CVE-2025-52935 is a critical integer overflow or wraparound vulnerability identified in the dragonflydb project, specifically within the dragonfly product versions 1.28.18, 1.30.0, and 1.30.1. The vulnerability is located in the lua_struct.C source file, which is part of the Lua module integration in dragonflydb. Integer overflow (CWE-190) occurs when an arithmetic operation attempts to create a numeric value that is outside the range that can be represented with a given number of bits, causing the value to wrap around to an unintended value. In this context, the flaw likely arises from improper handling of integer values within the Lua scripting module, potentially allowing attackers to manipulate internal data structures or memory management. The CVSS 4.0 score of 9.4 (critical) reflects the high severity of this vulnerability, with an attack vector of network (AV:N), low attack complexity (AC:L), no privileges required (PR:L, indicating low privileges), no user interaction (UI:N), and high impact on confidentiality, integrity, and availability (VC:H, VI:H, VA:H). The vulnerability does not require user interaction and can be exploited remotely, making it highly dangerous. Exploitation could lead to arbitrary code execution, denial of service, or data corruption within systems running vulnerable versions of dragonflydb. Although no known exploits are currently reported in the wild, the critical nature and ease of exploitation suggest that threat actors may develop exploits rapidly. Dragonflydb is a high-performance in-memory data store and cache, similar in function to Redis, and is used in various enterprise and cloud environments for fast data access and caching. The vulnerability in the Lua scripting module could allow attackers to bypass security controls or disrupt critical caching services, impacting dependent applications and services.

For European organizations, the impact of CVE-2025-52935 could be significant, especially for those relying on dragonflydb for caching, session management, or real-time data processing. Exploitation could lead to unauthorized data access, data corruption, or service outages, affecting business continuity and data integrity. Industries such as finance, telecommunications, e-commerce, and public sector entities that use dragonflydb for performance-critical applications may face operational disruptions. The vulnerability’s ability to compromise confidentiality, integrity, and availability simultaneously raises the risk of data breaches, loss of customer trust, and regulatory non-compliance under GDPR. Additionally, the potential for remote exploitation without user interaction increases the likelihood of automated attacks targeting exposed dragonflydb instances. Given the criticality, organizations may also face increased costs related to incident response, forensic investigations, and remediation efforts.

1. Immediate upgrade: Organizations should prioritize upgrading dragonflydb to a patched version once available. Since no patch links are currently provided, monitoring vendor advisories and security mailing lists for updates is essential. 2. Access controls: Restrict network access to dragonflydb instances by implementing strict firewall rules and network segmentation to limit exposure to untrusted networks. 3. Privilege minimization: Run dragonflydb services with the least privileges necessary to reduce the impact of potential exploitation. 4. Monitoring and detection: Deploy anomaly detection and logging focused on Lua scripting module usage and unusual integer operations or crashes that may indicate exploitation attempts. 5. Disable Lua scripting: If feasible, temporarily disable or restrict Lua scripting capabilities in dragonflydb to reduce the attack surface until a patch is applied. 6. Incident response readiness: Prepare and test incident response plans specific to in-memory data store compromises, including data recovery and service restoration procedures. 7. Vendor engagement: Engage with dragonflydb maintainers or community to obtain early patches or mitigations and share threat intelligence relevant to this vulnerability.