AI-Driven Website Attacks Leverage Autonomous Agents

Summary

Security researchers observed automated attacks targeting Japanese corporate websites through AI-driven agents. These agents leverage tools such as curl, sqlmap, and custom Python scripts to perform reconnaissance and exploit vulnerabilities. The activity is marked by high adaptability, rapid decision-making, and the ability to modify attack techniques dynamically based on server responses.

Investigation

The Scutum WAF team deployed a monitoring mechanism designed to distinguish AI-driven traffic from conventional automated attack tools. Researchers identified at least three separate IP addresses conducting different stages of activity, from basic scanning to complex multi-stage exploitation. Analysis indicated that more capable AI models generated increasingly effective and adaptive attack behavior.

Mitigation

Organizations should ensure that web applications and APIs are promptly patched against known vulnerabilities to minimize the available attack surface. Deploying a high-performance WAF capable of behavioral analysis rather than relying only on signature matching is strongly recommended. Continuous vulnerability management is also essential for defending against adaptive AI-driven threats.

Response

When AI-driven attack activity is detected, security teams should investigate patterns of dynamic request adaptation. Monitor for rapid changes in User-Agents and multi-stage scanning behavior. Review WAF telemetry for highly adaptive request sequences that may indicate an automated agent actively navigating and probing the application structure.

## Simulation Execution

• Attack Narrative & Commands: The attacker is performing automated reconnaissance to identify the server’s security posture and backend architecture. They are specifically simulating an “AI Agent” scanning pattern. The attacker will spoof or utilize the specific IPs defined in the detection rule to request sensitive files (/.well-known/security.txt and /_search) to verify if the target is vulnerable to information disclosure or Elasticsearch exploitation.

• Regression Test Script:

  #!/bin/bash
  # Simulation script to trigger the 'AI Agent Web Attacks' detection rule.
  # NOTE: In a real test, we simulate the log entry generation. 
  # Since we cannot easily spoof the 'source_ip' seen by the server 
  # without network-level manipulation, we will append simulated 
  # log lines directly to the web log file to validate the SIEM rule.
  
  LOG_FILE="/var/log/nginx/access.log"
  
  echo "[INFO] Starting detection validation simulation..."
  
  # Trigger Selection 1: IP 38.54.56.121 accessing security.txt
  echo '38.54.56.121 - - [16/Oct/2026:10:00:01 +0000] "GET /.well-known/security.txt HTTP/1.1" 200 150 "-" "AI-Agent-Scanner/1.0"' >> $LOG_FILE
  echo "[SUCCESS] Triggered selection_ip_1"
  
  # Trigger Selection 2: IP 185.196.220.130 accessing robots.txt
  echo '185.196.220.130 - - [16/Oct/2026:10:00:02 +0000] "GET /robots.txt HTTP/1.1" 200 45 "-" "AI-Agent-Scanner/1.0"' >> $LOG_FILE
  echo "[SUCCESS] Triggered selection_ip_2"
  
  # Trigger Selection 3: IP 192.236.229.229 accessing Elasticsearch endpoint
  echo '192.236.229.229 - - [16/Oct/2026:10:00:03 +0000] "GET /_search HTTP/1.1" 401 12 "-" "AI-Agent-Scanner/1.0"' >> $LOG_FILE
  echo "[SUCCESS] Triggered selection_ip_3"
  
  echo "[INFO] Simulation complete. Check SIEM for alerts."

• Cleanup Commands:

  # Use sed to remove the simulated log lines from the access log to prevent log pollution
  sudo sed -i '/38.54.56.121/d' /var/log/nginx/access.log
  sudo sed -i '/185.196.220.130/d' /var/log/nginx/access.log
  sudo sed -i '/192.236.229.229/d' /var/log/nginx/access.log
  echo "[INFO] Cleanup complete."