DLL hijacking is a sophisticated attack technique targeting Windows systems, where attackers replace or sideload malicious DLL files that are then executed by legitimate processes. This allows malware to run with the privileges of trusted applications, bypassing many traditional endpoint security solutions that rely on detecting unknown or suspicious executables. Variants include DLL sideloading with distributed malicious libraries, replacing existing DLLs on the system, or manipulating system search order mechanisms to load attacker-controlled DLLs. Detecting such attacks is challenging because the malicious DLLs run within legitimate processes, making their activity appear normal. To address this, Kaspersky’s AI Technology Research Center developed a machine learning model trained on extensive telemetry data from the Kaspersky Security Network and internal analysis systems. The model uses indirect indicators such as whether DLLs and executables are located in standard paths, if files have been renamed, changes in DLL size or structure, and the integrity of digital signatures. After multiple iterations and refinements, the model achieves high accuracy in detecting DLL hijacking attempts. Integrated into the Kaspersky Unified Monitoring and Analysis Platform (SIEM), the model analyzes DLL load events and correlates findings with cloud-based reputation data to reduce false positives. It can operate in two modes: analyzing only events triggered by correlation rules for faster alerts, or processing all relevant DLL load events for comprehensive retrospective threat hunting. This approach enhances early detection of targeted attacks leveraging DLL hijacking, which are increasingly common in sophisticated threat campaigns. The model’s accuracy is expected to improve over time as more data is collected and algorithms evolve. Although no direct exploits are currently known in the wild for this specific detection technology, the underlying DLL hijacking technique remains a medium-severity threat due to its stealth and potential for privilege escalation.

For European organizations, DLL hijacking poses a significant risk as it enables attackers to execute malicious code stealthily within trusted processes, potentially leading to unauthorized access, data exfiltration, or lateral movement within networks. Critical infrastructure, financial institutions, and large enterprises running Windows environments are particularly vulnerable due to their reliance on legacy applications and complex software ecosystems where DLL hijacking can be exploited. The difficulty in detecting these attacks with traditional endpoint protection increases the risk of prolonged undetected breaches. The integration of Kaspersky’s ML-based detection into SIEM platforms enhances visibility and response capabilities, reducing dwell time and limiting potential damage. However, organizations not using advanced detection tools or relying solely on signature-based defenses remain exposed. The threat also complicates compliance with European data protection regulations (e.g., GDPR) if breaches occur undetected. Additionally, the stealthy nature of DLL hijacking can facilitate supply chain attacks or targeted espionage campaigns, which are of strategic concern in the European geopolitical context. Overall, the impact includes potential confidentiality breaches, integrity compromises, and operational disruptions, though availability impact is generally limited unless combined with other attack vectors.

European organizations should adopt a multi-layered defense strategy that includes deploying advanced detection solutions like Kaspersky’s Unified Monitoring and Analysis Platform with the integrated ML model for DLL hijacking detection. Regularly audit and enforce strict DLL integrity and digital signature verification policies to prevent unauthorized modifications or replacements. Implement application whitelisting and restrict execution privileges to minimize the risk of malicious DLL execution. Conduct frequent threat hunting exercises focusing on DLL load events and anomalous process behaviors using enriched telemetry data. Maintain up-to-date software and patch known vulnerabilities that could facilitate DLL hijacking. Educate security teams on the nuances of DLL hijacking techniques and the importance of monitoring indirect indicators such as unusual DLL paths or renamed files. Integrate SIEM alerts with incident response workflows to enable rapid investigation and containment. Finally, collaborate with threat intelligence providers to stay informed about emerging DLL hijacking tactics and update detection models accordingly.