CVE-2026-54284 in sqlparse
Summary
by MITRE • 08/17/2026
sqlparse is a non-validating SQL parser module for Python. Prior to 0.6.0, TokenList construction and string conversion in sqlparse/sql.py repeatedly flatten nested token subtrees constructed by group_parenthesis and group_case, causing quadratic CPU consumption through sqlparse.parse(), sqlparse.format(), and sqlparse.split() before depth and token limits terminate processing. This issue is fixed in version 0.6.0.
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Analysis
by VulDB Data Team • 08/17/2026
The vulnerability identified within the sqlparse library, a widely used non-validating SQL parser for Python applications, represents a significant performance degradation risk stemming from inefficient algorithmic design rather than a traditional security breach like code injection or memory corruption. The core technical flaw resides in how TokenList objects are constructed and converted to strings during the parsing process. Specifically, functions such as group_parenthesis and group_case generate nested token subtrees that represent complex SQL structures involving parentheses and conditional logic. In versions prior to 0.6.0, the subsequent flattening of these nested structures into a linear list of tokens was implemented in a manner that resulted in quadratic time complexity relative to the depth and size of the input SQL statement. This means that as the nesting level or token count increases, the processing time does not grow linearly but rather exponentially, leading to severe CPU consumption spikes.
From an operational perspective, this inefficiency creates a tangible risk for denial-of-service conditions in any application that relies on sqlparse for parsing user-supplied SQL input before execution or analysis. Attackers can craft specially designed SQL strings with deeply nested parentheses or complex case statements to trigger the quadratic behavior. When such inputs are processed through entry points like parse(), format(), or split(), the system may become unresponsive as it exhausts available CPU resources attempting to flatten the token tree. This is particularly dangerous in web applications, database administration tools, or any service that parses SQL dynamically based on user input without strict length or complexity limits prior to parsing. The lack of early termination mechanisms for excessive depth before processing begins allows malicious actors to effectively stall services by forcing them into infinite-like loops of computation until system resources are depleted or the process is killed by an external watchdog.
This vulnerability aligns with CWE-400, which describes uncontrolled resource consumption, and can be mapped to MITRE ATT&CK technique T1496, Resource Hijacking, where adversaries leverage inefficient code paths to consume computational resources and degrade service availability for legitimate users. The issue is not about the correctness of the SQL parsing logic itself but rather the efficiency of the internal data structure manipulation during string conversion and tree flattening. While sqlparse does not execute the SQL, making it less prone to direct injection attacks if used correctly as a parser only, its role in preprocessing often places it directly in the path of untrusted input, thereby amplifying the impact of this performance flaw.
The remediation for this vulnerability is straightforward and has already been addressed by the maintainers in version 0.6.0 of the library. The fix involves optimizing the TokenList construction and string conversion algorithms to ensure linear time complexity regardless of nesting depth or token count. Organizations utilizing sqlparse must upgrade to version 0.6.0 or later to eliminate this performance bottleneck. In addition to upgrading, it is recommended that developers implement strict input validation policies for SQL strings before they are passed to the parser. This includes enforcing maximum length limits and potentially restricting the complexity of nested structures allowed in user-supplied queries. By combining library updates with robust input sanitization strategies, organizations can mitigate both this specific denial-of-service vector and broader risks associated with processing untrusted SQL data.