# Minos: A Multi-Agent Collaborative Framework for Provenance-Based Backward Tracking

arXiv: https://arxiv.org/abs/2607.00440 · HTML: https://mlsanity.com/paper/2607.00440/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 18 of its 30 references that are in the map.
- First posted on arXiv 1 Jul 2026.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 18 of 18)

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- [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [Simple synthetic data reduces sycophancy in large language models](https://mlsanity.com/paper/2308.03958/) (2023)
- [Qwen3-Coder-Next Technical Report](https://mlsanity.com/paper/2603.00729/) (2026)
- [SLEUTH: Real-time Attack Scenario Reconstruction from COTS Audit Data](https://mlsanity.com/paper/1801.02062/) (2018)
- [Audit-LLM: Multi-Agent Collaboration for Log-based Insider Threat Detection](https://mlsanity.com/paper/2408.08902/) (2024)
- [Threat Detection and Investigation with System-level Provenance Graphs: A Survey](https://mlsanity.com/paper/2006.01722/) (2020)
- [From Sands to Mansions: Towards Automated Cyberattack Emulation with Classical Planning and Large Language Models](https://mlsanity.com/paper/2407.16928/) (2024)
- [SHIELD: APT Detection and Intelligent Explanation Using LLM](https://mlsanity.com/paper/2502.02342/) (2025)
- [OMNISEC: LLM-Driven Provenance-based Intrusion Detection via Retrieval-Augmented Behavior Prompting](https://mlsanity.com/paper/2503.03108/) (2025)

## Abstract (arXiv)

Sophisticated cyber attacks, particularly Advanced Persistent Threats (APTs), require effective post-intrusion forensic analysis. Provenance-based backward tracking reconstructs attack scenarios by tracing causality from security alerts, but existing methods rely on low-level statistical features and rigid traversal strategies, limiting their ability to capture high-level adversarial intent and suffering from dependency explosion. We present Minos, a multi-agent framework that formulates backward tracking as an LLM-driven reasoning process. Minos adopts a two-tiered architecture: for event-level analysis, it combines hierarchical context management, retrieval-augmented reasoning with citation verification, and adversarial deliberation to improve reasoning quality; for graph exploration, it coordinates four specialized agents under a finite state machine (FSM), replacing exhaustive traversal with hypothesis-guided reasoning and count-first query protocols to efficiently prune the search space. Experiments on 14 attack scenarios across five public datasets show that Minos achieves an average recall of 0.92 and precision of 0.64, significantly outperforming state-of-the-art baselines while producing attack subgraphs that are 49% more compact. Moreover, Minos generates interpretable reasoning throughout the tracking process, facilitating forensic auditing and system refinement. These results demonstrate the effectiveness of LLM-driven reasoning for automated provenance-based backward tracking.