In security, “Living off the Land” (LotL or LOTL)-type attacks are not new. Bad actors have been using legitimate software and functions to target systems and carry out malicious attacks for many years. And although it’s not novel, LotL is still one of the preferred approaches even for highly skilled attackers. Why? Because hackers tend not to reinvent the wheel and prefer to keep a low profile, i.e., leave no “footprints,” such as random binaries or scripts on the system. Interestingly, these stealthy moves are exactly why it’s often very difficult to determine which of these actions are a valid system administrator and which are an attacker. It’s also why static rules can trigger so many false positives and why compromises can go undetected.
Most antivirus vendors do not treat executed commands (from a syntax and vocabulary perspective) as an attack vector, and most of the log-based alerts are static, limited in scope, and hard to update. Furthermore, classic LotL detection mechanisms are noisy and somewhat unreliable, generating a high number of false positives, and because typical rules grow organically, it becomes easier to retire and rewrite the rules rather than maintain and update them.
The security intelligence team at Adobe set out to help fix this problem. Using open source and other representative incident data, we developed a dynamic and high-confidence program, called LotL Classifier, and we have recently open sourced it to the broader security community. In this webcast you will learn about the LotL Classifer, intended usage and best practices, and how you can obtain and contribute to the project.