Honeypot Threat Analysis — August 19, 2026

Cybersecurity Analysis for 2026-08-19 at Honey-AI.Dev

Overview

On August 19, 2026, the honeypot system “Honey-AI.Dev” was monitored in Spain with a total of 146 unique IP addresses. The security analysts observed a significant amount of suspicious activity across various protocols and services.

Top IPs

The top ten IPs reported over the period were as follows:

Top IP: 182.188.24.243 - This IP was involved in multiple types of attacks, including SSH brute force, HTTP requests, and more.
Second Top IP: 139.135.44.115 - This IP was also heavily involved in different attack vectors such as HTTP requests, FTP, Telnet, and more.

Top Passwords

The top three passwords used were:

Top Password: “8c6976e5b5410415”
**Second Top Password: “4813494d137e1631”`

These passwords suggest that the attackers are using commonly guessed or easily found passwords.

HTTP Paths

The top 20 HTTP paths accessed were:

”/”
“/login”
“/SDK/webLanguage”
“/solr/admin/cores?action=STATUSGALAH_PATHS_PHwt=json”
“/solr/admin/info/system”

These paths indicate that the attackers are targeting specific web services or applications.

Threat Overview

The total number of threats observed was 9451 alerts, with a severity level marked as “critical”. This suggests a high volume and urgency of security incidents on the honeypot system. The attackers appear to be highly motivated, with multiple IPs involved in various types of attacks.

Geographic Analysis

Global Distribution

The global distribution of threats was notable:

United States: 456 (36%)
China: 85 (6%)
Germany: 67 (5%)
United Kingdom: 57 (4%)
Belgium: 56 (4%)
Pakistan: 53 (4%)
Netherlands: 51 (4%)
Vietnam: 34 (2%)
Sweden: 31 (2%)

This distribution suggests that the honeypot system is under attack from a diverse range of locations, including major economies and developing countries.

SSH Brute Force

The most significant threat was a continuous SSH brute force attempt. The attackers were attempting to gain unauthorized access through common passwords or precomputed dictionaries. This is an ongoing effort by the attackers to compromise the honeypot system continuously.

Post-Exploitation

Post-exploitation activities were observed, including:

Shell Escapes: Attempted to execute shell commands and bypass traditional authentication methods.
RDP Scans: The attacks involved RDP scans, which are common in this type of environment where security measures might be less stringent.

Web Scanning

The top 20 HTTP paths accessed were indicative of advanced scanning techniques, suggesting that the attackers had access to multiple services and applications. The high volume of requests indicates a level of sophistication in their attack strategy.

IDS & Scan Intel

Total Suricata Alerts: 9451 alerts.
Unique IPs: 761 unique IP addresses reported over the period.

The IDS system was active, detecting various types of traffic and activity, including HTTP requests, SSH attempts, and more. This indicates that the honeypot is effectively monitoring its environment for potential threats.

Malware

There were no instances of malware captured during this period on the honeypot system. This suggests that the security measures in place are effective at detecting and preventing malware from being introduced into the environment.

Tarpit

The tarpit attack was observed, where a large number of connections (82) were trapped but not exploited fully. The attackers did not gain control over any of these connections, which is a positive indicator for the honeypot system’s effectiveness in detecting and preventing such attacks.

Malware

Canarytokens

Two Canarytokens were detected, indicating a high level of activity from malicious actors attempting to bypass security measures. The tokens appear to be related to AWS authentication credentials, which suggests a continuous attempt by attackers to gain access through known vulnerabilities in the system.

MCP Trap

MCP traps occurred on 6 unique IP addresses, with no significant activities reported. This indicates that while these IPs were targeted, they did not result in any notable outcomes or incidents.

Portscans

Portscans were observed but did not yield any significant results during this period. This suggests that the honeypot system is effectively monitoring and detecting port scans without allowing them to succeed.

AI Defense

The defense mechanisms against injection attacks were ineffective, with no blocks reported. This indicates a need for further improvement in preventing such threats from being introduced into the environment.

Community Defense

There was no detection of any community-based threat actors or malicious activities during this period on the honeypot system. This suggests that while the system is effective at monitoring and detecting threats, it does not yet have robust defenses against known attack vectors.

Conclusion

The security posture of “Honey-AI.Dev” demonstrated resilience in dealing with a significant volume of attacks over the past week. However, there are several areas for improvement:

Enhanced Malware Detection: The lack of malware detection is a concern that needs to be addressed.
Improved Tarpit Response: While the tarpit attack was not exploited fully, it indicates ongoing security lapses that need to be mitigated.
Threat Awareness: The high volume of threats suggests that continuous threat intelligence and monitoring are essential for staying ahead of evolving attacks.

The system’s effectiveness in detecting and preventing known vulnerabilities is commendable. However, the lack of defense against new or advanced attack vectors indicates a need for further investment in security measures and training personnel to better understand and respond to emerging 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.