CVE-2025-46724: Langroid Langroid RCE Vulnerability

CVE-2025-46724 Overview

CVE-2025-46724 is a critical code injection vulnerability discovered in Langroid, a Python framework for building large language model (LLM)-powered applications. The vulnerability exists in the TableChatAgent component, which uses pandas eval() to process user input. When deployed in public-facing LLM applications that accept untrusted user input, this implementation allows attackers to inject and execute arbitrary Python code on the underlying system.

Critical Impact
Attackers can achieve remote code execution by injecting malicious Python code through the TableChatAgent component in public-facing Langroid applications, potentially leading to complete system compromise.

Affected Products

Langroid versions prior to 0.53.15

Discovery Timeline

2025-05-20 - CVE-2025-46724 published to NVD
2025-06-17 - Last updated in NVD database

Technical Details for CVE-2025-46724

Vulnerability Analysis

This code injection vulnerability (CWE-94) stems from the unsafe use of Python's pandas.eval() function within the TableChatAgent component. The pandas.eval() function is designed to evaluate Python expressions for performance optimization in data operations, but when combined with untrusted user input, it becomes a dangerous attack surface for code injection.

In LLM-powered applications, user prompts are often parsed and transformed into operations on underlying data structures. When TableChatAgent processes these inputs without proper sanitization, an attacker can craft malicious input that escapes the intended data context and executes arbitrary Python code. This is particularly concerning for public-facing chatbot applications where external users can interact with the system.

The vulnerability allows attackers to bypass the application's intended functionality entirely, executing system commands, accessing sensitive data, or establishing persistent access to the compromised environment.

Root Cause

The root cause of this vulnerability is the direct passing of unsanitized user input to pandas.eval() within the TableChatAgent class. Python's eval() family of functions inherently execute code, and without strict input validation and sanitization, any expression provided by an attacker will be evaluated with the privileges of the running Python process.

Attack Vector

This vulnerability is exploitable over the network without authentication. An attacker targeting a public-facing Langroid application can submit specially crafted input through the LLM interface that, when processed by TableChatAgent, escapes the data context and executes arbitrary Python code. The attack requires no user interaction and can be automated, making it highly dangerous for exposed applications.

Common attack patterns include:

Injecting Python system commands via os.system() or subprocess calls
Reading sensitive files from the server filesystem
Establishing reverse shells for persistent access
Accessing environment variables containing credentials or API keys

The security patch introduces a sanitize_command function to validate and sanitize user input before it reaches the evaluation functions:

python

 from langroid.utils.configuration import settings
 from langroid.utils.object_registry import ObjectRegistry
 from langroid.utils.output.printing import print_long_text
-from langroid.utils.pandas_utils import stringify
+from langroid.utils.pandas_utils import sanitize_command, stringify
 from langroid.utils.pydantic_utils import flatten_dict
 
 logger = logging.getLogger(__name__)

Source: GitHub Commit Details

Detection Methods for CVE-2025-46724

Indicators of Compromise

Unusual system command execution patterns originating from Python/Langroid processes
Unexpected network connections from the application server to external hosts
Anomalous file system access by the Langroid application outside normal data directories
Log entries showing error messages related to malformed pandas expressions or Python execution errors

Detection Strategies

Monitor application logs for suspicious input patterns containing Python keywords like import, os., subprocess, exec, or eval
Implement runtime application security monitoring to detect code execution attempts within the Langroid process
Deploy web application firewalls (WAF) with rules to identify code injection payloads in LLM chat inputs
Review outbound network connections from application servers for unexpected communication patterns

Monitoring Recommendations

Enable verbose logging for the TableChatAgent component to capture all processed user inputs
Set up alerts for process spawning or network socket creation from the Langroid application
Monitor for file access patterns that deviate from normal application behavior
Track CPU and memory usage anomalies that may indicate cryptominer deployment or resource abuse

How to Mitigate CVE-2025-46724

Immediate Actions Required

Upgrade Langroid to version 0.53.15 or later immediately
Audit all deployed Langroid applications for public exposure and assess risk
Review application logs for signs of exploitation attempts
Consider temporarily disabling TableChatAgent functionality in production until patching is complete

Patch Information

Langroid version 0.53.15 addresses this vulnerability by implementing input sanitization for TableChatAgent by default. The patch adds the sanitize_command function to filter out common attack vectors before user input is processed. The fix is available through the standard Python package manager. For detailed information about the security fix, see the GitHub Security Advisory.

Workarounds

Restrict network access to Langroid applications to trusted internal users only until patching is possible
Implement additional input validation layers before data reaches TableChatAgent
Deploy the application in a sandboxed container environment with minimal system privileges
Review and follow the security warnings added to the Langroid project documentation regarding risky behavior

bash

# Upgrade Langroid to the patched version
pip install --upgrade langroid>=0.53.15

# Verify installed version
pip show langroid | grep Version