AttackSeqBench: Benchmarking Large Language Models' Understanding of Sequential Patterns in Cyber Attacks

• URL: http://arxiv.org/abs/2503.03170v1
• Date: Wed, 05 Mar 2025 04:25:21 GMT
• Title: AttackSeqBench: Benchmarking Large Language Models' Understanding of Sequential Patterns in Cyber Attacks
• Authors: Javier Yong, Haokai Ma, Yunshan Ma, Anis Yusof, Zhenkai Liang, Ee-Chien Chang,
• Abstract summary: We introduce AttackSeqBench, a benchmark to evaluate Large Language Models' (LLMs) capability to understand and reason attack sequences in Cyber Threat Intelligence (CTI) reports.<n>Our benchmark encompasses three distinct Question Answering (QA) tasks, each task focuses on the varying granularity in adversarial behavior.<n>We conduct extensive experiments and analysis with both fast-thinking and slow-thinking LLMs, while highlighting their strengths and limitations in analyzing the sequential patterns in cyber attacks.
• Score: 13.082370325093242
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The observations documented in Cyber Threat Intelligence (CTI) reports play a critical role in describing adversarial behaviors, providing valuable insights for security practitioners to respond to evolving threats. Recent advancements of Large Language Models (LLMs) have demonstrated significant potential in various cybersecurity applications, including CTI report understanding and attack knowledge graph construction. While previous works have proposed benchmarks that focus on the CTI extraction ability of LLMs, the sequential characteristic of adversarial behaviors within CTI reports remains largely unexplored, which holds considerable significance in developing a comprehensive understanding of how adversaries operate. To address this gap, we introduce AttackSeqBench, a benchmark tailored to systematically evaluate LLMs' capability to understand and reason attack sequences in CTI reports. Our benchmark encompasses three distinct Question Answering (QA) tasks, each task focuses on the varying granularity in adversarial behavior. To alleviate the laborious effort of QA construction, we carefully design an automated dataset construction pipeline to create scalable and well-formulated QA datasets based on real-world CTI reports. To ensure the quality of our dataset, we adopt a hybrid approach of combining human evaluation and systematic evaluation metrics. We conduct extensive experiments and analysis with both fast-thinking and slow-thinking LLMs, while highlighting their strengths and limitations in analyzing the sequential patterns in cyber attacks. The overarching goal of this work is to provide a benchmark that advances LLM-driven CTI report understanding and fosters its application in real-world cybersecurity operations. Our dataset and code are available at https://github.com/Javiery3889/AttackSeqBench .

Related papers

• LLM-Safety Evaluations Lack Robustness [58.334290876531036]
  We argue that current safety alignment research efforts for large language models are hindered by many intertwined sources of noise.<n>We propose a set of guidelines for reducing noise and bias in evaluations of future attack and defense papers.
  arXiv  Detail & Related papers  (2025-03-04T12:55:07Z)
• OCCULT: Evaluating Large Language Models for Offensive Cyber Operation Capabilities [0.0]
  We demonstrate a new approach to assessing AI's progress towards enabling and scaling real-world offensive cyber operations. We detail OCCULT, a lightweight operational evaluation framework that allows cyber security experts to contribute to rigorous and repeatable measurement. We find that there has been significant recent advancement in the risks of AI being used to scale realistic cyber threats.
  arXiv  Detail & Related papers  (2025-02-18T19:33:14Z)
• Interactive Agents to Overcome Ambiguity in Software Engineering [61.40183840499932]
  AI agents are increasingly being deployed to automate tasks, often based on ambiguous and underspecified user instructions.<n>Making unwarranted assumptions and failing to ask clarifying questions can lead to suboptimal outcomes.<n>We study the ability of LLM agents to handle ambiguous instructions in interactive code generation settings by evaluating proprietary and open-weight models on their performance.
  arXiv  Detail & Related papers  (2025-02-18T17:12:26Z)
• LLMs in Software Security: A Survey of Vulnerability Detection Techniques and Insights [12.424610893030353]
  Large Language Models (LLMs) are emerging as transformative tools for software vulnerability detection. This paper provides a detailed survey of LLMs in vulnerability detection. We address challenges such as cross-language vulnerability detection, multimodal data integration, and repository-level analysis.
  arXiv  Detail & Related papers  (2025-02-10T21:33:38Z)
• CTINEXUS: Leveraging Optimized LLM In-Context Learning for Constructing Cybersecurity Knowledge Graphs Under Data Scarcity [49.657358248788945]
  Textual descriptions in cyber threat intelligence (CTI) reports are rich sources of knowledge about cyber threats. Current CTI extraction methods lack flexibility and generalizability, often resulting in inaccurate and incomplete knowledge extraction. We propose CTINexus, a novel framework leveraging optimized in-context learning (ICL) of large language models.
  arXiv  Detail & Related papers  (2024-10-28T14:18:32Z)
• Context is Key: A Benchmark for Forecasting with Essential Textual Information [87.3175915185287]
  "Context is Key" (CiK) is a forecasting benchmark that pairs numerical data with diverse types of carefully crafted textual context.<n>We evaluate a range of approaches, including statistical models, time series foundation models, and LLM-based forecasters.<n>We propose a simple yet effective LLM prompting method that outperforms all other tested methods on our benchmark.
  arXiv  Detail & Related papers  (2024-10-24T17:56:08Z)
• Jailbreaking and Mitigation of Vulnerabilities in Large Language Models [4.564507064383306]
  Large Language Models (LLMs) have transformed artificial intelligence by advancing natural language understanding and generation. Despite these advancements, LLMs have shown considerable vulnerabilities, particularly to prompt injection and jailbreaking attacks. This review analyzes the state of research on these vulnerabilities and presents available defense strategies.
  arXiv  Detail & Related papers  (2024-10-20T00:00:56Z)
• CTIBench: A Benchmark for Evaluating LLMs in Cyber Threat Intelligence [0.7499722271664147]
  CTIBench is a benchmark designed to assess Large Language Models' performance in CTI applications. Our evaluation of several state-of-the-art models on these tasks provides insights into their strengths and weaknesses in CTI contexts.
  arXiv  Detail & Related papers  (2024-06-11T16:42:02Z)
• Security Vulnerability Detection with Multitask Self-Instructed Fine-Tuning of Large Language Models [8.167614500821223]
  We introduce MSIVD, multitask self-instructed fine-tuning for vulnerability detection, inspired by chain-of-thought prompting and LLM self-instruction. Our experiments demonstrate that MSIVD achieves superior performance, outperforming the highest LLM-based vulnerability detector baseline (LineVul) with a F1 score of 0.92 on the BigVul dataset, and 0.48 on the PreciseBugs dataset.
  arXiv  Detail & Related papers  (2024-06-09T19:18:05Z)
• Unveiling the Misuse Potential of Base Large Language Models via In-Context Learning [61.2224355547598]
  Open-sourcing of large language models (LLMs) accelerates application development, innovation, and scientific progress. Our investigation exposes a critical oversight in this belief. By deploying carefully designed demonstrations, our research demonstrates that base LLMs could effectively interpret and execute malicious instructions.
  arXiv  Detail & Related papers  (2024-04-16T13:22:54Z)
• Data Poisoning for In-context Learning [49.77204165250528]
  In-context learning (ICL) has been recognized for its innovative ability to adapt to new tasks. This paper delves into the critical issue of ICL's susceptibility to data poisoning attacks. We introduce ICLPoison, a specialized attacking framework conceived to exploit the learning mechanisms of ICL.
  arXiv  Detail & Related papers  (2024-02-03T14:20:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.