CVE-2025-46725 is a high-severity code injection vulnerability affecting versions of the langroid Python framework prior to 0.53.15. Langroid is designed to facilitate building large language model (LLM)-powered applications. The vulnerability arises from the use of pandas' eval() function within the LanceDocChatAgent component, specifically in the compute_from_docs() method. This method evaluates expressions on dataframes using pandas eval(), which can execute arbitrary code if the input is not properly sanitized. An attacker can exploit this by crafting malicious input to the QueryPlan.dataframe_calc parameter, causing the agent to execute arbitrary commands on the host system. This leads to potential full system compromise, including unauthorized code execution, data theft, or disruption of services. The vulnerability is classified under CWE-94 (Improper Control of Generation of Code), indicating that the application fails to properly control or sanitize dynamically generated code. Langroid version 0.53.15 addresses this issue by sanitizing inputs to the affected function by default and adding warnings in the documentation about the risks of using eval(). No known exploits are currently reported in the wild, but the vulnerability's nature and high CVSS score (8.1) indicate a significant risk if left unpatched. The CVSS vector indicates that the vulnerability is remotely exploitable without authentication or user interaction, with high impact on confidentiality, integrity, and availability, and no scope change.

For European organizations leveraging langroid framework versions prior to 0.53.15 in their LLM-powered applications, this vulnerability poses a severe risk. Successful exploitation could allow attackers to execute arbitrary code remotely, leading to full system compromise. This could result in data breaches, intellectual property theft, disruption of critical AI services, and potential lateral movement within enterprise networks. Given the increasing adoption of AI and LLM-based solutions in sectors such as finance, healthcare, and government across Europe, the impact could be substantial. Compromise of AI application infrastructure could undermine trust in AI-driven decision-making processes and expose sensitive personal or corporate data. Additionally, disruption or manipulation of AI outputs could have downstream effects on automated workflows and customer-facing services. The lack of required authentication and user interaction makes this vulnerability particularly dangerous in exposed environments, increasing the likelihood of exploitation if vulnerable versions are accessible over the network.

European organizations should immediately audit their use of the langroid framework and identify any deployments running versions prior to 0.53.15. Upgrading to langroid 0.53.15 or later is the primary and most effective mitigation step, as it includes input sanitization and security warnings. If immediate upgrade is not feasible, organizations should implement strict input validation and sanitization on any user-controlled data passed to the LanceDocChatAgent, particularly the QueryPlan.dataframe_calc parameter. Network-level protections such as firewall rules and segmentation should restrict access to systems running vulnerable langroid versions, limiting exposure to untrusted networks. Monitoring and logging of unusual eval() usage or unexpected command execution attempts within the application can help detect exploitation attempts. Additionally, organizations should review and harden the security posture of hosts running langroid, including applying OS-level security controls, restricting execution privileges, and employing endpoint detection and response (EDR) solutions to detect anomalous behaviors. Finally, educating developers and operators about the risks of dynamic code execution and the importance of secure coding practices in AI frameworks is recommended to prevent similar issues.