CVE-2026-31841 Overview
CVE-2026-31841 is an information disclosure vulnerability in Hyperterse, a tool-first MCP (Model Context Protocol) framework designed for building AI-ready backend surfaces from declarative configuration. Prior to version 2.2.0, the search tool functionality allows LLMs to search for tools using natural language queries. When returning results, Hyperterse inadvertently exposed raw SQL queries that were intended to be executed internally and protected from public display, potentially revealing sensitive database schema and query logic to unauthorized parties.
Critical Impact
Attackers could leverage exposed SQL queries to gain insight into database structure, query patterns, and potentially identify further attack vectors against the underlying data layer.
Affected Products
- Hyperterse versions prior to v2.2.0
- Systems utilizing Hyperterse search tool functionality with LLM integration
Discovery Timeline
- 2026-03-12 - CVE CVE-2026-31841 published to NVD
- 2026-03-12 - Last updated in NVD database
Technical Details for CVE-2026-31841
Vulnerability Analysis
This vulnerability falls under CWE-433 (Unparsed Raw Web Content Delivery), where the application fails to properly sanitize or filter internal implementation details before presenting them to users. In the context of Hyperterse, when an LLM utilizes the search tool to find tools using natural language, the framework returns not only the expected search results but also the underlying SQL queries used to retrieve those results.
The exposure of raw SQL queries presents several security concerns. First, it reveals database schema information including table names, column names, and relationships that should remain confidential. Second, it exposes the query construction logic which could help attackers understand how the application processes data. Third, this information could be leveraged to craft more sophisticated attacks such as SQL injection if other input validation weaknesses exist.
Root Cause
The root cause of this vulnerability lies in improper output filtering within Hyperterse's search tool response handling. The framework was designed to execute SQL queries internally to power the natural language search functionality, but the response serialization logic failed to strip these internal queries before returning results to the LLM or end user. This represents a violation of the principle of least privilege and information hiding, where internal implementation details should never be exposed to external consumers regardless of their role.
Attack Vector
The attack vector for CVE-2026-31841 is network-based and requires no authentication or user interaction. An attacker can exploit this vulnerability by simply using the search tool functionality through normal application interfaces. The vulnerability is passively exploitable, meaning any legitimate use of the search feature would inadvertently expose the SQL queries without requiring any malicious manipulation of inputs.
The exploitation flow involves sending natural language search queries through the Hyperterse search tool interface. When the framework processes these queries, it translates them into SQL queries executed against the backend database. In vulnerable versions, both the search results and the raw SQL queries are returned in the response, allowing the attacker to observe database implementation details.
Detection Methods for CVE-2026-31841
Indicators of Compromise
- Unexpected SQL query strings appearing in API responses or logs from Hyperterse search tool endpoints
- Increased reconnaissance activity targeting search functionality
- Log entries showing attempts to enumerate database schema through repeated search queries
- Network traffic containing SQL syntax in application layer responses where it should not appear
Detection Strategies
- Monitor API responses from Hyperterse search endpoints for SQL syntax patterns (SELECT, FROM, WHERE, JOIN keywords)
- Implement application-layer inspection to detect SQL queries in outbound responses
- Review access logs for unusual patterns of search queries that may indicate reconnaissance
- Deploy data loss prevention rules to flag responses containing database query patterns
Monitoring Recommendations
- Enable verbose logging on Hyperterse instances to capture search tool request/response pairs
- Configure alerting for responses containing SQL reserved words from search tool endpoints
- Establish baseline behavior for search tool usage and alert on statistical anomalies
- Monitor for version fingerprinting attempts that may precede exploitation of known vulnerabilities
How to Mitigate CVE-2026-31841
Immediate Actions Required
- Upgrade Hyperterse to version 2.2.0 or later immediately
- Audit logs to determine if SQL queries have been exposed to unauthorized parties
- Review any LLM interactions or API responses that may have captured exposed SQL queries
- Assess whether exposed query patterns reveal sensitive schema information requiring additional remediation
Patch Information
The vulnerability has been addressed in Hyperterse version 2.2.0. The patch removes the raw SQL query exposure from search tool responses, ensuring only sanitized search results are returned to consumers. Users should upgrade to v2.2.0 or later by following the release notes available at the GitHub Release v2.2.0. Additional details about the security fix can be found in the GitHub Security Advisory GHSA-92gp-jfgx-9qpv.
Workarounds
- If immediate upgrade is not possible, consider temporarily disabling the search tool functionality until patching can be completed
- Implement a reverse proxy or API gateway rule to strip SQL-like patterns from search tool responses
- Restrict access to search tool endpoints to trusted internal users only until the patch is applied
- Deploy web application firewall rules to filter outbound responses containing SQL syntax from search endpoints
# Configuration example
# Upgrade Hyperterse to patched version
npm update hyperterse@2.2.0
# Or using yarn
yarn upgrade hyperterse@2.2.0
# Verify installed version
npm list hyperterse
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
