Adversaries may attempt to dump credentials to obtain account login and credential material, normally in the form of a hash or a clear text password. Credentials can be obtained from OS caches, memory, or structures.(Citation: Brining MimiKatz to Unix) Credentials can then be used to perform [Lateral Movement](https://attack.mitre.org/tactics/TA0008) and access restricted information. Several of the tools mentioned in associated sub-techniques may be used by both adversaries and professional security testers. Additional custom tools likely exist as well.
Protect sensitive information at rest, in transit, and during processing by using strong encryption algorithms. Encryption ensures the confidentiality and integrity of data, preventing unauthorized access or tampering. This mitigation can be implemented through the following measures: Encrypt Data at Rest: - Use Case: Use full-disk encryption or file-level encryption to secure sensitive data stored on devices. - Implementation: Implement BitLocker for Windows systems or FileVault for macOS devices to encrypt hard drives. Encrypt Data in Transit: - Use Case: Use secure communication protocols (e.g., TLS, HTTPS) to encrypt sensitive data as it travels over networks. - Implementation: Enable HTTPS for all web applications and configure mail servers to enforce STARTTLS for email encryption. Encrypt Backups: - Use Case: Ensure that backup data is encrypted both during storage and transfer to prevent unauthorized access. - Implementation: Encrypt cloud backups using AES-256 before uploading them to Amazon S3 or Google Cloud. Encrypt Application Secrets: - Use Case: Store sensitive credentials, API keys, and configuration files in encrypted vaults. - Implementation: Use HashiCorp Vault or AWS Secrets Manager to manage and encrypt secrets. Database Encryption: - Use Case: Enable Transparent Data Encryption (TDE) or column-level encryption in database management systems. - Implementation: Use MySQL’s built-in encryption features to encrypt sensitive database fields such as social security numbers.
Behavior Prevention on Endpoint refers to the use of technologies and strategies to detect and block potentially malicious activities by analyzing the behavior of processes, files, API calls, and other endpoint events. Rather than relying solely on known signatures, this approach leverages heuristics, machine learning, and real-time monitoring to identify anomalous patterns indicative of an attack. This mitigation can be implemented through the following measures: Suspicious Process Behavior: - Implementation: Use Endpoint Detection and Response (EDR) tools to monitor and block processes exhibiting unusual behavior, such as privilege escalation attempts. - Use Case: An attacker uses a known vulnerability to spawn a privileged process from a user-level application. The endpoint tool detects the abnormal parent-child process relationship and blocks the action. Unauthorized File Access: - Implementation: Leverage Data Loss Prevention (DLP) or endpoint tools to block processes attempting to access sensitive files without proper authorization. - Use Case: A process tries to read or modify a sensitive file located in a restricted directory, such as /etc/shadow on Linux or the SAM registry hive on Windows. The endpoint tool identifies this anomalous behavior and prevents it. Abnormal API Calls: - Implementation: Implement runtime analysis tools to monitor API calls and block those associated with malicious activities. - Use Case: A process dynamically injects itself into another process to hijack its execution. The endpoint detects the abnormal use of APIs like `OpenProcess` and `WriteProcessMemory` and terminates the offending process. Exploit Prevention: - Implementation: Use behavioral exploit prevention tools to detect and block exploits attempting to gain unauthorized access. - Use Case: A buffer overflow exploit is launched against a vulnerable application. The endpoint detects the anomalous memory write operation and halts the process.
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