SCRIPTIOC-BENCH is a benchmark designed to evaluate how well large language models can statically extract indicators of compromise (IOCs) from script-based malware. It contains 634 manually verified JavaScript, PowerShell, and VBScript samples and covers four IOC types—URLs, domains, IP addresses, and filesystem artifacts—while distinguishing between directly exposed and encoded indicators. Experiments show that even the best models achieve only 65.4 F1, and a false‑positive taxonomy is introduced to analyze error patterns, with two mitigations (deterministic string utilities and task‑specific adaptation) improving precision and shifting errors toward sample‑grounded mismatches.
By Hanna Kim, Jian Cui, Minkyoo Song, Hwanjo Heo, Seungwon Shin, Kimin Lee, Xiaojing Liao
Script-based malware remains a prevalent attack technique. These scripts often contain indicators of compromise (IOCs) that provide actionable threat intelligence. However, statically recovering such...
Large Language Models (LLMs) and generative AI (GenAI) systems, such as ChatGPT, Claude, Gemini, LLaMA, Copilot, Stable Diffusion by OpenAI, Anthropic, Google, Meta, Microsoft, Stability AI, respectively, are revolutionizing cybersecurity, enabling both automated defense and sophisticated attacks. These technologies power real-time threat detection, phishing defense, secure code generation, and vulnerability exploitation at unprecedented scales.
arXiv:2607. 06963v1 Announce Type: cross Abstract: Large Language Models (LLMs) and generative AI (GenAI) systems, such as ChatGPT, Claude, Gemini, LLaMA, Copilot, Stable Diffusion by OpenAI, Anthropic, Google, Meta, Microsoft, Stability AI, respectively, are revolutionizing cybersecurity, enabling both automated defense and sophisticated attacks.
By Kiarash Ahi, Saeed Valizadeh
Delphi Scanner is a static malware detection system for Windows PE files that balances efficiency and interpretability. It employs a convolutional neural network to model Windows API sequences and a rule‑based interpretation layer to map APIs to high‑level malicious capabilities. Tested on over 190,000 PE files, it achieves 95.35% accuracy with a 1.53 MB model, and demonstrates robustness against out‑of‑distribution samples and adversarial manipulations.
By Bijied Brahimi, Vincent Cohadon, Gabriel Glazman, Rayan Al Mohaize, Omran Berjawi, Rida Khatoun
arXiv:2509. 14335v2 Announce Type: replace-cross Abstract: Automated malware classifiers achieve strong detection performance, but auditing requires more than flagging a sample: analysts must explain malicious behaviors and justify them with code evidence.
By Xinran Zheng, Xingzhi Qian, Yiling He, Shuo Yang, Lorenzo Cavallaro
arXiv:2606. 20436v1 Announce Type: cross Abstract: Malware analysts often inspect compiled binaries through decompiled pseudo-C, when source code is unavailable.
By Bercan Turkmen, Vyas Raina
The paper reports a new attack surface in AI agent harnesses: lifecycle hooks that bind shell commands to runtime events. By updating only the hook configuration, an attacker can silently inject malicious commands into a benign plugin, enabling host‑side attacks such as privilege escalation. The authors present HookPry, an automated framework that demonstrates this vulnerability across 25 harness‑backend combinations, compromising all evaluated harnesses with high success rates.
By Pengxun Li, Litian Zhang, Jianwei Hou, Shujiang Wu, Song Li, Zifeng Kang, Xi Zhang
arXiv:2607. 20216v1 Announce Type: cross Abstract: Malware analysis demands rapid interpretation of complex detonation reports spanning filesystem, network, and process behaviours.
By Adel ElZemity, Shujun Li, Budi Arief
arXiv:2608. 08468v1 Announce Type: cross Abstract: Agent Skills---structured packages of instructions and scripts that augment LLM-based agents---are rapidly proliferating, yet their security properties remain under-explored.
By Xinze Chen, Chi Zhang, Ping Ji, Yimin Liu
arXiv:2607. 24177v1 Announce Type: cross Abstract: Due to the lack of systematic evaluations, we are not yet able to determine which AI-based Windows malware detector to deploy in production, since existing evaluations (i) differ in terms of data used for both training and testing; (ii) do not consider temporal analysis to showcase whether models withstand the passage of time; (iii) avoid security evaluations with adversarial attacks that could highlight their brittleness against content-injection attacks; and (iv) neglect the computational requirements for deployment, risking slow inference on endpoints.
By Andrea Ponte, Daniel Gibert, Matous Kozak, Dmitrijs Trizna, Maura Pintor, Battista Biggio, Fabio Roli, Luca Demetrio
arXiv:2606. 07792v1 Announce Type: cross Abstract: MOLOT (Malicious Operational Logic Observation Transformer) is a static malicious-code detection system designed for SAST setup where package metadata, maintainer history, and dynamic execution traces may be unavailable or unreliable.
By Daniil Lopatkin, Maksim Mitrofanov, Stanislav Rakovsky, Aleksandr Khalikov