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CVE Vulnerability Database
Vulnerability Database/CVE-2025-22234

CVE-2025-22234: Timing Attack Information Disclosure Flaw

CVE-2025-22234 is a timing attack information disclosure vulnerability in DaoAuthenticationProvider that allows attackers to infer valid usernames through response-time analysis. This article covers technical details, impact, and mitigation.

Updated: March 12, 2026

CVE-2025-22234 Overview

CVE-2025-22234 is a timing attack vulnerability in Spring Security's DaoAuthenticationProvider component. The fix applied for a previous vulnerability (CVE-2025-22228) inadvertently broke the timing attack mitigation that was implemented to prevent username enumeration. This regression allows attackers to infer valid usernames or other authentication behavior by analyzing response-time differences during authentication attempts under certain configurations.

Critical Impact

Attackers can enumerate valid usernames through response-time analysis, potentially enabling targeted credential attacks against known valid accounts.

Affected Products

  • Spring Security (affected versions not specified in advisory)

Discovery Timeline

  • 2026-01-22 - CVE CVE-2025-22234 published to NVD
  • 2026-01-26 - Last updated in NVD database

Technical Details for CVE-2025-22234

Vulnerability Analysis

This vulnerability is classified under CWE-208 (Observable Timing Discrepancy), a side-channel attack weakness. The flaw stems from a regression introduced when patching CVE-2025-22228, which inadvertently disabled the constant-time comparison mechanisms in the DaoAuthenticationProvider class.

In properly secured authentication systems, the time taken to process authentication requests should remain constant regardless of whether a username exists in the system. This prevents attackers from determining valid usernames by measuring response times. When this mitigation is broken, authentication requests for valid usernames may process differently than requests for invalid usernames, creating a measurable timing discrepancy.

The network-based attack vector with low complexity makes this vulnerability accessible to remote attackers without requiring authentication or user interaction, though the impact is limited to information disclosure of valid usernames.

Root Cause

The root cause is a regression bug introduced during the remediation of CVE-2025-22228. The patch for the previous vulnerability inadvertently removed or bypassed the constant-time password comparison logic in DaoAuthenticationProvider, which was designed to ensure uniform response times regardless of user validity. This created an observable timing discrepancy that attackers can exploit to enumerate valid accounts.

Attack Vector

The attack is network-based and requires no authentication or special privileges. An attacker can exploit this vulnerability by:

  1. Sending multiple authentication requests with different usernames to the target application
  2. Precisely measuring the response times for each request
  3. Analyzing the timing differences to identify which usernames produce different processing times
  4. Compiling a list of valid usernames based on the timing analysis
  5. Using the enumerated usernames for targeted password attacks or credential stuffing

This timing attack does not directly compromise credentials but provides valuable reconnaissance data that significantly improves the efficiency of subsequent attacks.

Detection Methods for CVE-2025-22234

Indicators of Compromise

  • High volume of failed authentication attempts from single IP addresses with varying usernames
  • Sequential or patterned authentication requests suggesting automated enumeration tools
  • Unusual patterns of authentication timing analysis in network traffic logs
  • Evidence of credential stuffing attacks following reconnaissance activity

Detection Strategies

  • Monitor authentication logs for patterns consistent with username enumeration (rapid sequential failures with different usernames)
  • Implement rate limiting on authentication endpoints to throttle potential timing attacks
  • Deploy web application firewalls (WAF) with rules to detect enumeration behavior
  • Analyze authentication endpoint response times for anomalous patterns that may indicate exploitation

Monitoring Recommendations

  • Enable detailed logging on Spring Security authentication components
  • Monitor for unusual authentication traffic patterns and high failure rates
  • Correlate authentication failures with subsequent targeted attacks on discovered usernames
  • Set up alerts for authentication attempts that exhibit timing attack characteristics

How to Mitigate CVE-2025-22234

Immediate Actions Required

  • Review the Spring Security CVE-2025-22234 advisory for affected versions and patches
  • Update Spring Security to the latest patched version as specified in the vendor advisory
  • Implement additional authentication rate limiting as a defense-in-depth measure
  • Monitor authentication endpoints for signs of ongoing exploitation attempts

Patch Information

VMware/Spring has released security guidance for this vulnerability. Refer to the Spring Security CVE-2025-22234 Advisory for specific patch versions and upgrade instructions. Organizations should prioritize updating their Spring Security dependencies to the latest secure version.

Workarounds

  • Implement aggressive rate limiting on authentication endpoints to make timing analysis impractical
  • Add artificial random delays to authentication responses to obscure timing differences
  • Deploy a WAF configured to detect and block username enumeration patterns
  • Consider implementing CAPTCHA or other challenge-response mechanisms after failed authentication attempts

The recommended approach for implementing rate limiting on Spring Security authentication endpoints would involve configuring appropriate filters or leveraging Spring Security's built-in rate limiting capabilities. Consult the Spring Security documentation for implementation details specific to your application configuration.

Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.

  • Vulnerability Details
  • TypeInformation Disclosure

  • Vendor/TechN/A

  • SeverityMEDIUM

  • CVSS Score5.3

  • EPSS Probability0.01%

  • Known ExploitedNo
  • CVSS Vector
  • CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:N
  • Impact Assessment
  • ConfidentialityLow
  • IntegrityNone
  • AvailabilityNone
  • CWE References
  • CWE-208
  • Technical References
  • Spring Security CVE-2025-22234
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