CVE-2022-28614 Overview
The ap_rwrite() function in Apache HTTP Server 2.4.53 and earlier may read unintended memory if an attacker can cause the server to reflect very large input using ap_rwrite() or ap_rputs(), such as with mod_lua's r:puts() function. Modules compiled and distributed separately from Apache HTTP Server that use the ap_rputs function and may pass it a very large (INT_MAX or larger) string must be compiled against current headers to resolve the issue.
Critical Impact
This integer overflow vulnerability enables remote attackers to potentially read unintended server memory contents through crafted requests, leading to information disclosure without requiring authentication.
Affected Products
- Apache HTTP Server (versions up to and including 2.4.53)
- Fedora Project Fedora (versions 35 and 36)
- NetApp Clustered Data ONTAP
Discovery Timeline
- 2022-06-09 - CVE CVE-2022-28614 published to NVD
- 2024-11-21 - Last updated in NVD database
Technical Details for CVE-2022-28614
Vulnerability Analysis
This vulnerability stems from an integer overflow condition (CWE-190) in the Apache HTTP Server's ap_rwrite() function. When processing requests that involve reflecting very large input strings, the function fails to properly validate the size of input data before processing. If an attacker can cause the server to handle strings approaching or exceeding INT_MAX (2,147,483,647 bytes), the integer overflow can cause the function to read beyond the intended memory boundaries.
The vulnerability is particularly relevant when using modules like mod_lua that expose the r:puts() function, which internally calls ap_rputs(). Third-party modules compiled against older Apache headers are especially vulnerable, as they may not include the necessary size validation checks.
Root Cause
The root cause is an integer overflow vulnerability in the ap_rwrite() and ap_rputs() functions. When these functions receive input strings with lengths at or near INT_MAX, the internal size calculations can overflow, causing the functions to operate on incorrect memory regions. This is a classic integer overflow issue where arithmetic operations on size values wrap around to unexpected values, leading to out-of-bounds memory reads.
Attack Vector
The attack vector is network-based and can be exploited remotely without authentication. An attacker must craft requests that cause the Apache HTTP Server to reflect very large input through the vulnerable functions. The primary exploitation path involves:
- Identifying Apache HTTP Server instances running version 2.4.53 or earlier
- Targeting endpoints that utilize mod_lua's r:puts() function or similar mechanisms
- Sending crafted requests with extremely large payloads designed to trigger the integer overflow
- Observing the server response for leaked memory contents
The vulnerability mechanism involves the ap_rwrite() function processing large string inputs. When the input size approaches INT_MAX, integer overflow occurs in the internal size calculations. This causes the function to read beyond the intended buffer boundaries, potentially exposing sensitive memory contents in the HTTP response. For detailed technical analysis, refer to the Apache HTTPD Vulnerabilities List.
Detection Methods for CVE-2022-28614
Indicators of Compromise
- Unusual HTTP responses containing unexpected binary data or memory fragments
- Web server logs showing requests with abnormally large payloads or Content-Length headers approaching INT_MAX
- Anomalous memory consumption patterns in Apache HTTP Server processes
- Network traffic containing responses significantly larger than expected for specific endpoints
Detection Strategies
- Monitor Apache access logs for requests with extremely large Content-Length headers or unusually sized request bodies
- Implement network-based intrusion detection rules to identify HTTP traffic patterns associated with large payload reflection attacks
- Deploy web application firewalls (WAF) with rules to block requests exceeding reasonable size thresholds
- Use SentinelOne's Singularity platform to detect anomalous process behavior and memory access patterns in Apache httpd processes
Monitoring Recommendations
- Enable verbose logging for Apache HTTP Server to capture detailed request information
- Implement real-time monitoring of Apache process memory usage for sudden spikes
- Configure alerts for HTTP responses containing binary or non-printable characters in text-based endpoints
- Monitor mod_lua script execution for unusual input sizes or execution patterns
How to Mitigate CVE-2022-28614
Immediate Actions Required
- Upgrade Apache HTTP Server to version 2.4.54 or later immediately
- Recompile any third-party Apache modules against current Apache HTTP Server headers
- Implement request size limits at the web server and WAF level to block oversized payloads
- Review and audit any mod_lua scripts that use the r:puts() function
Patch Information
Apache has released a fix for this vulnerability in Apache HTTP Server version 2.4.54 and later. Organizations should upgrade to the latest stable version as soon as possible. For detailed patch information and security advisories, consult the Apache HTTPD Vulnerabilities List.
Additional vendor-specific advisories are available:
Workarounds
- Implement strict request size limits using LimitRequestBody directive to prevent extremely large payloads
- Disable or restrict access to mod_lua endpoints if not required for business operations
- Deploy a reverse proxy or load balancer with request size filtering capabilities in front of Apache servers
- Use input validation in custom modules to reject strings approaching INT_MAX before passing to ap_rputs()
# Configuration example
# Add to Apache httpd.conf to limit request body size
# This helps mitigate the attack vector by preventing extremely large payloads
LimitRequestBody 10485760
# Limit request line and header sizes
LimitRequestLine 8190
LimitRequestFields 100
LimitRequestFieldSize 8190
# If mod_lua is not required, consider disabling it
# LoadModule lua_module modules/mod_lua.so
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.


