Model Detail
Cybersecurity-BaronLLM_Offensive_Security_LLM_Q6_K_GGUF
—Cybersecurity-BaronLLM_Offensive_Security_LLM_Q6_K_GGUF is a large language model with 8B parameters released by AlicanKiraz0. The model is registered under the text-generation pipeline tag on Hugging Face, distributed under the permissive mit license.
Cybersecurity-BaronLLM_Offensive_Security_LLM_Q6_K_GGUF ships with 8B parameters, distributed as a quantized weight variant for lower-VRAM inference. The mit license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.
Cybersecurity-BaronLLM_Offensive_Security_LLM_Q6_K_GGUF is best fit for general-purpose chat and instruction-following workloads. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
LLM-Based Weak Annotation for Cybersecurity Tweet Classification: A Teacher-Student Framework
arXiv:2610.21254v1 Announce Type: new Abstract: Cyber threat intelligence (CTI) relies on the timely identification of emerging threats, enabling organizations and security analysts to support situational awareness and decision-making. Social media platforms such as X (formerly Twitter) have become
Robustness and Cybersecurity in the EU Artificial Intelligence Act
arXiv:2502.16184v4 Announce Type: replace Abstract: The EU Artificial Intelligence Act (AIA) establishes different legal principles for different types of AI systems. While prior work has sought to clarify some of these principles, little attention has been paid to robustness and cybersecurity. This
The Next Challenge for Agentic Cybersecurity: A Realistic, Contamination-Free Reverse Engineering Benchmark
arXiv:2608.11469v2 Announce Type: replace-cross Abstract: AI agents are rapidly improving in cybersecurity when source code is available, yet much of the software most consequential to security, including malware, firmware, and proprietary applications, exists only as binaries. Analyzing such softwa
Cybersecurity in Edge Computing: A Trust-Aware Federated Hybrid Intrusion Detection Framework
arXiv:2609.39584v1 Announce Type: cross Abstract: Edge computing has emerged as a critical computing paradigm in modern distributed systems by migrating data processing closer to end users and Internet of Things (IoT) devices. While this paradigm decentralizes processes, minimizes latency, and reduc
KaliBench: A Fine-Grained Benchmark for Cybersecurity Tool Use on Kali Linux with Runtime-Free Verifiable Rewards
arXiv:2610.02206v1 Announce Type: cross Abstract: LLMs are increasingly applied to cybersecurity workflows, where they are expected to translate analysts' intent into tool invocations. However, existing evaluations focus on knowledge-based assessments or end-to-end agentic tasks, and do not directly
Adversarial Defense in Cybersecurity: A Systematic Review of GANs for Threat Detection and Mitigation
arXiv:2509.20411v3 Announce Type: replace-cross Abstract: Machine learning-based cybersecurity systems are highly vulnerable to adversarial attacks, while Generative Adversarial Networks (GANs) act as both powerful attack enablers and promising defenses. This survey systematically reviews GAN-based
