LLM Graph Analysis of Cloud RBAC Covert Channels
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Solution Overview
Problem
Conventional cloud access control policies are complex and prone to misconfigurations, leading to covert channels that allow unauthorized access and theft of resources, which are difficult to detect due to the manual interpretation and conversion of access specifications into policies.
Innovation Solution
A method and system using Large Language Models (LLMs) to analyze access control policies, identify violations, and generate a graph highlighting covert channels, enabling automated detection and recommendation of policy changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual interpretation and conversion of access specification into access control policies is performed, then flexibility in policy formulation is improved, but accuracy and reliability of policy configuration deteriorates due to human error and cognitive load
Solution Approach 1:
The patent introduces an automated policy analysis system that acts as an intermediary between the access specification and the final policy configuration. This system uses graph-based modeling to automatically interpret and validate policies, eliminating human error while preserving the flexibility of manual policy formulation. The analyzer serves as a mediator that checks policy correctness without requiring manual intervention in the policy creation process.
Solution Approach 2:
The patent replaces the manual mechanical process of policy interpretation and validation with an automated computational system. Instead of relying on human cognitive processes to interpret access specifications and convert them to policies, the system uses automated graph analysis algorithms to perform this function, significantly improving accuracy while maintaining ease of operation.
2Reliability
If role based access control is implemented to manage cloud resources, then security and control over resources is improved, but complexity of policy formulation increases leading to implicit access violations
Solution Approach 1:
The patent segments the complex policy analysis problem into distinct components: building a graph representation of the policy, identifying access paths, detecting covert channels, and validating against security requirements. This segmentation allows the system to handle role-based access control complexity systematically, maintaining security while reducing the burden of policy formulation through automated analysis.
Solution Approach 2:
The automated analyzer acts as an intermediary that handles the complexity of role-based policy formulation. It automatically interprets the intended access control logic and detects implicit violations, allowing security personnel to formulate policies without directly managing the underlying complexity, thus maintaining security control while reducing formulation complexity.
3Ease of operation
If users are allowed to self-configure access control policies, then ease of operation and adaptability is improved, but security risk increases due to misconfigurations
Solution Approach 1:
The patent implements preliminary validation by automatically analyzing access control policies before they are deployed to the cloud environment. The system detects potential misconfigurations and covert channels in advance, allowing users to self-configure policies with ease while preventing security risks through pre-deployment verification. This preliminary action ensures that misconfigured policies are identified and corrected before they can cause harm.
Solution Approach 2:
The automated policy analyzer provides immediate feedback on policy configurations, alerting users to misconfigurations and security vulnerabilities. This feedback mechanism allows users to self-configure policies while receiving real-time guidance to avoid common errors, thus maintaining ease of operation while reducing security risks through continuous validation.
4Device complexity
If traditional policy analysis methods are used, then simplicity of analysis is maintained, but detection precision of covert channels and violations deteriorates
Solution Approach 1:
The patent transitions from traditional linear policy analysis to a graph-based multi-dimensional representation. By modeling policies as graphs with nodes representing principals, roles, and resources, and edges representing access relationships, the system achieves higher detection precision for covert channels while maintaining analytical simplicity through standardized graph processing algorithms.
Data Source
AI summary
Literature on access control policies in cloud computing has focused on the design of secure models of access or on the syntactic and semantic analysis of policies but not into aspect of covert channel. A method and system for detecting covert channels in role based cloud access control policies using Large Language Model (LLM) is disclosed. A set of access control policies and an access specification is analyzed by the LLM for presence of violation indicating an implicit path between an entity (principal) and a restricted resource. Further, the LLM is prompted to generate a script Python program, which in turn generates a graph to visually depict paths leading from principal to one or more resources in the cloud environment, wherein any implicit access to the restricted resource if detected is highlighted by introducing a path from the principal to the restricted resources with label as covert channel. The covert channel so depicted provides information on possible theft threats.