CVE-2024-7042 is an SQL injection vulnerability identified in the GraphCypherQAChain class of the langchain-ai/langchainjs library, specifically version 0.2.5 and other versions containing this class. The vulnerability stems from improper neutralization of special elements used in SQL commands (CWE-89), allowing attackers to inject malicious SQL code via prompt injection techniques. This can lead to unauthorized creation, modification, or deletion of nodes and relationships within the underlying graph database. The flaw enables attackers to exfiltrate sensitive data, disrupt service availability by deleting all data (denial of service), and compromise data integrity. Additionally, in multi-tenant environments, attackers could cross tenant boundaries, accessing or manipulating data belonging to other tenants, thus breaching isolation guarantees. The CVSS 3.0 score is 4.9 (medium), reflecting a local attack vector with high attack complexity, no privileges required, and no user interaction needed. The vulnerability impacts confidentiality, integrity, and availability but requires local access or a trusted environment to exploit. No patches or known exploits are currently reported, but the risk remains significant for applications relying on this library for database interactions.

For European organizations, the impact of CVE-2024-7042 can be substantial, especially for those deploying langchain-ai/langchainjs in production environments handling sensitive or regulated data. Confidentiality breaches could expose personal data protected under GDPR, leading to legal and financial repercussions. Integrity violations could corrupt critical datasets, undermining trust in AI-driven decision-making systems. Availability impacts from data deletion could disrupt business operations, causing downtime and loss of productivity. Multi-tenant cloud service providers or SaaS platforms using this library risk cross-tenant data leakage, severely damaging customer trust and incurring compliance violations. Given the growing adoption of AI frameworks and graph databases in sectors like finance, healthcare, and public administration across Europe, the vulnerability poses a risk to critical infrastructure and sensitive data processing. Organizations lacking robust input validation or isolation controls are particularly vulnerable.

To mitigate CVE-2024-7042, organizations should first monitor for official patches or updates from langchain-ai and apply them promptly once available. In the absence of patches, developers must implement strict input validation and sanitization to prevent injection of malicious payloads into SQL queries. Employing parameterized queries or prepared statements within the GraphCypherQAChain class can effectively neutralize special characters and prevent SQL injection. Conduct thorough code reviews focusing on all user-controllable inputs reaching database query layers. For multi-tenant environments, enforce strict tenant isolation and access controls to limit the blast radius of any potential exploit. Additionally, implement runtime monitoring and anomaly detection to identify unusual database activities indicative of exploitation attempts. Regularly audit and back up databases to enable recovery from potential data deletion attacks. Finally, educate developers and security teams about prompt injection risks in AI frameworks to foster secure coding practices.