CVE-2025-61677 is a security vulnerability classified under CWE-502, which involves the deserialization of untrusted data within the DataChain product developed by iterative. DataChain is a Python-based AI data warehouse designed for transforming and analyzing unstructured data. Versions prior to 0.34.2 (specifically 0.34.1 and below) are affected. The vulnerability arises because the DataChain library reads serialized objects from environment variables such as DATACHAIN__METASTORE and DATACHAIN__WAREHOUSE in its loader.py module. If an attacker can manipulate these environment variables, they can supply malicious serialized data that, when deserialized by the application, can lead to arbitrary code execution. This is a classic deserialization flaw where untrusted input is processed without sufficient validation or sanitization, allowing an attacker to execute code during the loading phase of the application. The vulnerability requires the attacker to have the ability to set environment variables, which typically implies some level of access to the host environment or the deployment pipeline. The issue is resolved in DataChain version 0.34.2, where presumably safer deserialization practices or input validation have been implemented. The CVSS v3.1 score assigned is 2.5, indicating a low severity primarily because the attack vector is local (AV:L), requires high attack complexity (AC:H), low privileges (PR:L), and no user interaction (UI:N). The impact is limited to integrity (I:L) with no confidentiality or availability impact. No known exploits are currently reported in the wild.

For European organizations utilizing DataChain versions below 0.34.2, this vulnerability poses a risk of unauthorized code execution if an attacker can influence environment variables on the host system. This could lead to unauthorized modification of data processing workflows or injection of malicious logic into AI data transformations, potentially corrupting analytical outputs or introducing backdoors. However, the requirement for local or privileged access to set environment variables limits the risk to insider threats, compromised deployment environments, or misconfigured systems. Given the AI and data analytics focus of DataChain, organizations in sectors such as finance, healthcare, and research that rely on unstructured data analysis could face integrity risks in their data pipelines. While confidentiality and availability impacts are minimal, the integrity compromise could undermine trust in AI-driven decision-making processes. The low CVSS score reflects these constraints, but organizations should still consider the risk in the context of their deployment environments and threat models.

European organizations should upgrade DataChain installations to version 0.34.2 or later immediately to eliminate this vulnerability. Beyond patching, organizations should implement strict environment variable management policies, ensuring that only trusted administrators or automated deployment systems can set or modify environment variables related to DataChain. Employ runtime environment hardening techniques such as containerization with immutable infrastructure and restricted environment variable injection. Additionally, conduct regular audits of deployment pipelines and host configurations to detect unauthorized environment variable changes. Implement application-level input validation and consider using safer serialization formats or libraries that enforce strict deserialization controls. Monitoring for anomalous process behavior during DataChain startup can also help detect exploitation attempts. Finally, restrict access to systems running DataChain to trusted personnel and enforce the principle of least privilege to minimize the risk of environment variable tampering.