Topological Attribution Distance (TAD): Revealing Segment-Level RAG Influence on LLM Output Geometry for Incident Log Analysis
2026-08-17 • Cryptography and Security
Cryptography and SecurityArtificial Intelligence
AI summaryⓘ
The authors explain that Large Language Models (LLMs) are used in cybersecurity to help quickly identify threats, but it's important to trust and verify the model's outputs. They identify a challenge in tracking which pieces of evidence, like incident logs, actually influenced the model's decisions. To address this, the authors propose a method called Topological Attribution Distance (TAD), which uses geometric shapes in data to find the most important evidence behind a model's output. Their approach helps explain and trace how the model used different logs, making cybersecurity decisions more transparent and trustworthy.
Large Language Models (LLMs)cybersecurityevidence attributionprovenance trackingAgentic AIincident logsembedding spacetopologyablationexplainability
Authors
Reza Fayyazi, Michael Zuzak, Shanchieh Jay Yang
Abstract
Large Language Models (LLMs) are increasingly being deployed in cybersecurity operations to assist cybersecurity analysts with rapid decision-making against emerging threats. However, there is a main criteria that must be met when using LLMs in cybersecurity, that is, trust in the generated outputs. As Agentic AI is integrated into operational systems, a robust evidence attribution and provenance tracking technique is essential to trace the origins of model generations. When autonomous agents make a decision (right or wrong), the ability to trace back through the decision chain is critical, as without it, teams cannot identify which segment of the data caused the model generation. Existing methods often struggle to distinguish among complex and highly similar evidence sources, such as cyber incident logs. This reveals a key gap: current approaches do not adequately capture the holistic geometric relationship between the retrieved evidence and the generated response for reliable evidence verification. To bridge this gap, we propose Topological Attribution Distance (TAD), inspired by Topology, to characterize and capture the global geometric shape of an output and its changes against its retrieved logs. In other words, if the embeddings of a specific source log drastically changes the geometry of the model's response in the embedding space, this suggests that such log is a critical source for the model's generated response. Therefore, TAD is powered by segment-level ablation attribution to investigate incident logs of an actual cyberattack. We demonstrate how TAD finds the most attributed logs on LLM outputs in an adaptive manner. This can provide an explainable and trustworthy tracing based on each LLM's hidden state to understand how geometrically different retrieved logs influence the model generation, and provide evidence verification in cybersecurity and Agentic-AI workflows.