CVE-2024-35515 in sqlitedict
Summary
by MITRE • 09/18/2024
Insecure deserialization in sqlitedict up to v2.1.0 allows attackers to execute arbitrary code.
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Analysis
by VulDB Data Team • 05/16/2026
Insecure deserialization vulnerabilities in sqlitedict versions up to v2.1.0 represent a critical security weakness that can lead to remote code execution. This vulnerability stems from the library's improper handling of serialized data structures during the deserialization process, creating an attack surface where malicious actors can inject crafted payloads that execute arbitrary code on the target system. The flaw exists because the library does not validate or sanitize serialized objects before processing them, allowing attackers to manipulate the deserialization flow and execute malicious instructions. This type of vulnerability falls under the CWE-502 category, which specifically addresses deserialization of untrusted data, and aligns with ATT&CK technique T1210 for exploitation of remote services. The impact is particularly severe in environments where sqlitedict is used for data persistence, as attackers can leverage this weakness to gain unauthorized access to systems, escalate privileges, or compromise entire application infrastructures. The vulnerability affects applications that rely on sqlitedict for database operations, particularly those handling user-provided data or external inputs that get serialized and stored in SQLite databases.
The technical implementation of this vulnerability occurs when sqlitedict processes serialized objects that contain malicious payloads designed to exploit the Python pickle module's deserialization mechanism. Attackers can craft serialized data that, when loaded by the vulnerable library, triggers code execution through the pickle protocol's ability to instantiate arbitrary objects and call methods during deserialization. This exploitation vector is particularly dangerous because pickle deserialization can execute arbitrary code without requiring special privileges or complex attack chains. The vulnerability manifests when applications using sqlitedict store or retrieve serialized data from databases, especially in scenarios involving user input, network communications, or file operations that involve pickle serialization. The attack typically involves creating a malicious serialized object that, when processed by the vulnerable library, executes commands on the host system. This behavior makes the vulnerability particularly attractive to threat actors targeting applications that utilize sqlitedict for data persistence and storage operations.
Organizations and developers using sqlitedict versions up to v2.1.0 face significant operational risks from this vulnerability, as it can be exploited remotely without requiring authentication or specialized knowledge of the target system. The vulnerability can be triggered through various attack vectors including web applications that store user data in sqlite databases, distributed systems that use sqlitedict for inter-process communication, or any application that accepts serialized input from untrusted sources. The exploitation process typically involves crafting a malicious serialized payload that leverages the pickle deserialization mechanism to execute arbitrary commands on the target system. This could result in complete system compromise, data exfiltration, or the establishment of persistent backdoors within the affected infrastructure. Security teams must consider the widespread use of sqlitedict in Python applications and the potential for cascading effects across interconnected systems that may be vulnerable to similar deserialization flaws. The vulnerability's impact extends beyond individual applications to potentially affect entire network infrastructures where sqlitedict is used for data management and persistence.
Mitigation strategies for this vulnerability primarily involve upgrading to sqlitedict version 2.1.1 or later, which includes proper input validation and sanitization measures to prevent malicious deserialization attempts. Organizations should conduct comprehensive vulnerability assessments to identify all applications using vulnerable versions of the library and prioritize remediation efforts accordingly. Additional protective measures include implementing strict input validation for all serialized data, using alternative serialization formats that do not support arbitrary code execution, and employing sandboxing techniques to limit the potential impact of successful attacks. Security configurations should enforce least privilege principles for database access and implement network segmentation to limit the attack surface. Organizations should also establish monitoring and detection capabilities to identify suspicious deserialization activities and implement regular security updates to address emerging threats. The remediation process should include code reviews to identify potential deserialization points and ensure that all serialized data is properly validated before processing, aligning with industry best practices for secure coding and vulnerability management.