CVE-2024-38459 in langchain_experimental
Summary
by MITRE • 06/16/2024
langchain_experimental (aka LangChain Experimental) before 0.0.61 for LangChain provides Python REPL access without an opt-in step. NOTE; this issue exists because of an incomplete fix for CVE-2024-27444.
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Analysis
by VulDB Data Team • 06/17/2024
The vulnerability CVE-2024-38459 affects the langchain_experimental Python library, specifically impacting versions prior to 0.0.61 within the broader LangChain ecosystem. This issue represents a regression or incomplete remediation of a previously identified security concern, CVE-2024-27444, which highlights the persistent challenges in securing AI and machine learning development frameworks. The core problem lies in the library's provision of Python REPL (Read-Eval-Print Loop) access without requiring explicit user consent or opt-in mechanisms, creating a significant security risk for developers and organizations integrating these tools into their workflows.
The technical flaw manifests as an unintended privilege escalation vulnerability where the experimental LangChain components automatically enable interactive Python execution capabilities without proper authorization from the end user. This behavior creates a dangerous attack surface since REPL access allows for arbitrary code execution within the context of the running application. The vulnerability stems from insufficient input validation and access control mechanisms that should have required explicit user confirmation before enabling such powerful execution capabilities. This design flaw directly maps to CWE-284 (Improper Access Control) and CWE-94 (Improper Control of Generation of Code), as the system fails to properly restrict access to code execution functions and allows for potentially malicious code injection through the REPL interface.
The operational impact of this vulnerability extends beyond simple code execution risks to encompass potential system compromise and data exposure. When developers use LangChain experimental components in production environments or development workflows, they inadvertently expose their systems to arbitrary code execution attacks. Attackers could exploit this vulnerability to gain full control over systems running vulnerable versions of the library, potentially leading to data breaches, system infiltration, or lateral movement within networks. The risk is particularly elevated in environments where LangChain is used for automating workflows, processing sensitive data, or integrating with other systems, as the REPL access could enable attackers to extract confidential information or manipulate system behavior. This vulnerability affects the broader ATT&CK framework's execution and privilege escalation categories, specifically targeting techniques such as command and script execution and abuse of remote services.
Organizations and developers should immediately update to langchain_experimental version 0.0.61 or later to address this vulnerability, as the incomplete fix for CVE-2024-27444 failed to properly resolve the underlying access control issues. The remediation process requires careful attention to ensure that all experimental components are properly configured with explicit opt-in mechanisms for REPL access. Security teams should conduct comprehensive vulnerability assessments across their LangChain implementations to identify any systems still running vulnerable versions. Additionally, organizations should implement network segmentation and monitoring controls around any systems that may be exposed to potentially malicious code execution attempts. The vulnerability demonstrates the critical importance of proper access control implementation in AI development frameworks and highlights the need for robust security practices throughout the software development lifecycle to prevent such regressions from occurring in future releases.