In the first part of our exploration into actor anomalies, we delved into the critical role these unique identifiers play in enhancing cybersecurity measures, particularly in detecting and mitigating Account Takeover (ATO) attacks. As our journey continues, we now turn our attention to two other critical attack vectors: business logic attacks and data exfiltration attacks. In this blog, we will explore new types of actors: application actors and actors from HTTP responses to understand how anomalies in these actors can help identify and neutralize these threats. Specifically, we will see how application actors can aid in detecting business logic attacks (BLA), while actors from HTTP responses can be instrumental in identifying data exfiltration attempts.
Understanding Business Logic Attacks
Business logic attacks exploit the inherent functionality of an application to achieve malicious goals. These attacks do not rely on exploiting vulnerabilities in the software code but rather manipulate legitimate processes to cause harm. Examples include abusing discount codes to gain unauthorized discounts, bypassing payment processes, or manipulating user permissions.
Identifying Application Actors in Business Logic Attacks
In business logic attacks, the actors are typically parameters in HTTP requests that represent various entities within the application. Common examples include:
- User ID: Uniquely identifies a user interacting with the application.
- Order ID: Represents a specific transaction or purchase order.
- Product ID: Identifies a particular product within an application.
By monitoring these actors and detecting anomalies in their behavior, security systems can identify potential business logic attacks.
Analyzing Application Actors' Behavior
To detect business logic attacks, it is essential to analyze the behavior of application actors. Key factors to consider include:
- Rate Correlation: Monitoring the frequency and rate at which specific actors interact with the application can reveal suspicious patterns. For example, a sudden spike in order placements from a single user ID or multiple order IDs being manipulated in quick succession could indicate an attack.
- Functional Authorization: Ensuring that actors are performing actions within their authorized scope is critical. For instance, if a regular user ID suddenly gains administrative privileges or accesses restricted areas of the application, it signals a potential breach.
Generating Patterns and Signatures Based on Actor Behavior
To effectively detect business logic attacks, it is necessary to generate patterns and signatures based on actor behavior. By correlating the activities of multiple actors, security systems can identify deviations from expected behavior. For example, if a user ID frequently interacts with specific product IDs and order IDs in a manner inconsistent with normal usage, it could indicate malicious intent.
Case Study on Mitigation Based on Application Actor Anomalies
Consider a scenario where an attacker attempts to exploit a business logic vulnerability to gain unauthorized discounts. The attacker identifies that manipulating the product ID in the order submission process results in discounted prices. By monitoring the following actors, the security system can detect and mitigate this attack:
- User ID: The attacker uses a single user ID to place multiple orders with manipulated product IDs.
- Product ID: The product IDs in the orders are altered to gain unauthorized discounts.
- Order ID: The order IDs associated with the manipulated product IDs exhibit abnormal patterns.
By analyzing the rate of order placements, functional authorization, and correlation between user ID, product ID, and order ID, the security system can flag the suspicious behavior and block the malicious transactions before they cause significant damage.
Understanding Data Exfiltration Attacks
Data exfiltration attacks involve the unauthorized transfer of sensitive data from an organization’s systems. These attacks can result in severe data breaches, leading to financial loss, reputational damage, and regulatory penalties. Identifying actor anomalies within HTTP responses is crucial for detecting and mitigating data exfiltration attempts.
Identifying Data Exfiltration Attack Actors
In data exfiltration attacks, the actors are often parameters within HTTP responses that contain sensitive information. Common examples include:
- User ID: Uniquely identifies a user interacting with the application.
- Source IP Address: Identifies the location from which the user is accessing the system.
- Personally Identifiable Information (PII): Data such as credit card numbers, names, addresses, social security numbers, and other personal details.
Analyzing Actors' Behavior in Data Exfiltration Attacks
To detect data exfiltration attempts, it is essential to analyze the behavior of actors within HTTP responses. Key factors to consider include:
- Unusual Volume of Data: Monitoring the volume of sensitive data being transmitted can reveal suspicious activity. For instance, a sudden surge in the amount of PII, like credit card numbers, being transferred indicates potential data exfiltration.
- Abnormal Access Patterns: Identifying unusual access patterns, such as repeated requests for the same sensitive data within a short time frame, can signal an attack.
- Multiple Actor Correlation: Correlating the activities of multiple actors, such as User IDs, Source IP Addresses, and PII, can provide insights into potential exfiltration attempts.
Generating Patterns and Signatures Based on Actor Behavior
To effectively detect data exfiltration attacks, it is necessary to generate patterns and signatures based on actor behavior. By correlating the activities of multiple actors, security systems can identify deviations from expected behavior. For example, if a specific user ID frequently accesses and transfers large volumes of credit card numbers from an unusual Source IP Address, it could indicate data exfiltration.
Case Study on Mitigation Based on Actor Anomalies Using Rate Correlation Strategy
Consider a scenario where an attacker attempts to exfiltrate sensitive customer data from an application. The attacker identifies a vulnerability that allows them to access and transfer credit card numbers using a compromised user account. By monitoring the following actors, the security system can detect and mitigate this attack:
- User ID: The compromised user ID is used to access and transfer large volumes of credit card numbers.
- Source IP Address: The attacker accesses the system from an unusual or foreign IP address not typically associated with the legitimate user.
- PII (Credit Card Numbers): The HTTP responses contain a large volume of credit card numbers being accessed and transferred.
By analyzing the volume of data being transferred, abnormal access patterns, and correlation between the User ID, Source IP Address, and PII, the security system can flag the suspicious behavior and block the data exfiltration attempts before significant damage occurs.
Conclusion
In conclusion, understanding and leveraging actor anomalies is crucial for detecting and mitigating business logic attacks and data exfiltration attempts. By identifying and analyzing the behavior of application actors and parameters within HTTP responses, security systems can detect deviations from expected patterns and proactively respond to threats. As the threat landscape continues to evolve, it is essential to continually refine and innovate detection mechanisms to stay ahead of adversaries and protect sensitive data.
By implementing these strategies and focusing on actor anomalies, organizations can enhance their cybersecurity posture and safeguard against sophisticated attacks targeting business logic and sensitive data.
Your digital assets deserve the best defense – stay informed, stay safe, and contact us. Radware products offer multiple techniques for anomaly detection and mitigation. Learn more about Radware’s Cloud WAAP services and application protection for Kubernetes.