Pod Artifact Filtering for Vulnerable Function Blocking
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Solution Overview
Problem
Detecting and addressing software vulnerabilities in pods within a cloud computing environment is challenging, especially when unauthorized access attempts are made, and managing necessary fixes and patches across multiple operating systems and runtime environments is cumbersome.
Innovation Solution
A host platform, such as a KUBERNETES® cluster, proactively identifies software vulnerabilities in pods by applying filters to prevent execution of vulnerable functions and offloads requests to secure pods, while downloading patches to fix the vulnerabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If filters are activated to prevent execution of vulnerable functions, then security against exploitation is improved, but system functionality may be disrupted
Solution Approach 1:
The system proactively identifies software vulnerabilities and activates filters before exploitation can occur. The filter is applied in advance to prevent execution of vulnerable functions, and only after a patch is successfully applied is the filter removed to restore full functionality.
Solution Approach 2:
The filter application is dynamic rather than static. The system continuously monitors for vulnerabilities, activates filters when threats are detected, and automatically removes filters after patching. This dynamic approach ensures security when needed while maintaining functionality when safe.
2Reliability
If proactive vulnerability identification and filtering is implemented, then security is improved, but device complexity increases
Solution Approach 1:
The system performs self-service by automatically identifying vulnerabilities, applying appropriate filters, monitoring for security threats, and managing patch deployments without requiring manual intervention. This automation reduces the operational complexity despite the enhanced security capabilities.
Solution Approach 2:
The host platform implements a multi-functional security system that combines vulnerability scanning, filter management, patch deployment, and automated monitoring into a single integrated solution. This universal approach handles multiple security tasks through one system rather than requiring separate tools for each function.
3Reliability
If filters are activated to block vulnerable code execution, then exploitation prevention is improved, but productivity decreases due to function blocking
Solution Approach 1:
The filter is applied locally and specifically to only those functions containing vulnerabilities, rather than blocking all function execution. This targeted approach prevents exploitation of vulnerable code while allowing non-vulnerable functions to continue executing normally, maintaining productivity for unaffected operations.
Solution Approach 2:
The system applies partial action by blocking only the specific vulnerable functions identified through scanning, rather than implementing a blanket block on all functions. This selective filtering minimizes impact on overall system productivity while providing sufficient security coverage for the identified vulnerabilities.
Data Source
AI summary
An example operation may include one or more of determining that a software artifact hosted by a pod within a cluster of a host platform contains code that is a security vulnerability, identifying a function within the software artifact that requests the code, activating a filter within the pod of the host platform which prevents execution of the function within the software artifact, and installing a fix for the security vulnerability within the software artifact.