Honeypot Threat Analysis — September 23, 2026
Critical threat level — massive coordinated attack activity across all honeypot services.
2026-09-23 Cybersecurity Analysis
Threat Overview
As of September 23, 2026, the honeypot running on a Raspberry Pi 5 in Spain has seen significant activity from 258 attackers over the past month. The analysis reveals that these attacks span multiple protocols and target various critical systems including SSH, HTTP, and others.
Geographic Analysis
The top three countries reporting are United States (627 IPs), Germany (199 IPs), and China (122 IPs). These regions represent a significant portion of the total attackers. The geographical distribution suggests that international actors and individuals from these locations engage with the honeypot for various malicious activities.
SSH Brute Force
SSH is one of the most commonly targeted protocols, with 498 connections in total. This high volume of brute force attempts indicates potential misconfiguration or vulnerabilities on the network edge. The attackers are using a variety of passwords ranging from simple to complex, suggesting that either weak password policies are enforced or users frequently change their SSH keys.
Post-Exploitation
The post-exploitation phase is often exploited to gain additional access and maintain persistence. Multiple attackers demonstrated this by attempting to change the root password, install unauthorized software, and create backdoors using various commands like lockr and cat. The use of shell utilities such as uname, cat /proc/cpuinfo, and chattr underscores their efforts at gaining deeper insights into the system’s configuration.
Web Scanning
HTTP scanning was conducted to identify vulnerabilities in web applications, with 46 requests coming from different IP addresses. This indicates that attackers are using a variety of tools or techniques to probe for known vulnerabilities on the honeypot. The paths accessed suggest an attempt at exploiting common flaws such as weak passwords, misconfigured settings, and unpatched software.
IDS & Scan Intel
The Honey-ai.dev honeypot has reported 8464 critical alerts from the Intrusion Detection System (IDS), indicating that it is effectively monitoring for malicious activities. The high number of alerts suggests a robust security posture but also indicates that attackers are using sophisticated methods to evade detection.
Malware
No malware was captured on this day, which aligns with the lack of reported cases in the AbuseIPDB and other sources. This could be attributed to effective sandboxing or the honeypot’s design focusing solely on monitoring for attacks rather than capturing malware samples.
Tarpit & MCP Trap
A total of 95 connections were trapped from 37 IPs, indicating that tarpitting is an ongoing threat. The lack of any reported breaches suggests that attackers are not exploiting vulnerabilities in the honeypot itself but instead finding ways to avoid detection through various means such as connecting from different networks.
Canarytokens
The presence of Canarytokens on a Raspberry Pi 5 honeypot is notable, with multiple instances captured over several days. This indicates that an attacker may be attempting to exploit a known vulnerability or misconfiguration related to the use of these tokens in web applications.
MCP Trap & Portscans
No malicious activity was detected during portscans by any tool, which suggests that the honeypot is effectively acting as a decoy for attackers. This indicates that it has successfully lured attackers into its environment without causing any real harm or damage to the network infrastructure.
AI Defense
The honeypot’s AI defense mechanisms have been effective in blocking 11 leaks and 0 injection attacks, suggesting that the security measures in place are sufficiently robust against common attack vectors. However, the absence of reported malware captures indicates that attackers may be using alternative methods to compromise systems or they might not be targeting this environment.
Community Defense
The low number of attacks and the successful detection of some threats suggest a high level of community defense and awareness. The honeypot’s effectiveness is also evident in the fact that it has successfully identified potential vulnerabilities without being compromised itself, demonstrating its utility as an educational tool for security professionals.
Conclusion
In summary, the honeypot on Raspberry Pi 5 in Spain remains effective at identifying threats to network security. Its ability to monitor multiple protocols and provide valuable data through detailed logs is highlighted by the high volume of alerts from IDS systems. The consistent use of tarpitting and MCP traps further underscores its effectiveness in evading detection. While not all attacks were thwarted, the overall success rate indicates a robust defense mechanism that could be used as an educational tool for security professionals.
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.