Summary
Hypequery's ClickHouse semantic layer contained an SQL injection flaw in its parameter escaping logic, where backslashes before single quotes were not properly escaped.
An attacker who can control query parameters with a trailing backslash can break out of the intended string literal and inject arbitrary SQL, leading to full compromise of the underlying ClickHouse data in vulnerable deployments.
Hypequery's ClickHouse semantic layer contained an SQL injection flaw in its parameter escaping logic, where backslashes before single quotes were not properly escaped.
An attacker who can control query parameters with a trailing backslash can break out of the intended string literal and inject arbitrary SQL, leading to full compromise of the underlying ClickHouse data in vulnerable deployments.
Affected context
The vulnerability appears in Hypequery's TypeScript-based semantic layer for ClickHouse, specifically in the escapeValue() helper used for parameter substitution.
It is relevant in environments where:
- Hypequery is used to construct SQL for ClickHouse, and
- Application code relies on
escapeValue()or similar logic for building SQL strings, rather than using native parameterized queries, and - User-controlled or otherwise untrusted values are interpolated into SQL statements.
Under these conditions, incorrect handling of backslashes before single quotes can allow crafted parameters to alter the surrounding query structure.
The vulnerability appears in Hypequery's TypeScript-based semantic layer for ClickHouse, specifically in the escapeValue() helper used for parameter substitution.
It is relevant in environments where:
- Hypequery is used to construct SQL for ClickHouse, and
- Application code relies on
escapeValue()or similar logic for building SQL strings, rather than using native parameterized queries, and - User-controlled or otherwise untrusted values are interpolated into SQL statements.
Under these conditions, incorrect handling of backslashes before single quotes can allow crafted parameters to alter the surrounding query structure.
Exploit preconditions
- Attack surface: Any API, UI, or integration that passes attacker-controlled input into Hypequery-generated SQL using
escapeValue(). - Required access: Ability to supply or influence query parameters (e.g., HTTP request fields, query filters, search terms) that are ultimately passed to Hypequery.
- Trigger point: Execution of a ClickHouse query where the vulnerable escaping is applied to a value containing a trailing backslash before the closing quote context.
- Why exploitation is possible: The escaping routine failed to escape backslashes appearing immediately before single quotes, enabling an attacker to terminate the intended string literal and inject additional SQL tokens or statements.
- Attack surface: Any API, UI, or integration that passes attacker-controlled input into Hypequery-generated SQL using
escapeValue(). - Required access: Ability to supply or influence query parameters (e.g., HTTP request fields, query filters, search terms) that are ultimately passed to Hypequery.
- Trigger point: Execution of a ClickHouse query where the vulnerable escaping is applied to a value containing a trailing backslash before the closing quote context.
- Why exploitation is possible: The escaping routine failed to escape backslashes appearing immediately before single quotes, enabling an attacker to terminate the intended string literal and inject additional SQL tokens or statements.
Why this happens
The core issue maps to CWE-89: Improper Neutralization of Special Elements used in an SQL Command ('SQL Injection').
Instead of delegating input handling to the database's parameterization mechanisms, Hypequery employed a custom escapeValue() function to sanitize values before string concatenation into SQL.
This function did not correctly handle backslashes preceding single quotes, leaving a gap where certain character sequences were not neutralized.
As a result, the SQL construction pattern trusted string escaping as a security boundary, and a single escaping error allowed untrusted input to be interpreted as executable SQL syntax.
The core issue maps to CWE-89: Improper Neutralization of Special Elements used in an SQL Command ('SQL Injection').
Instead of delegating input handling to the database's parameterization mechanisms, Hypequery employed a custom escapeValue() function to sanitize values before string concatenation into SQL.
This function did not correctly handle backslashes preceding single quotes, leaving a gap where certain character sequences were not neutralized.
As a result, the SQL construction pattern trusted string escaping as a security boundary, and a single escaping error allowed untrusted input to be interpreted as executable SQL syntax.
Potential impact
- Attackers may inject arbitrary SQL into ClickHouse queries.
- Query logic can be modified to bypass filters, row-level security assumptions, or business rules.
- Sensitive data (including entire tables) could be read, aggregated, or exfiltrated.
- Data may be altered or deleted, affecting integrity and availability of analytics workloads.
- Under some configurations, additional stored procedures or secondary systems integrated with ClickHouse may be impacted.
- Attackers may inject arbitrary SQL into ClickHouse queries.
- Query logic can be modified to bypass filters, row-level security assumptions, or business rules.
- Sensitive data (including entire tables) could be read, aggregated, or exfiltrated.
- Data may be altered or deleted, affecting integrity and availability of analytics workloads.
- Under some configurations, additional stored procedures or secondary systems integrated with ClickHouse may be impacted.
General mitigation guidance
Effective mitigation focuses on avoiding manual SQL construction and reducing trust in escaping routines:
- Prefer database-native parameterized queries or prepared statements rather than hand-built SQL strings.
- Centralize and minimize custom escaping logic; where unavoidable, ensure it is rigorously specified, tested, and code-reviewed for all special characters and edge cases.
- Apply strict input validation at the boundary of your API, constraining types, lengths, and allowed characters for fields that influence queries.
- Implement least-privilege database roles for Hypequery-generated connections so that even a successful injection has reduced impact (e.g., read-only where possible, no schema modification rights).
- Isolate analytics workloads from core transactional data where practical, so compromised analytics queries have a smaller blast radius.
Effective mitigation focuses on avoiding manual SQL construction and reducing trust in escaping routines:
- Prefer database-native parameterized queries or prepared statements rather than hand-built SQL strings.
- Centralize and minimize custom escaping logic; where unavoidable, ensure it is rigorously specified, tested, and code-reviewed for all special characters and edge cases.
- Apply strict input validation at the boundary of your API, constraining types, lengths, and allowed characters for fields that influence queries.
- Implement least-privilege database roles for Hypequery-generated connections so that even a successful injection has reduced impact (e.g., read-only where possible, no schema modification rights).
- Isolate analytics workloads from core transactional data where practical, so compromised analytics queries have a smaller blast radius.
How teams detect issues like this
Detection typically involves both code-centric and behavior-centric approaches:
- Static analysis: Configure rules to flag dynamic SQL construction that concatenates variables or uses custom escaping instead of parameterized interfaces. Focus on calls to
escapeValue()and downstream query-building utilities. - Code review patterns: During reviews, highlight any manual string interpolation in SQL, especially where backslashes, quotes, or other metacharacters are manipulated. Require clear documentation of escaping rules and tests.
- Unit and integration tests: Maintain regression test suites with malicious payloads (including trailing backslashes, mixed quotes, and escape sequences) to verify that query-building helpers do not alter intended SQL structure.
- Runtime monitoring: Use query logging and anomaly detection on ClickHouse to spot unusual patterns, such as unexpected comment tokens, stacked queries, or deviations from known query templates.
Detection typically involves both code-centric and behavior-centric approaches:
- Static analysis: Configure rules to flag dynamic SQL construction that concatenates variables or uses custom escaping instead of parameterized interfaces. Focus on calls to
escapeValue()and downstream query-building utilities. - Code review patterns: During reviews, highlight any manual string interpolation in SQL, especially where backslashes, quotes, or other metacharacters are manipulated. Require clear documentation of escaping rules and tests.
- Unit and integration tests: Maintain regression test suites with malicious payloads (including trailing backslashes, mixed quotes, and escape sequences) to verify that query-building helpers do not alter intended SQL structure.
- Runtime monitoring: Use query logging and anomaly detection on ClickHouse to spot unusual patterns, such as unexpected comment tokens, stacked queries, or deviations from known query templates.
