exploitation
Threats Detected:
The honeypot experienced a significant number of post-exploitation activities. For example, attackers used tarmacs to redirect traffic, exploited vulnerabilities in the system, and conducted further reconnaissance on the network. These types of attacks are critical for understanding the capabilities of an attacker’s toolkit.
Portscans & Backfire Scans
Threats Detected:
No port scans were detected during this period, which is expected due to the nature of a honeypot setup. Additionally, backfire scans targeting specific targets were observed but no open ports or vulnerabilities were exploited.
Tarpit
The lack of tarpits suggests that attackers are either more selective in their connections or have been redirected away from the honeypot without causing significant damage.
Conclusion
Despite being a typical day for a honeypot, with relatively low malware activity and no security breaches reported, our monitoring system highlighted several areas where improvements can be made. These include increased focus on tarpit protection, better detection of post-exploitation techniques, and continuous testing to ensure the effectiveness of AI-based defenses.
Notes:
- The honeypot is currently running on Raspberry Pi 5 under the open-source community, with no hardware restrictions or limitations.
- This dataset covers a significant period and provides valuable insights into cyber threats targeting systems that are designed as honeypots.
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.