Attack Path Explainer With Graph Anonymization and LLM Guardrails
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Solution Overview
Problem
Large Language Models (LLMs) struggle to answer domain-specific questions due to insufficient training data, exhibit hallucinations, and pose privacy risks when handling enterprise network data, particularly in explaining complex attack paths and large graphs.
Innovation Solution
A privacy-aware dynamic path explainer system that summarizes and explains attack paths using a summarizer, decoder model, and guardrails to reduce hallucinations and protect proprietary data, utilizing a decoder-only generative model to generate structured outputs within context limitations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If full network graphs with proprietary data are sent to LLMs for attack path explanation, then comprehensive security analysis is achieved, but privacy is compromised and costs increase
Solution Approach 1:
The system extracts and removes proprietary data elements (asset names, sensitive identifiers) from the network graph before sending it to the LLM. This extraction process retains the structural and topological information necessary for security analysis while eliminating privacy-sensitive content, thus resolving the contradiction between analysis completeness and privacy protection.
Solution Approach 2:
The system creates an anonymized copy of the network graph that preserves the attack path structure and vulnerability relationships while replacing sensitive asset identifiers with generic placeholders. This copy enables comprehensive security analysis without exposing proprietary data, effectively resolving the privacy-risk contradiction.
2Measurement precision
If full network graphs are sent to LLMs for attack path explanation, then accurate security analysis is achieved, but latency and costs increase
Solution Approach 1:
The system extracts only the essential structural and topological elements of the network graph that are necessary for attack path analysis, removing redundant asset details and proprietary information. This extraction reduces the input size to the LLM, thereby reducing processing latency while preserving the accuracy needed for meaningful security explanations.
3Adaptability or versatility
If LLMs are used to explain attack paths, then domain-specific security insights are generated, but hallucinations occur reducing reliability
Solution Approach 1:
The system implements a feedback mechanism where the LLM's attack path explanations are validated against the original (anonymized) graph structure and known security principles. This feedback loop identifies and corrects hallucinated content, ensuring that the domain-specific insights generated by the LLM maintain high reliability and accuracy.
Data Source
AI summary
Various techniques for providing a privacy-aware dynamic path explainer are disclosed. In some embodiments, a system, a process, and/or a computer program product for a privacy-aware dynamic path explainer includes receiving a graph of a network that includes one or more vulnerabilities and/or one or more risk findings (e.g., the graph can also include one or more systems and/or one or more misconfigurations); contextualizing the graph of the network; generating one or more prompts and inputting the contextualized graph to a Large-Language Model (LLM); and generating an output that summarizes the contextualized graph using the LLM.