CVE-2025-46725: Langroid Python Framework RCE Vulnerability

CVE-2025-46725 Overview

CVE-2025-46725 is a code injection vulnerability in Langroid, a Python framework designed to build large language model (LLM)-powered applications. The vulnerability exists in the LanceDocChatAgent component, which uses pandas eval() through the compute_from_docs() function without proper input sanitization. This allows attackers to inject and execute malicious Python code by manipulating input processed through QueryPlan.dataframe_calc, potentially leading to complete system compromise.

Critical Impact

Attackers can achieve remote code execution on systems running vulnerable Langroid versions by injecting malicious payloads through the unsafe pandas eval() function, enabling full host system compromise.

Affected Products

• Langroid versions prior to 0.53.15
• Applications using LanceDocChatAgent with compute_from_docs() functionality
• Systems exposing Langroid-based LLM applications to untrusted input

Discovery Timeline

• 2025-05-20 - CVE-2025-46725 published to NVD
• 2025-08-13 - Last updated in NVD database

Technical Details for CVE-2025-46725

Vulnerability Analysis

This vulnerability falls under CWE-94 (Improper Control of Generation of Code), commonly known as code injection. The root issue stems from the direct use of pandas' eval() function to process user-controllable input without adequate sanitization. The pandas eval() function is designed to evaluate Python expressions and can execute arbitrary code when supplied with malicious input strings.

In the context of Langroid's LanceDocChatAgent, the compute_from_docs() method processes queries that may contain attacker-controlled data. When this data flows into the QueryPlan.dataframe_calc parameter, it reaches the vulnerable eval() call, enabling arbitrary Python code execution within the application's security context.

The vulnerability is network-exploitable with no authentication required, meaning any user who can interact with a Langroid-based application could potentially exploit this flaw. Successful exploitation grants attackers the ability to execute commands with the privileges of the Langroid application process, which could lead to data exfiltration, lateral movement, or complete host takeover.

Root Cause

The root cause is the unsafe use of pandas' eval() function to evaluate expressions derived from user input. The eval() function in pandas, similar to Python's built-in eval(), can execute arbitrary code when given malicious expressions. Without proper input validation and sanitization, attacker-controlled strings can be crafted to break out of the intended expression context and execute system commands.

The LanceDocChatAgent component trusts input passed through compute_from_docs() and forwards it to dataframe operations via QueryPlan.dataframe_calc. This creates a direct injection point where malicious expressions can be executed in the application's runtime environment.

Attack Vector

The attack vector is network-based, requiring no privileges or user interaction. An attacker can exploit this vulnerability by:

1. Identifying a Langroid-based application that uses LanceDocChatAgent
2. Crafting malicious input designed to be processed by compute_from_docs()
3. Injecting Python code expressions through the QueryPlan.dataframe_calc parameter
4. Achieving code execution when pandas eval() processes the malicious payload

The vulnerability allows attackers to inject code such as system command execution payloads, reverse shell connections, or data exfiltration scripts. Since the pandas eval() function can access the Python runtime environment, attackers can import modules, execute system commands via os.system(), or establish persistent access to compromised systems.

For technical implementation details, refer to the GitHub Security Advisory GHSA-22c2-9gwg-mj59.

Detection Methods for CVE-2025-46725

Indicators of Compromise

• Unusual process spawning from Python applications running Langroid
• Unexpected network connections initiated by Langroid-based services
• Anomalous system command execution patterns from web application contexts
• Log entries showing malformed or suspicious query strings containing Python code syntax

Detection Strategies

• Monitor application logs for query inputs containing Python keywords such as import, os.system, subprocess, or exec
• Implement runtime application self-protection (RASP) to detect and block code injection attempts
• Deploy network intrusion detection rules to identify payloads targeting pandas eval() injection
• Use SentinelOne's behavioral AI to detect anomalous code execution patterns from LLM application processes

Monitoring Recommendations

• Enable verbose logging for Langroid applications to capture all input processed by LanceDocChatAgent
• Implement real-time alerting for process creation events spawned by Python/Langroid processes
• Monitor for file system modifications in application directories that could indicate persistence mechanisms
• Track outbound network connections from application servers for potential data exfiltration or C2 communication

How to Mitigate CVE-2025-46725

Immediate Actions Required

• Upgrade Langroid to version 0.53.15 or later immediately
• Audit all Langroid deployments to identify instances using LanceDocChatAgent
• Implement network segmentation to isolate LLM-powered applications from sensitive systems
• Review application logs for potential exploitation attempts prior to patching

Patch Information

Langroid version 0.53.15 addresses this vulnerability by implementing input sanitization for the affected function. The fix targets the most common attack vectors by default and includes additional security warnings in the project documentation. The security patch is available in the GitHub commit 0d9e4a7bb3ae2eef8d38f2e970ff916599a2b2a6.

For complete details on the vulnerability and remediation steps, consult the GitHub Security Advisory GHSA-22c2-9gwg-mj59.

Workarounds

• Disable or remove LanceDocChatAgent functionality if not required for application operation
• Implement strict input validation and allowlisting for all user-supplied data processed by Langroid
• Deploy web application firewalls (WAF) with rules to block Python code injection patterns
• Run Langroid applications in sandboxed environments with minimal system privileges and restricted network access

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

# Verify installed version
pip show langroid | grep Version