CVE-2026-30835 Overview
Parse Server, an open source backend framework deployable on any Node.js infrastructure, contains an information disclosure vulnerability in its query parameter handling. Prior to versions 8.6.7 and 9.5.0-alpha.6, a malformed $regex query parameter (such as [abc) causes the database to return a structured error object that is passed unsanitized through the API response. This behavior leaks sensitive database internals including error messages, error codes, code names, cluster timestamps, and topology details.
Critical Impact
This vulnerability allows unauthenticated attackers to extract sensitive database infrastructure details through crafted API queries, potentially enabling reconnaissance for more sophisticated attacks against the underlying MongoDB deployment.
Affected Products
- Parse Server versions prior to 8.6.7
- Parse Server 9.5.0-alpha.1 through 9.5.0-alpha.5
- Any Parse Server deployment with exposed query endpoints
Discovery Timeline
- 2026-03-06 - CVE-2026-30835 published to NVD
- 2026-03-11 - Last updated in NVD database
Technical Details for CVE-2026-30835
Vulnerability Analysis
This vulnerability is classified as CWE-209 (Generation of Error Message Containing Sensitive Information). The core issue lies in Parse Server's error handling mechanism when processing regular expression query parameters. When a malformed regex pattern is submitted through the $regex query operator, the underlying MongoDB driver generates a detailed error response containing internal database information.
Instead of sanitizing or filtering these error responses before returning them to the client, Parse Server passes the raw database error object directly through the API response. This creates an information exposure condition where attackers can deliberately trigger errors to harvest database infrastructure details.
The vulnerability is network-accessible and requires no authentication or user interaction to exploit, depending on the deployment's permission configuration. While the direct impact is limited to information disclosure rather than data modification or system compromise, the exposed details can significantly aid attackers in planning subsequent attacks against the database infrastructure.
Root Cause
The root cause is improper error handling in Parse Server's query processing pipeline. When MongoDB encounters an invalid regular expression, it returns a structured error object containing diagnostic information intended for debugging purposes. Parse Server fails to sanitize these error responses before forwarding them to API clients, violating the principle of minimal information disclosure.
The error objects can contain:
- Detailed error messages describing the regex parsing failure
- MongoDB error codes and code names
- Cluster timestamps that reveal timing information
- Topology details exposing database architecture
Attack Vector
The attack vector is network-based, requiring only the ability to send query requests to a Parse Server instance. An attacker crafts a malformed regular expression pattern within a $regex query parameter and submits it through the Parse Server API. The malformed pattern triggers a parsing error in MongoDB, which returns a detailed error response that Parse Server passes through unsanitized.
The malformed regex exploitation technique involves submitting an intentionally invalid regular expression pattern (such as an unclosed character class like [abc) through the $regex query operator. When MongoDB's regex parser encounters this invalid pattern, it generates a detailed error response. Parse Server's insufficient error sanitization then exposes this internal database information to the attacker through the API response. For full technical details, see the GitHub Security Advisory.
Detection Methods for CVE-2026-30835
Indicators of Compromise
- API responses containing MongoDB error objects with fields like code, codeName, clusterTime, or topology information
- Unusual patterns of query requests containing malformed regex patterns in the $regex parameter
- Error log entries showing repeated regex parsing failures from similar source addresses
Detection Strategies
- Implement API response monitoring to detect and alert on responses containing database error structure patterns
- Deploy web application firewall rules to identify and block requests with malformed regex patterns in query parameters
- Enable detailed logging of query parameters and correlate with error responses to identify exploitation attempts
Monitoring Recommendations
- Monitor Parse Server access logs for suspicious patterns of malformed $regex queries from individual IP addresses
- Set up alerting for API responses that include MongoDB-specific error fields or topology information
- Review application logs for repeated regex validation errors that may indicate active exploitation
How to Mitigate CVE-2026-30835
Immediate Actions Required
- Upgrade Parse Server to version 8.6.7 or 9.5.0-alpha.6 or later immediately
- Review access logs for evidence of exploitation attempts using malformed regex queries
- Audit API responses to ensure no sensitive database information is being exposed
Patch Information
Parse Platform has released patched versions that address this vulnerability. Organizations should upgrade to Parse Server 8.6.7 for stable deployments or Parse Server 9.5.0-alpha.6 for those tracking the alpha release channel. The patches implement proper error sanitization to prevent database internals from being exposed through API responses.
For additional details, refer to the GitHub Security Advisory GHSA-9cp7-3q5w-j92g.
Workarounds
- Implement an API gateway or reverse proxy layer to filter and sanitize error responses before they reach clients
- Restrict query endpoint access through stricter permission configurations to limit exposure
- Deploy input validation middleware to reject malformed regex patterns before they reach Parse Server
# Example: Update Parse Server using npm
npm update parse-server@8.6.7
# Or for alpha channel users
npm update parse-server@9.5.0-alpha.6
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.

