Attack Surface: Untrusted Code Execution
- Description: Execution of arbitrary, malicious code within the Flink cluster (JobManager or TaskManagers) through job submission.
- How Flink Contributes: Flink's core purpose is to execute user-provided code (JARs). This inherent functionality is the primary attack vector. Flink's distributed execution model amplifies the impact.
- Example: An attacker submits a JAR containing a reverse shell, providing remote control over a TaskManager. A JAR uses reflection to bypass security restrictions and access system resources or exfiltrate data.
- Impact: Complete cluster compromise, data exfiltration, data destruction, lateral movement within the network, potential for complete control of connected systems.
- Risk Severity: Critical
- Mitigation Strategies:
- Strict Code Review & Signing (Mandatory): Rigorous, mandatory code review for all submitted JARs. Digitally sign JARs and enforce signature verification before execution. Maintain a whitelist of approved code/developers.
- Authentication & Authorization (Job Submission - Mandatory): Implement strong authentication (e.g., Kerberos, multi-factor authentication) and granular authorization to strictly control who can submit jobs. Integrate with external identity providers.
- Resource Quotas: Enforce strict resource limits (CPU, memory, network I/O) per job and per user/tenant to limit the damage a malicious job can inflict.
- Limited Sandboxing: Utilize Java Security Manager (despite deprecation, it provides some protection) and containerization (Docker) to isolate TaskManagers. Note: Perfect sandboxing of Java is extremely difficult.
- Input Validation (within UDFs): If User-Defined Functions (UDFs) accept external input, rigorously validate that input within the UDF code to prevent injection attacks inside the UDF.
Attack Surface: Resource Exhaustion (Denial of Service)
- Description: A malicious or poorly written job consumes excessive Flink cluster resources, preventing legitimate jobs from running or crashing the cluster.
- How Flink Contributes: Flink's distributed nature and ability to process large datasets, combined with its resource management, make it susceptible to resource exhaustion if not properly configured and monitored.
- Example: A job with an infinite loop or a job that allocates massive amounts of memory without releasing it. A job that opens too many network connections, exhausting Flink's connection pool.
- Impact: Denial of service for legitimate users, cluster instability, potential data loss.
- Risk Severity: High
- Mitigation Strategies:
- Strict Resource Quotas (Per Job/User - Mandatory): Enforce hard limits on CPU, memory, network bandwidth, and disk I/O for each job and each user/tenant.
- Job Monitoring & Alerting (Proactive): Implement real-time monitoring of resource usage within Flink. Set up alerts to trigger when jobs exceed predefined thresholds. Automate job termination if necessary.
- Backpressure Handling: Ensure the application and Flink configuration are correctly handling backpressure to prevent cascading failures due to resource constraints.
Attack Surface: JobManager RPC Endpoint Exposure
- Description: Unprotected JobManager RPC endpoints allowing unauthorized job submission, cluster control, or data exfiltration.
- How Flink Contributes: The JobManager's RPC interface is the primary control plane for Flink. Exposure of this interface is a direct and critical Flink-related risk.
- Example: An attacker discovering an exposed JobManager port and submitting a malicious job. An attacker using the RPC interface to query cluster status and extract sensitive information, or to terminate running jobs.
- Impact: Complete cluster compromise, data exfiltration, denial of service, full control over Flink's operations.
- Risk Severity: Critical
- Mitigation Strategies:
- Network Segmentation (Critical): Isolate the Flink cluster on a private network, preventing direct external access to the JobManager's RPC ports.
- Authentication & Authorization (JobManager - Mandatory): Enable and strictly enforce Flink's built-in authentication and authorization mechanisms (Kerberos, custom plugins).
- TLS Encryption (JobManager Communication - Mandatory): Use TLS to encrypt all communication with the JobManager's RPC endpoints. Configure strong cipher suites.
- Firewall Rules: Implement strict firewall rules to allow access to the JobManager's ports only from authorized clients (e.g., specific IP addresses or networks).
Attack Surface: State Backend Vulnerabilities
- Description: Exploiting vulnerabilities or misconfigurations in the state backend (e.g., RocksDB, HDFS) to access or modify application state.
- How Flink Contributes: Flink relies on state backends to manage application state. The security of the chosen backend, and how Flink interacts with it, is a direct concern.
- Example: An attacker gaining access to the RocksDB files on disk and extracting sensitive state data. An attacker exploiting a vulnerability in HDFS to modify the state stored there, corrupting application data.
- Impact: Data breaches, data corruption, application manipulation, potential for complete application compromise.
- Risk Severity: High
- Mitigation Strategies:
- Encryption at Rest (State Backend): If using a persistent state backend (like RocksDB), enable encryption at rest to protect the state data on disk.
- Access Control (Distributed State Backends): If using a distributed state backend (e.g., HDFS), ensure proper access controls (e.g., HDFS permissions, Kerberos) are in place to prevent unauthorized access. This is managed through Flink's configuration, making it a Flink-related concern.
- Network Segmentation: Isolate the state backend storage (e.g., the HDFS cluster) from unauthorized network access.
- Regular Updates (State Backend): Keep the state backend software (e.g., RocksDB, HDFS) up-to-date with the latest security patches. Flink's configuration determines which backend is used, making this a Flink-specific concern.