DLL hijacking is a technique used by attackers to execute malicious code by tricking legitimate Windows processes into loading malicious dynamic-link libraries (DLLs) instead of the intended legitimate ones. This can occur through DLL sideloading, replacing existing DLLs, or manipulating system search order mechanisms. Because the malicious DLL runs within the context and privileges of a trusted process, traditional endpoint protection solutions often fail to detect such activity. To address this challenge, Kaspersky's AI Technology Research Center developed a machine learning model trained on extensive telemetry data and file reputation databases to detect subtle indicators of DLL hijacking. Key features analyzed include whether executables and DLLs reside in standard paths, file renaming, changes in size and structure, and digital signature integrity. The model underwent multiple refinement iterations to improve accuracy and reduce false positives. Integrated into the Kaspersky Unified Monitoring and Analysis Platform SIEM, the model analyzes DLL load events and correlates findings with cloud-based threat intelligence from Kaspersky Security Network (KSN). It functions in two modes: analyzing only events triggering correlation rules for efficient real-time detection, and processing all relevant DLL load events for comprehensive retrospective threat hunting. This approach enhances early detection of targeted attacks leveraging DLL hijacking, a technique increasingly used by threat actors to evade detection and maintain persistence. While no active exploits are currently reported, the threat remains significant due to the stealth and privilege escalation potential inherent in DLL hijacking.

For European organizations, DLL hijacking poses a medium-level risk primarily in Windows-based environments where attackers can gain unauthorized code execution under the guise of trusted processes. This can lead to privilege escalation, persistence within networks, and potential lateral movement, thereby compromising confidentiality, integrity, and availability of critical systems. The stealthy nature of DLL hijacking means traditional endpoint protection may not detect these attacks, increasing the risk of prolonged undetected breaches. Organizations relying on legacy software or lacking advanced detection capabilities are particularly vulnerable. The integration of ML-based detection in SIEM platforms like Kaspersky's enhances visibility and response capabilities, reducing dwell time and mitigating impact. However, failure to detect such attacks can result in data breaches, operational disruptions, and regulatory non-compliance, especially under stringent European data protection laws such as GDPR. The threat is more pronounced in sectors with high-value targets, including finance, government, and critical infrastructure, where attackers seek persistent footholds.

1. Deploy advanced detection solutions incorporating machine learning models capable of identifying anomalous DLL loading behaviors, such as the Kaspersky Unified Monitoring and Analysis Platform with the integrated DLL hijacking detection model. 2. Enforce strict application whitelisting and DLL integrity verification policies to prevent unauthorized DLL replacements or sideloading. 3. Regularly audit and monitor DLL load paths and process behaviors to identify deviations from baseline norms. 4. Implement robust endpoint detection and response (EDR) tools that can correlate DLL load events with process metadata and threat intelligence. 5. Maintain up-to-date software and patch known vulnerabilities that could facilitate DLL hijacking. 6. Conduct periodic threat hunting exercises focusing on DLL-related anomalies using retrospective analysis capabilities of SIEM systems. 7. Educate security teams on DLL hijacking techniques and indicators to improve incident response readiness. 8. Limit privileges of processes and users to reduce the impact of potential DLL hijacking exploitation. 9. Integrate cloud-based threat intelligence feeds to enhance detection accuracy and reduce false positives. 10. Establish incident response playbooks specifically addressing DLL hijacking scenarios to ensure swift containment and remediation.