CVE-2026-71491 in sqlparse
Summary
by MITRE • 08/17/2026
sqlparse is a non-validating SQL parser module for Python. Prior to 0.6.0, group_comments in sqlparse/engine/grouping.py repeatedly rescans comment-only statements before the MAX_GROUPING_TOKENS guard, causing quadratic CPU consumption through sqlparse.parse() and sqlparse.format(sql, strip_comments=True). 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 represents a significant performance degradation flaw rooted in inefficient algorithmic complexity during SQL statement parsing. Sqlparse serves as a widely adopted non-validating SQL parser for Python, utilized extensively by web frameworks and database tools to format or analyze raw SQL strings without executing them against a live database engine. The core issue lies within the grouping logic implemented in the library's internal engine module, specifically affecting how comment-only statements are processed prior to version 0.6.0. When an input string consists primarily of comments or contains sequences where comments dominate the token stream, the parser enters a state of repeated rescanning that fails to adhere to linear time complexity expectations for text processing tasks.
At the technical level, the flaw is located in the grouping mechanism which attempts to organize tokens into logical structures such as statements and clauses. The specific defect involves the handling of comment-only segments where the algorithm repeatedly re-evaluates previously scanned tokens without making sufficient forward progress toward a resolution state. This behavior occurs before the MAX_GROUPING_TOKENS guard can effectively truncate or limit the processing scope. Consequently, for inputs with substantial comment content, the number of operations required to parse the string grows quadratically relative to the length of the input rather than linearly. This means that doubling the size of the commented SQL text could result in four times the computational effort, leading to severe latency spikes and excessive CPU utilization during parsing or formatting operations.
The operational impact of this vulnerability is primarily centered on resource exhaustion and potential denial-of-service conditions within applications relying on sqlparse for dynamic SQL processing. In web application contexts where user-supplied input might be parsed using functions like sqlparse.parse() or formatted with strip_comments enabled, an attacker could craft maliciously sized comment-heavy payloads to trigger the quadratic behavior. This would consume disproportionate amounts of CPU cycles on the server side, potentially degrading service availability for legitimate users or causing complete unresponsiveness in resource-constrained environments such as containerized deployments or serverless functions where compute limits are strictly enforced. While this does not typically lead to remote code execution or data exfiltration directly through SQL injection due to the non-validating nature of the parser, it serves as a potent vector for application-level denial-of-service attacks targeting parsing endpoints.
Mitigation strategies focus on immediate version upgrades and defensive coding practices. The most effective remediation is upgrading sqlparse to version 0.6.0 or later, where the grouping logic has been refactored to prevent redundant rescanning of comment-only statements and ensure linear time complexity for such inputs. For environments unable to upgrade immediately due to dependency constraints, implementing input size limits on SQL strings before they are passed to the parser can help mitigate the risk by preventing excessively large payloads from triggering the quadratic behavior. Additionally, validating or sanitizing user-supplied SQL fragments prior to parsing adds a layer of defense against potential abuse. From a classification perspective, this issue aligns with CWE-400 Uncontrolled Resource Consumption and reflects patterns associated with inefficient algorithmic complexity that can be leveraged for denial-of-service attacks within the ATT&CK framework's resource hijacking tactics.