What Is Lateral Movement?
Lateral movement refers to the technique used by an attacker to spread within a compromised network to gain access to other systems and sensitive information. This can be achieved through techniques such as remote execution, privileged account escalation, and network-level exploitation. The goal of lateral movement is to maintain a persistent presence within the network and to eventually achieve the attacker’s objectives.
This is part of a series of articles about cloud attacks.
In this article:
Lateral Movement in Cyber Attacks: Steps and Techniques
A cyber attack that makes use of lateral movement typically follows these steps:
- Reconnaissance: The attacker collects information about the network and identifies potential targets, such as systems with elevated privileges or sensitive data.
- Initial compromise: The attacker gains initial access to a single system within the target network, often through techniques such as phishing, exploiting vulnerabilities, or password cracking.
- Lateral movement: The attacker uses the initial access to move from the compromised system to other systems within the network. This can be achieved through techniques such as pass-the-hash, remote execution, or privilege escalation.
- Establishing persistence: The attacker seeks to maintain access to the compromised systems, often by installing backdoors, modifying system configurations, or planting malicious software.
- Achieving objectives: The attacker seeks to achieve their goals, such as stealing sensitive information, disrupting operations, or installing ransomware.
Lateral movement attacks can be complex and sophisticated, often involving multiple techniques. Some common lateral movement techniques include:
- Pass-the-hash: This technique involves stealing and reusing a user’s hashed credentials to gain access to other systems on the network.
- Remote execution: Attackers can use remote execution tools such as PowerShell or Remote Desktop Protocol (RDP) to run commands on other systems within the network.
- Privilege escalation: Attackers can exploit vulnerabilities in software or the operating system to elevate their privileges and gain access to sensitive information.
- Kerberoasting: This technique involves stealing Kerberos tickets, which can be used to access other systems within the network.
- ‘Living off the land’: This involves using built-in system tools and applications, such as Windows Management Instrumentation (WMI) or Windows Task Scheduler, to move laterally within the network.
- Phishing: Attackers can use phishing emails to trick users into downloading malware or giving up their credentials, which can then be used to move laterally within the network.
Detecting Lateral Movement
Real-Time Monitoring and Alerting
Real-time monitoring and alerting involve continuously monitoring the activity on a network and triggering alerts when specific security events occur. The goal is to detect potential security incidents as soon as they occur, allowing the organization to quickly respond and prevent further damage.
Real-time monitoring is typically achieved through the use of security information and event management (SIEM) systems, intrusion detection systems (IDS), and network flow analysis tools. These systems collect data from various sources, such as network traffic, logs, and user activity, and analyze it in real-time to identify potential security incidents.
The alerting component of real-time monitoring and alerting is designed to notify security teams when specific security events occur. For example, an alert may be triggered when a user logs in from an unusual location, when a high volume of network traffic is detected, or when a user attempts to access sensitive data. The alert provides security personnel with the information they need to assess the situation and determine whether it represents a security incident.
Behavioral analytics is a method of detecting anomalies in user and system behavior, which can be used to identify potential security incidents, including lateral movement attacks. This involves collecting and analyzing data from various sources, such as network traffic, logs, and user activity, to identify patterns of behavior that are typical for a given environment. By continuously monitoring the behavior of users and systems, behavioral analytics can identify deviations from normal behavior that may indicate a security incident.
User and entity behavior analysis (UEBA) solutions are a type of behavioral analytics technology that specifically focuses on analyzing user behavior. UEBA solutions collect data on user behavior, such as login patterns, file access, and network activity, and use machine learning algorithms to identify patterns of behavior that are typical for a given user or group of users. UEBA solutions can then detect deviations from normal behavior that may indicate a security incident, such as a user accessing sensitive data outside of normal working hours.
UEBA solutions are an effective tool for detecting lateral movement attacks, as they can identify suspicious behavior that may indicate an attacker moving laterally within the network. For example, UEBA solutions can detect when a user logs into systems from unusual locations, when a user accesses sensitive data that is outside of their normal responsibilities, or when a user creates new accounts or permissions.
Preventing Lateral Movement
Preventing lateral movement attacks is a critical component of an effective cybersecurity strategy. There are several methods organizations can use to proactively prevent lateral movement, including:
Proactively Hunt for Advanced Threats
Proactively hunting for advanced threats involves actively searching for indicators of compromise (IOCs) that may indicate a security incident has occurred. This can be achieved through the use of threat intelligence feeds, which provide information about the latest threats and vulnerabilities, and through security testing, such as penetration testing and red teaming exercises, which can identify weaknesses in an organization’s security posture.
Implement Network Microsegmentation
Network microsegmentation is a security technique that involves dividing a network into smaller, isolated segments, with each endpoint (such as a server, desktop, or mobile device) being treated as a separate microsegment. This creates a security perimeter around each endpoint and limits the lateral movement of threats within the network.
Multi-factor Authentication (MFA)
Multi-factor authentication (MFA) can help prevent lateral movement by making it more difficult for attackers to steal and use legitimate credentials to move laterally within a network. MFA requires users to provide more than one form of authentication to gain access to a system or network. This can include a combination of something the user knows (such as a password), something the user has (such as a security token), or something the user is (such as a fingerprint).
By requiring multiple forms of authentication, MFA makes it much harder for attackers to successfully use stolen or compromised credentials to move laterally. Even if an attacker is able to steal a user’s password, they will still need to provide other forms of authentication in order to gain access.
In addition, MFA can detect if an attacker is trying to use a stolen or compromised credential. For example, if an attacker uses a stolen password to try to log in from an unfamiliar location or device, MFA might detect this and prompt the user to provide an additional form of authentication before allowing access.
Extended Detection and Response (XDR)
XDR is a security solution that integrates multiple security technologies and provides a single platform for security monitoring, detection, and response. XDR solutions can be used to detect and prevent lateral movement attacks by providing a comprehensive view of the network, identifying security incidents, and automating the response process. XDR solutions can also be used to integrate threat intelligence feeds and incident response plans, providing organizations with a more comprehensive security posture.
How to Protect Against Lateral Movement with Aqua
To prevent lateral movement in your cloud native environment, you need to follow security best practices and minimize your attack surface. One of the approaches is to implement identity-based segmentation of your network. This will allow you to discover, visualize, and define network connections in cloud native environments and detect any malicious network activity across VMs, containers, Kubernetes clusters, and pods.
With the unified Aqua Cloud Native Security Platform, you can:
- Detect and prevent unauthorized network connections such as open ports (on the same or across hosts and pods) based on automated policies.
- Define zero-trust network connections based on service-oriented firewall rules, regardless of where the workload runs.
- Automatically alert on or block unauthorized communication flows with no container downtime.