# HIDBench: Benchmarking Large Language Models for Host-Based Intrusion Detection

arXiv: https://arxiv.org/abs/2605.21773 · HTML: https://mlsanity.com/paper/2605.21773/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 45 references that are in the map.
- First posted on arXiv 20 May 2026.

## Builds on (most cited first, 23 of 23)

- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters](https://mlsanity.com/paper/2408.03314/) (2024)
- [MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark](https://mlsanity.com/paper/2406.01574/) (2024)
- [When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs](https://mlsanity.com/paper/2406.01297/) (2024)
- [Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies](https://mlsanity.com/paper/2308.03188/) (2023)
- [Cybench: A Framework for Evaluating Cybersecurity Capabilities and Risks of Language Models](https://mlsanity.com/paper/2408.08926/) (2024)
- [Purple Llama CyberSecEval: A Secure Coding Benchmark for Language Models](https://mlsanity.com/paper/2312.04724/) (2023)
- [AutoAttacker: A Large Language Model Guided System to Implement Automatic Cyber-attacks](https://mlsanity.com/paper/2403.01038/) (2024)
- [Kairos: Practical Intrusion Detection and Investigation using Whole-system Provenance](https://mlsanity.com/paper/2308.05034/) (2023)
- [threaTrace: Detecting and Tracing Host-based Threats in Node Level Through Provenance Graph Learning](https://mlsanity.com/paper/2111.04333/) (2021)
- [MAGIC: Detecting Advanced Persistent Threats via Masked Graph Representation Learning](https://mlsanity.com/paper/2310.09831/) (2023)
- [Exploring ChatGPT's Capabilities on Vulnerability Management](https://mlsanity.com/paper/2311.06530/) (2023)
- [NODLINK: An Online System for Fine-Grained APT Attack Detection and Investigation](https://mlsanity.com/paper/2311.02331/) (2023)
- [RACONTEUR: A Knowledgeable, Insightful, and Portable LLM-Powered Shell Command Explainer](https://mlsanity.com/paper/2409.02074/) (2024)
- [Threat Detection and Investigation with System-level Provenance Graphs: A Survey](https://mlsanity.com/paper/2006.01722/) (2020)
- [OMNISEC: LLM-Driven Provenance-based Intrusion Detection via Retrieval-Augmented Behavior Prompting](https://mlsanity.com/paper/2503.03108/) (2025)
- [Hybrid LLM-Enhanced Intrusion Detection for Zero-Day Threats in IoT Networks](https://mlsanity.com/paper/2507.07413/) (2025)
- [Towards a scalable AI-driven framework for data-independent Cyber Threat Intelligence Information Extraction](https://mlsanity.com/paper/2501.06239/) (2025)
- [PIDSMaker: Building and Evaluating Provenance-based Intrusion Detection Systems](https://mlsanity.com/paper/2601.22983/) (2026)

## Built on by (most cited first, 1 of 1)

- [BlueSTAR: Tiered Agentic Architecture for Autonomous Cyber Defense](https://mlsanity.com/paper/2609.11852/) (2026)

## Abstract (arXiv)

Recent benchmark efforts have advanced the evaluation of large language models (LLMs) in cybersecurity, including tasks such as penetration testing and vulnerability identification. However, a critical cybersecurity task, namely intrusion detection from system logs, remains unexplored. In this work, we present a new benchmark to assess LLMs' capabilities in supporting host-based intrusion detection systems (HIDS). This task requires fine-grained reasoning over large-scale, noisy, and highly imbalanced system logs, where complex interactions between benign and malicious activities make reliable detection challenging. Our benchmark unifies three public system log datasets, DARPA-E3, DARPA-E5, and NodLink, and introduces a data construction pipeline that transforms raw host telemetry into LLM-compatible inputs, enabling systematic evaluation under realistic intrusion detection settings. Our evaluation of frontier LLMs reveals substantial performance gaps across datasets. While many models achieve high precision (often above 0.8) on simpler datasets, their performance degrades significantly as system logs become noisier and more complex, with MCC frequently dropping below 0.5 and false positive rates increasing sharply. We further analyze model behavior and identify distinct regimes, including conservative detectors with low false positive rates and over-sensitive models that generate excessive alerts. Overall, our results highlight that while LLMs show strong potential for HIDS, their effectiveness is highly sensitive to data complexity, and robust system design is essential for reliable deployment.