CVE-2017-3608 in Berkeley DB
Summary
by MITRE
Vulnerability in the Data Store component of Oracle Berkeley DB. The supported version that is affected is Prior to 6.2.32. Difficult to exploit vulnerability allows unauthenticated attacker with logon to the infrastructure where Data Store executes to compromise Data Store. Successful attacks require human interaction from a person other than the attacker. Successful attacks of this vulnerability can result in takeover of Data Store. CVSS 3.0 Base Score 7.0 (Confidentiality, Integrity and Availability impacts). CVSS Vector: (CVSS:3.0/AV:L/AC:H/PR:N/UI:R/S:U/C:H/I:H/A:H).
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Analysis
by VulDB Data Team • 11/29/2022
The vulnerability identified as CVE-2017-3608 resides within Oracle Berkeley DB's Data Store component, representing a significant security weakness that emerged in versions prior to 6.2.32. This database system serves as a foundational element for numerous applications requiring high-performance data storage solutions, making the identified flaw particularly concerning for organizations relying on its services. The vulnerability's classification as difficult to exploit indicates that while the attack vector requires specific conditions, the potential impact remains severe enough to warrant immediate attention. The CVSS 3.0 scoring system assigns this vulnerability a base score of 7.0, reflecting high severity across all three core security principles: confidentiality, integrity, and availability, with a vector indicating local access requirements, high attack complexity, no privilege requirements, and user interaction necessity.
The technical nature of this vulnerability stems from insufficient input validation within the Data Store component, creating opportunities for attackers to manipulate database operations through carefully crafted inputs. This flaw operates under the Common Weakness Enumeration framework as CWE-20, which encompasses "Improper Input Validation" - a fundamental security weakness that allows malicious actors to inject unintended data or commands into the system. The vulnerability specifically targets the database's processing mechanisms, potentially allowing an attacker who has already gained logon access to the underlying infrastructure to escalate privileges and assume complete control over the Data Store service. The requirement for human interaction suggests that while the attacker needs assistance from an authorized user, this dependency does not significantly reduce the overall risk profile since social engineering or insider threats could still facilitate successful exploitation.
The operational impact of this vulnerability extends beyond simple data compromise, as successful exploitation can result in complete takeover of the Data Store component, potentially leading to widespread service disruption and data loss. Organizations utilizing Oracle Berkeley DB in production environments face substantial risk from this vulnerability, particularly those operating in regulated industries where data integrity and availability are paramount. The local access requirement means that attackers must first establish a foothold on the target system, but once achieved, the vulnerability provides sufficient leverage to compromise the database service. The combination of high attack complexity and user interaction requirements creates a scenario where automated exploitation may be difficult, but targeted attacks leveraging social engineering or insider access could prove highly effective.
Mitigation strategies for CVE-2017-3608 should prioritize immediate patching of all affected Oracle Berkeley DB installations to version 6.2.32 or later, which contains the necessary security fixes. Organizations should implement network segmentation to limit access to database systems and reduce the attack surface available to potential adversaries. The principle of least privilege should be enforced across all database access points, ensuring that only authorized personnel have the necessary credentials to interact with the Data Store component. Additionally, organizations should monitor for unusual database access patterns and implement robust logging mechanisms to detect potential exploitation attempts. The vulnerability's classification under the ATT&CK framework would place it within the privilege escalation and persistence categories, highlighting the need for comprehensive monitoring and incident response procedures. Regular security assessments and vulnerability scanning should be conducted to identify similar weaknesses in other database components or applications that may be similarly affected by input validation flaws.