CVE2AP is an LLM-based system that automatically converts natural language CVE descriptions into PDDL-encoded attack paths. It uses structured prompting and an error‑feedback loop that refines outputs based on planner‑reported syntactic and solvability errors. Empirical tests across various LLMs show high quality results, with up to 86.9% syntax correctness, 78.6% solvability, and 93.1% semantic correctness, and GPT‑5.5 providing the best quality‑cost balance.
By Lin Cui, Vincenzo Scotti, Raffaela Mirandola
The paper introduces AUTOSIGMA, an automated system that converts unstructured cyber threat intelligence reports into Sigma detection rules. It enriches input data with a structured knowledge base, matches it against existing Sigma rule repositories, and uses a large language model as a judge to validate the generated rules. Experiments on real-world APT reports and security blogs show that AUTOSIGMA outperforms other methods in rule validity, relevancy, MITRE ATT&CK coverage, and robustness to input quality.
By Sepehr Ghaffarzadegan, Boubakr Nour, Makan Pourzandi, Mourad Debbabi, Chadi Assi
arXiv:2608. 07808v1 Announce Type: cross Abstract: Four years after prompt injection was first identified in 2022, attacks are still predominantly documented as verbatim strings rather than structured exploits, despite advancing agent capabilities and threat actors embedding injections to subvert AI-assisted security analysis.
By Jeremy McHugh
arXiv:2608.28394v1 Announce Type: cross
Abstract: Cyber threat intelligence (CTI) is foundational to modern cyber defense, yet much of it resides in unstructured reports whose volume and heterogeneit...
By Changze Li, Yutong Cheng, Tsania Camila Finnisa, Qian Cui, Wei Ding, Peng Gao
arXiv:2609.15523v1 Announce Type: cross
Abstract: Logic attack graphs grounded in scanner output provide explicit and auditable attack path reasoning LLM-based agents lack. Integrating symbolic frame...
By Oliver Stevanovic, Jasmin Wachter
arXiv:2606. 31557v1 Announce Type: cross Abstract: In the evolving threat landscape, adversaries exploit software vulnerabilities to launch sophisticated attacks, challenging traditional defenses.
By Basant Agarwal, Dincy R. Arikkat, Swati Yadav, Serena Nicolazzo, Antonino Nocera, Vinod P
arXiv:2608. 00143v2 Announce Type: replace-cross Abstract: Automated attack chain generation is critical for modern cybersecurity, yet manual construction fails to scale as adversary behaviors expand.
By Ramya Varunsegar
arXiv:2609. 07344v1 Announce Type: cross Abstract: Large language model (LLM) based agents are increasingly applied to cybersecurity tasks such as vulnerability discovery and automated penetration testing.
By Weizhe Wang, Yitong Zhang, Yao Zhang, Xiaoqiang Di, Zhigang Li, Bin Wu, Guangquan Xu
arXiv:2608.23181v1 Announce Type: cross
Abstract: As large language models (LLMs) continue to advance in coding capabilities, their potential in cybersecurity has drawn increasing research attention,...
By Jian Yang, Haau-Sing Li, Shawn Guo, Zixi Zhao, Yibo Tan, Jiajun Wu, Aishan Liu, Xianglong Liu, Tianyu Zheng, Bryan Dai, Chengran Yang
arXiv:2608. 09526v1 Announce Type: cross Abstract: Real-world cyberattacks often require sustained progress across multiple hosts and network segments, making multi-hop cyber ranges essential infrastructure for studying and improving LLM agents' ability to sustain complete attack chains.
By Hanlin Jiang, Puyi Wang, Jiandong Jin, Shaofei Li, Zhan Shen, Pengli Wang, Ziming Wang, Yifeng Cai, Ning Jia, Yuxin Ren, Peng Jiang, Yao Guo, Ding Li
arXiv:2608. 03591v1 Announce Type: cross Abstract: Large Language Model (LLM) agents offer a promising approach to attack chain reconstruction by retrieving and interpreting heterogeneous telemetry to infer ordered attacker actions.
By Xuyang Liu, Yibin Han, Zhenwei Zhang, Kai Chang, Zhiwei Xu, Tian Qiu, Weixian Deng, Jiabao Gao, Xiaolin Peng, Hai Wan, Xibin Zhao
arXiv:2508. 18684v2 Announce Type: replace-cross Abstract: Signature-based Intrusion Detection Systems (IDS) detect malicious activity by matching network or host events against predefined rules.
By Shaswata Mitra, Subash Neupane, Martin Duclos, Sudip Mittal, Aritran Piplai, Md Rayhanur Rahman, Edward Zieglar, Shahram Rahimi