Vulnerabilities > CVE-2021-25958 - Information Exposure Through an Error Message vulnerability in Apache Ofbiz
In Apache Ofbiz, versions v17.12.01 to v17.12.07 implement a try catch exception to handle errors at multiple locations but leaks out sensitive table info which may aid the attacker for further recon. A user can register with a very long password, but when he tries to login with it an exception occurs.
Common Weakness Enumeration (CWE)
Common Attack Pattern Enumeration and Classification (CAPEC)
- Fuzzing for garnering J2EE/.NET-based stack traces, for application mapping An attacker sends random, malformed, or otherwise unexpected messages to a target application and observes any stack traces produced by error messages. Fuzzing techniques involve sending random or malformed messages to a target and monitoring the target's response. The attacker does not initially know how a target will respond to individual messages but by attempting a large number of message variants they may find a variant that trigger's desired behavior. In this attack, the purpose of the fuzzing is to cause the targeted application to return an error including a stack trace, although fuzzing a target can also sometimes cause the target to enter an unstable state, causing a crash. The stack trace enumerates the chain of methods that led up to the point where the error was encountered. This can not only reveal the names of the methods (some of which may have known weaknesses) but possibly also the location of class files and libraries as well as parameter values. In some cases, the stack trace might even disclose sensitive configuration or user information.
- Fuzzing and observing application log data/errors for application mapping An attacker sends random, malformed, or otherwise unexpected messages to a target application and observes the application's log or error messages returned. Fuzzing techniques involve sending random or malformed messages to a target and monitoring the target's response. The attacker does not initially know how a target will respond to individual messages but by attempting a large number of message variants they may find a variant that trigger's desired behavior. In this attack, the purpose of the fuzzing is to observe the application's log and error messages, although fuzzing a target can also sometimes cause the target to enter an unstable state, causing a crash. By observing logs and error messages, the attacker can learn details about the configuration of the target application and might be able to cause the target to disclose sensitive information.
- Probe Application Error Reporting An Attacker, aware of an application's location (and possibly authorized to use the application) can probe the application's structure and evaluate its robustness by probing its error conditions (not unlike one would during a 'fuzz' test, but more purposefully here) in order to support attacks such as blind SQL injection, or for the more general task of mapping the application to mount another subsequent attack.
- Blind SQL Injection Blind SQL Injection results from an insufficient mitigation for SQL Injection. Although suppressing database error messages are considered best practice, the suppression alone is not sufficient to prevent SQL Injection. Blind SQL Injection is a form of SQL Injection that overcomes the lack of error messages. Without the error messages that facilitate SQL Injection, the attacker constructs input strings that probe the target through simple Boolean SQL expressions. The attacker can determine if the syntax and structure of the injection was successful based on whether the query was executed or not. Applied iteratively, the attacker determines how and where the target is vulnerable to SQL Injection. For example, an attacker may try entering something like "username' AND 1=1; --" in an input field. If the result is the same as when the attacker entered "username" in the field, then the attacker knows that the application is vulnerable to SQL Injection. The attacker can then ask yes/no questions from the database server to extract information from it. For example, the attacker can extract table names from a database using the following types of queries: If the above query executes properly, then the attacker knows that the first character in a table name in the database is a letter between m and z. If it doesn't, then the attacker knows that the character must be between a and l (assuming of course that table names only contain alphabetic characters). By performing a binary search on all character positions, the attacker can determine all table names in the database. Subsequently, the attacker may execute an actual attack and send something like: