LLM Attack Graph Construction for Complex Vulnerability Analysis
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Solution Overview
Problem
Constructing accurate attack graphs for computer security is difficult due to the complexity of modern computing systems, and existing methods often rely on static rules or heuristics that are not scalable and do not leverage advanced natural language processing features, leading to suboptimal results.
Innovation Solution
Utilize large language models (LLMs) in conjunction with structured information to automate the construction of attack graphs by extracting relevant Common Vulnerabilities and Exposures (CVEs) through a retriever-augmented approach, incorporating relational databases and natural language processing techniques to generate up-to-date and scalable attack graphs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional static rules or heuristics are used to construct attack graphs, then the construction process is simpler, but the accuracy and scalability deteriorate due to the complexity of modern computing systems and the sheer volume of information
Solution Approach 1:
The patent replaces traditional mechanical rule-based attack graph construction systems with a large language model-based system. The LLM automatically analyzes vulnerability information, system configurations, and exploit data to generate attack graphs, eliminating the need for manual rule definition and heuristic methods. This substitution enables the system to handle the complexity of modern computing systems while maintaining or improving accuracy.
Solution Approach 2:
The patent changes the fundamental parameters of attack graph construction by transitioning from static rules to dynamic, context-aware LLM analysis. The system processes natural language descriptions of vulnerabilities and system states, extracting relevant information and generating attack paths based on semantic understanding rather than predefined rules. This parameter change allows the system to adapt to diverse and evolving system configurations.
2Productivity
If manual analysis of vulnerability information is performed, then the attack graph construction is more controllable, but the productivity and efficiency deteriorate due to the time-consuming nature of analyzing sheer volume of information
Solution Approach 1:
The patent implements a self-service attack graph construction system where the LLM autonomously analyzes vulnerability information, identifies attack paths, and generates attack graphs without requiring manual intervention. The system automatically processes input data about system configurations, vulnerabilities, and exploits, performing the entire analysis pipeline independently. This self-service capability dramatically improves productivity while reducing the time investment required from security analysts.
Solution Approach 2:
The patent replaces manual mechanical analysis processes with automated LLM-based analysis. The large language model processes and synthesizes vulnerability information at speeds impossible for human analysts, extracting key details and constructing attack graphs rapidly. This substitution maintains analytical depth while exponentially increasing construction speed and reducing time loss.
3Adaptability or versatility
If existing attack graph methods are used, then the implementation is more straightforward, but the adaptability deteriorates because they do not leverage advanced natural language processing features and are not scalable
Solution Approach 1:
The patent creates a universal attack graph construction system based on LLM capabilities that can handle diverse input formats, vulnerability types, and system configurations. The LLM's natural language processing abilities enable it to understand and process various descriptions of security vulnerabilities, system states, and exploit conditions uniformly. This universal approach allows the system to adapt to different computing environments and threat scenarios without requiring method-specific modifications, thereby improving scalability.
4Reliability
If comprehensive vulnerability information is analyzed, then the attack graph completeness is improved, but the difficulty of detecting and measuring deteriorates due to the sheer volume of information
Solution Approach 1:
The patent introduces the large language model as an intermediary between raw vulnerability information and the final attack graph. The LLM processes and synthesizes comprehensive vulnerability data, filtering and structuring the information into meaningful attack paths. This intermediary capability allows the system to handle large volumes of information while maintaining completeness, as the LLM can understand contextual relationships and prioritize relevant vulnerabilities without being overwhelmed by the sheer volume of data.
Data Source
AI summary
In one embodiment, a method for automatic construction of attack graphs using large language models includes sending, by a computing system comprising one or more processors configured to execute one or more processes, vulnerability information to at least one large language model in order to extract structured information and building, by the computing system comprising the one or more processors configured to execute the one or more processes, a natural language query based on the structured information. The method further comprises sending, by the computing system comprising the one or more processors configured to execute the one or more processes, the natural language query to the at least one large language model to generate at least a portion of an attack graph from the vulnerability information.