CVE-2024-46946: Langchain-experimental RCE Vulnerability

CVE-2024-46946 Overview

CVE-2024-46946 is a critical arbitrary code execution vulnerability in langchain_experimental (LangChain Experimental) versions 0.1.17 through 0.3.0. The vulnerability exists in the LLMSymbolicMathChain component, which uses sympy.sympify for symbolic mathematics operations. The sympify function internally uses Python's eval() function, allowing attackers to execute arbitrary code on systems running vulnerable versions of the library.

Critical Impact

This vulnerability allows unauthenticated remote attackers to execute arbitrary code with no user interaction required, potentially leading to complete system compromise, data exfiltration, or lateral movement within enterprise environments.

Affected Products

• langchain_experimental versions 0.1.17 through 0.3.0
• Applications utilizing LLMSymbolicMathChain functionality
• AI/ML pipelines integrating LangChain Experimental for symbolic mathematics

Discovery Timeline

• 2023-10-05 - LLMSymbolicMathChain introduced in commit fcccde406dd9e9b05fc9babcbeb9ff527b0ec0c6
• 2024-09-19 - CVE-2024-46946 published to NVD
• 2025-07-16 - Last updated in NVD database

Technical Details for CVE-2024-46946

Vulnerability Analysis

The vulnerability resides in the LLMSymbolicMathChain class within LangChain Experimental, which provides symbolic mathematics capabilities through the SymPy library. When processing user-supplied mathematical expressions, the chain passes input directly to sympy.sympify() without adequate sanitization.

The core issue is that SymPy's sympify function is documented to use eval() for parsing strings into symbolic expressions. This design choice, while convenient for legitimate mathematical operations, creates a direct code injection vector when processing untrusted input. An attacker can craft malicious input that, when evaluated, executes arbitrary Python code on the target system.

The attack requires network access to applications exposing LangChain functionality but does not require authentication or user interaction, making it particularly dangerous in AI-powered web applications and chatbots.

Root Cause

The root cause is the unsafe use of sympy.sympify() on unsanitized user input within the LLMSymbolicMathChain component. SymPy explicitly warns in its documentation that sympify uses eval and should not be used with untrusted input. The vulnerability represents a classic case of input validation failure (CWE-20) where user-controlled data reaches a dangerous sink function without proper sanitization.

Attack Vector

The attack is network-based and targets applications that expose LangChain Experimental's symbolic math functionality to users. An attacker can submit a specially crafted mathematical expression containing Python code injection payloads. When the LLMSymbolicMathChain processes this input through sympify, the embedded malicious code executes with the privileges of the application process.

Typical attack scenarios include:

• AI chatbots that offer mathematical computation features
• Educational platforms using LangChain for symbolic math assistance
• Data science applications integrating LLM-powered mathematical reasoning

The attacker crafts a malicious expression that bypasses any surface-level validation and is interpreted by sympy.sympify() as Python code. Since sympify uses eval() internally, the injected code executes directly in the Python runtime context. For detailed technical information, see the GitHub Gist CVE-2024-46946 for documented exploitation techniques.

Detection Methods for CVE-2024-46946

Indicators of Compromise

• Unusual process spawning from Python applications using LangChain
• Suspicious outbound network connections from AI/ML application servers
• Unexpected file system modifications in application directories
• Log entries containing malformed or suspicious mathematical expressions with Python code patterns
• Evidence of __import__, exec, eval, or os.system strings in application input logs

Detection Strategies

• Monitor application logs for mathematical expressions containing Python built-in functions or module imports
• Implement input validation rules to detect and block expressions containing dangerous patterns like __import__, subprocess, or os module references
• Deploy runtime application self-protection (RASP) to detect eval-based code injection attempts
• Use SentinelOne Singularity to detect anomalous behavior from Python processes

Monitoring Recommendations

• Enable verbose logging for LangChain applications to capture all user inputs to symbolic math chains
• Implement anomaly detection for unusual system calls originating from AI/ML services
• Monitor Python process behavior for signs of code injection such as spawning child processes or making unexpected network connections
• Set up alerts for dependency version checks to identify vulnerable langchain-experimental installations

How to Mitigate CVE-2024-46946

Immediate Actions Required

• Audit all applications using langchain-experimental to identify those with vulnerable versions (0.1.17 through 0.3.0)
• Temporarily disable or remove LLMSymbolicMathChain functionality in production applications until patches are applied
• Implement strict input validation to filter mathematical expressions before they reach symbolic math processing
• Upgrade to a patched version of langchain-experimental that addresses this vulnerability
• Review application logs for evidence of exploitation attempts

Patch Information

Organizations should update their langchain-experimental dependency to a version that addresses this vulnerability. The GitHub LangChain Release 0.3.0 page provides release notes and information about security updates. Consult the LangChain project's security advisories for specific patched versions and upgrade guidance.

Workarounds

• Remove or disable LLMSymbolicMathChain from production deployments if symbolic math functionality is not essential
• Implement a strict allowlist of permitted mathematical operations and expressions
• Use sympify with evaluate=False parameter and locals parameter to restrict available functions, though this may not provide complete protection
• Deploy the application in a sandboxed environment with minimal privileges to limit the impact of successful exploitation
• Implement network segmentation to isolate AI/ML services from critical infrastructure

# Configuration example - Upgrade langchain-experimental
pip install --upgrade langchain-experimental>=0.3.1

# Verify installed version
pip show langchain-experimental | grep Version

# Audit for vulnerable installations
pip list | grep langchain-experimental