CVE-2025-70949 Overview
An observable timing discrepancy vulnerability has been identified in @perfood/couch-auth version 0.26.0. This timing side-channel attack allows attackers to extract sensitive information by measuring the time taken by authentication operations. By analyzing response time variations, attackers can infer details about valid usernames, password lengths, or other authentication-related data, potentially leading to credential enumeration or authentication bypass scenarios.
Critical Impact
Attackers can leverage timing discrepancies in authentication routines to enumerate valid users and extract sensitive information, potentially facilitating credential stuffing attacks or targeted brute-force campaigns.
Affected Products
- @perfood/couch-auth v0.26.0
Discovery Timeline
- 2026-03-05 - CVE CVE-2025-70949 published to NVD
- 2026-03-05 - Last updated in NVD database
Technical Details for CVE-2025-70949
Vulnerability Analysis
This vulnerability falls into the category of Side Channel Attack, specifically a timing attack against authentication mechanisms. The @perfood/couch-auth library, which provides authentication functionality for CouchDB-based applications, exhibits observable timing differences in its authentication validation routines.
When processing authentication requests, the library performs string comparison operations that do not execute in constant time. This non-constant time comparison means that the time required to reject an authentication attempt varies based on how many characters match between the submitted credentials and the actual stored credentials. An attacker can statistically analyze these timing variations across multiple requests to incrementally determine valid credential values.
The impact of this vulnerability extends to information disclosure, where attackers can determine whether specific usernames exist in the system, infer password lengths, and potentially recover partial or complete credential information through iterative timing analysis.
Root Cause
The root cause of this vulnerability is the use of non-constant-time string comparison functions when validating authentication credentials. Standard string comparison operations in JavaScript (and most programming languages) terminate early upon finding the first mismatched character, creating measurable timing differences. Secure authentication implementations should use constant-time comparison functions that always perform the same number of operations regardless of input, preventing timing analysis attacks.
Attack Vector
The attack vector for this vulnerability is network-based. An attacker can remotely send authentication requests to the application using the vulnerable @perfood/couch-auth library and measure response times with high precision. By sending multiple requests with systematically varied credentials and performing statistical analysis on the response times, the attacker can extract information about valid credentials.
The attack requires network access to the authentication endpoint but does not require any prior authentication or special privileges. The attacker can perform this attack from anywhere on the network that can reach the vulnerable service.
A proof-of-concept demonstrating this timing attack methodology is available in the GitHub Gist PoC. For additional context on the affected library, see the GitHub Repository for Couch-Auth and the NPM Package for Couch-Auth.
Detection Methods for CVE-2025-70949
Indicators of Compromise
- High volume of authentication attempts from single IP addresses with systematically varying credentials
- Unusual patterns of failed authentication attempts that appear to be testing character-by-character variations
- Network traffic analysis showing precisely timed authentication requests at regular intervals
Detection Strategies
- Deploy application-layer monitoring to detect anomalous authentication request patterns
- Implement rate limiting with logging to identify potential timing attack enumeration attempts
- Configure SIEM rules to alert on statistical anomalies in authentication failure timing patterns
- Use network intrusion detection systems to identify high-frequency authentication probing
Monitoring Recommendations
- Monitor authentication endpoints for unusual request volume and timing patterns
- Enable detailed logging of authentication attempts including timestamps and source IPs
- Implement alerting for authentication brute-force patterns that may indicate timing analysis
- Review application logs for evidence of credential enumeration activities
How to Mitigate CVE-2025-70949
Immediate Actions Required
- Audit applications using @perfood/couch-auth version 0.26.0 for exposure
- Implement rate limiting on authentication endpoints to slow timing attack attempts
- Deploy web application firewall rules to detect and block suspicious authentication patterns
- Consider implementing additional authentication factors to reduce the impact of credential disclosure
Patch Information
No official patch information is currently available for this vulnerability. Monitor the GitHub Repository for Couch-Auth and NPM Package for Couch-Auth for updates. Organizations using this library should evaluate whether to upgrade when a patched version becomes available or implement compensating controls.
Workarounds
- Implement network-level rate limiting to throttle authentication requests and reduce timing attack effectiveness
- Add random delays to authentication responses to obscure timing differences (jitter injection)
- Deploy a reverse proxy with request timing normalization capabilities
- Consider wrapping authentication functions with constant-time comparison implementations
- Implement account lockout policies to limit the number of authentication attempts per account
# Example: Rate limiting configuration for nginx
# Add to server or location block for authentication endpoint
limit_req_zone $binary_remote_addr zone=auth_limit:10m rate=5r/s;
location /auth {
limit_req zone=auth_limit burst=10 nodelay;
# Additional authentication endpoint configuration
}
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.


