CVE-2018-19335 in Monorail
Summary
by MITRE
Google Monorail before 2018-06-07 has a Cross-Site Search (XS-Search) vulnerability because CSV downloads are affected by CSRF, and calculations of download times (for requests with a crafted groupby value) can be used to obtain sensitive information about the content of bug reports.
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Analysis
by VulDB Data Team • 04/14/2020
The vulnerability CVE-2018-19335 represents a sophisticated Cross-Site Search (XS-Search) flaw discovered in Google Monorail before June 7, 2018, which demonstrates the dangerous intersection of improper access control and information disclosure mechanisms within web applications. This vulnerability specifically affects the CSV download functionality of the Monorail bug tracking system, exposing sensitive data through a technique that leverages timing variations in request processing. The flaw arises from the lack of proper Cross-Site Request Forgery (CSRF) protection mechanisms in the download endpoints, combined with the predictable timing behavior of database queries when processing crafted groupby parameters. The vulnerability operates by exploiting the timing differences in query execution when the system processes different groupby values, allowing an attacker to infer information about the underlying data structure and content through statistical analysis of response times.
The technical implementation of this vulnerability follows a classic XS-Search attack pattern where an attacker crafts malicious requests with specific groupby parameters that cause the system to perform different computational workloads. When the system processes these requests, it returns CSV data that includes timing information indirectly through the varying response times, which can be measured by an attacker controlling a web page. The vulnerability specifically targets the download functionality that processes bug reports, making it particularly dangerous as it could expose confidential information about security issues, user details, or internal project data that should remain protected. This flaw is classified under CWE-352, which deals with Cross-Site Request Forgery, and demonstrates how CSRF protections can be insufficient when combined with information leakage through timing channels. The attack vector requires the victim to be authenticated to the Monorail system, as the timing variations are only observable within the context of legitimate user sessions, making this a session-based information disclosure vulnerability.
The operational impact of CVE-2018-19335 extends beyond simple data exposure, as it provides attackers with a method to systematically gather information about the structure and content of bug reports without direct access to the data itself. This type of vulnerability can be particularly damaging in security-sensitive environments where bug tracking systems contain classified information about security vulnerabilities, internal project details, or user data. The timing-based approach allows for statistical analysis that can reveal patterns in the bug report data, potentially enabling attackers to reconstruct sensitive information about security issues or identify potential targets for further exploitation. This vulnerability aligns with ATT&CK technique T1083, Information Discovery, as it enables attackers to gather information about the target environment through indirect timing channels rather than direct data access methods.
Mitigation strategies for CVE-2018-19335 require implementing comprehensive CSRF protection mechanisms alongside addressing the timing information leakage. Organizations should ensure that all download and data export functions include proper CSRF tokens and that timing variations are not exposed through response characteristics. The fix typically involves adding robust CSRF protection to the download endpoints, implementing consistent response times regardless of input parameters, and ensuring that sensitive data is not exposed through indirect timing channels. Additionally, organizations should implement rate limiting and monitoring for unusual download patterns that could indicate exploitation attempts. The vulnerability highlights the importance of considering timing channels as potential information leakages and demonstrates that even seemingly benign features like CSV downloads can become attack vectors when combined with insufficient access controls and predictable computational behavior. Security teams should also consider implementing proper input validation and sanitization for groupby parameters to prevent exploitation through crafted requests that manipulate database query execution paths.