September 15, 2025
Today’s cyber defenders are overwhelmed by a deluge of security alerts, threat intelligence signals, and shifting business context, creating an urgent need for AI systems that can enhance operational security work. Despite the potential of Large Language Models (LLMs) to automate and scale Security Operations Center (SOC) operations, existing evaluations are incomplete in assessing the scenarios that matter most to real-world cyber defenders. This lack of informed evaluation has significant implications for both AI developers and those seeking to apply LLMs to SOC automation. Without a clear understanding of how LLMs perform in real-world security scenarios, AI system developers lack a north star to guide their development efforts, and users are left without a reliable way to select the most effective models. Furthermore, malicious actors have begun using AI to scale cyber attacks, emphasizing the need for open source benchmarks to drive adoption and community-driven improvement among defenders and AI model developers. To address this gap, we introduce CyberSOCEval, a new suite of open source benchmarks that are part of CyberSecEval 4. CyberSOCEval consists of benchmarks tailored to evaluate LLMs in two tasks: Malware Analysis and Threat Intelligence Reasoning, core defensive domains that have inadequate coverage in current security benchmarks. Our evaluations reveal that larger, more modern LLMs tend to perform better, confirming the training scaling laws paradigm. We also find that reasoning models leveraging test time scaling do not achieve the boost they do in areas like coding and math, suggesting that these models have not been trained to reason about cybersecurity analysis, and pointing to a key opportunity for improvement. Finally, we find that current LLMs are far from saturating our evaluations, demonstrating that CyberSOCEval presents a significant hill to climb for AI developers to improve AI cyber defense capabilities.
Written by
Adam Bali
Ciprian Bejean
Diana Bolocan
Ioana Croitoru
Chase Midler
Calin Miron
Brad Moon
Bruno Ostarcevic
Alberto Peltea
Matt Rosenberg
Catalin Sandu
Sagar Shah
Daniel Stan
Ernest Szocs
Sven Krasser
Arthur Saputkin
David Molnar
James Crnkovich
Joshua Saxe
Krishna Durai
Lauren Deason
Shengye Wan
Spencer Whitman
Publisher
arXiv
Research Topics
May 12, 2026
Corentin Bel, Linnea Evanson, Julien Gadonneix, Andrea Santos Revilla, Mingfang (Lucy) Zhang, Julie Bonnaire, Charlotte Caucheteux, Alexandre Défossez, Théo Desbordes, Pablo Diego-Simón, Shubh Khanna, Juliette Millet, Pierre Orhan, Saarang Panchavati, Antoine Ratouchniak, Alexis Thual, Hubert Jacob Banville, Jarod Levy, Jean Remi King, Josephine Raugel, Jérémy Rapin, Katelyn Begany, Marlene Careil, Simon Dahan, Sophia Houhamdi, Stéphane d'Ascoli, Teon Brooks, Yohann Benchetrit
May 12, 2026
May 06, 2026
Saarang Panchavati, Antoine Ratouchniak, Mingfang (Lucy) Zhang, Elisa Cascardi, Hubert Banville, Jarod Levy, Jean-Rémi King, Jérémy Rapin, Katelyn Begany, Marlene Careil, Simon Dahan, Stéphane d'Ascoli, Teon Brooks, Yohann Benchetrit
May 06, 2026
April 16, 2026
Nicola Cancedda, Pontus Stenetorp, Alexis Audran-Reiss, Alisia Lupidi, Anton Protopopov, Bassel Al Omari, Carole-Jean Wu, Derek Dunfield, Despoina Magka, Edan Toledo, Hela Momand, Ishita Mediratta, Jakob Foerster, Jean-Christophe Gagnon-Audet, Karen Hambardzumyan, Kelvin Niu, Martin Josifoski, Michael Kuchnik, Michael Shvartsman, Nicolas Baldwin, Parth Pathak, Rishi Hazra, Tatiana Shavrina, Thomas Simon Foster, Yoram Bachrach
April 16, 2026
April 14, 2026
Zijian Zhou, Bohao Tang, Pengfei Liu, Fei Zhang, Frost Xu, Hang Li (BizAI), Semih Gunel, Sen He, Soubhik Sanyal, Tao Xiang, Viktar Atliha, Zhe Wang
April 14, 2026

Our approach
Latest news
Foundational models