CVE-2023-29374 in LangChain
Summary
by MITRE • 04/05/2023
In LangChain through 0.0.131, the LLMMathChain chain allows prompt injection attacks that can execute arbitrary code via the Python exec method.
VulDB is the best source for vulnerability data and more expert information about this specific topic.
Analysis
by VulDB Data Team • 07/01/2025
The vulnerability identified as CVE-2023-29374 represents a critical security flaw within the LangChain framework version 0.0.131 and earlier, specifically affecting the LLMMathChain component. This issue stems from insufficient input validation and sanitization mechanisms that allow malicious actors to inject crafted prompts capable of executing arbitrary code on the underlying system. The vulnerability exists within the mathematical reasoning chain functionality that processes user inputs through language models and attempts to solve mathematical problems by generating Python code snippets for execution.
The technical implementation of this vulnerability leverages the Python exec method within the LLMMathChain processing pipeline, creating a direct code execution pathway when malformed inputs are processed. The flaw occurs because the system does not adequately filter or sanitize user-provided mathematical expressions before passing them to the Python execution environment. This creates a prompt injection scenario where attackers can manipulate the input to include malicious Python code that gets executed within the context of the running application. The vulnerability is classified under CWE-94, which specifically addresses "Improper Control of Generation of Code" and falls under the broader category of code injection vulnerabilities.
From an operational impact perspective, this vulnerability presents a severe risk to systems utilizing LangChain for mathematical processing tasks, particularly in environments where user inputs are not properly validated or where the framework is deployed in production settings. Attackers could potentially execute arbitrary commands, access sensitive data, compromise system integrity, or establish persistent access through the code execution capability. The vulnerability affects any application that relies on the LLMMathChain component for processing mathematical queries, making it particularly dangerous in enterprise environments where such frameworks are commonly integrated into larger applications. The attack surface is expanded when the framework is used in conjunction with other components that may not properly isolate or validate inputs, creating additional vectors for exploitation.
Mitigation strategies for CVE-2023-29374 should prioritize immediate patching of the LangChain framework to version 0.0.132 or later, which includes fixes for the prompt injection vulnerability in the LLMMathChain component. Organizations should implement strict input validation and sanitization measures before any user input is processed by the mathematical reasoning chain, ensuring that all expressions are properly validated against known safe patterns and mathematical syntax. Additional protective measures include implementing sandboxing techniques to limit the execution environment of code generation, employing input/output filtering mechanisms, and establishing proper access controls to restrict the privileges of the execution context. Security practitioners should also consider implementing network segmentation and monitoring for suspicious code execution patterns, as outlined in the attack techniques documented under ATT&CK matrix tactic TA0002 (Execution) and technique T1059.001 (Command and Scripting Interpreter). Organizations should conduct comprehensive security assessments of all LangChain implementations and review their input handling mechanisms to prevent similar vulnerabilities from existing in other components of the framework or related systems.