Monitoring Suspicious User-Agent Strings in Web Logs

Overview

User-Agent strings are a critical first indicator of automated or potentially malicious activity against web infrastructure. Monitoring for unusual, malformed, or known-malicious User-Agents in honeypot or web server logs can reveal reconnaissance attempts, vulnerability scanning, or botnet activity. This report highlights the value of analyzing these strings for early threat detection and understanding attacker methodologies.

Technical Analysis

The source material notes general “curiosities” in User-Agent strings observed in honeypot logs. While specific examples are not provided, suspicious User-Agents typically include:
* Non-standard, malformed, or excessively long strings that do not conform to legitimate browser or client patterns.
* Strings explicitly associated with known web vulnerability scanners (e.g., sqlmap, Nmap Scripting Engine, Nikto, Acunetix).
* User-Agents indicating specific botnet or malware activity, often seen in high-volume, distributed scanning.
* Rapid changes in User-Agent strings from a single source IP, attempting to evade detection or mimic different clients.
* Empty or default User-Agents from scripting libraries (e.g., Python-requests, Go-http-client) when not expected for legitimate traffic.
These strings are frequently used to probe for web application vulnerabilities, identify server configurations, or enumerate directories and files.

Detection

• Log Sources: Web server access logs (Apache, Nginx, IIS), Web Application Firewall (WAF) logs, proxy logs, honeypot logs.
• Behavioral Indicators:
  • Frequent requests from a single IP address exhibiting varying or unusual User-Agent strings.
  • User-Agent strings containing keywords associated with known scanning tools (e.g., sqlmap, nmap, nikto, dirbuster, gobuster).
  • User-Agent strings that are empty, excessively short, or malformed (e.g., containing non-printable characters).
  • Requests from User-Agent strings that do not correspond to expected client types for the service (e.g., a mobile browser UA accessing an API endpoint typically used by server-side applications).
  • Spikes in requests from User-Agent strings that have a low historical frequency or are entirely new.
• Hunt Ideas: Query web access logs for User-Agent strings that appear infrequently, contain suspicious keywords, or show high cardinality from a single source IP. Analyze traffic patterns for sequential requests with different User-Agents from the same source.

Sigma Detection Rules

Web Scanner User-Agent Detection

title: Web Scanner User-Agent Detection
id: 8b1d4e0a-7c2f-4a3b-9e1d-5f6c7a8b9d0e
status: experimental
description: Detects common User-Agent strings associated with web vulnerability scanners and reconnaissance tools.
logsource:
  category: webserver
detection:
  selection:
    User-Agent|contains:
      - 'sqlmap'
      - 'Nmap Scripting Engine'
      - 'Nikto'
      - 'DirBuster'
      - 'GoBuster'
      - 'masscan'
      - 'Acunetix'
      - 'Netsparker'
      - 'Wget'
      - 'curl'
  condition: selection
level: high

Empty or Malformed User-Agent String

title: Empty or Malformed User-Agent String
id: c3f5d7e9-1a2b-3c4d-5e6f-7a8b9c0d1e2f
status: experimental
description: Detects requests with empty, null, or unusually short User-Agent strings, often indicative of automated scripts or basic scanning tools.
logsource:
  category: webserver
detection:
  selection_empty:
    User-Agent:
      - ''
      - '-'
      - 'null'
      - 'None'
  selection_short:
    User-Agent|length|less_than: 5
  condition: selection_empty or selection_short
level: medium

Mitigations

1. Implement robust Web Application Firewalls (WAFs) to filter requests based on suspicious User-Agent patterns, known scanner signatures, and other request attributes.
2. Regularly review web server, WAF, and proxy logs for unusual activity, specifically focusing on User-Agent strings and their associated source IPs and request paths.
3. Maintain up-to-date threat intelligence feeds to identify User-Agent strings associated with known malicious tools, botnets, or campaigns.
4. Implement rate limiting and IP blocking for sources exhibiting high volumes of suspicious requests or known malicious User-Agents.
5. Ensure web applications are regularly patched, configured securely, and follow secure coding practices to withstand scanning and exploitation attempts.

References

• https://isc.sans.edu/diary/rss/33394

Indicators of Compromise

No public IOCs available at time of writing.

MITRE ATT&CK

• T1595.002 — Vulnerability Scanning