17 articles mentioning Cybersecurity-BaronLLM_Offensive_Security_LLM_Q6_K_GGUF
arXiv:2604.20389v1 Announce Type: cross Abstract: The rapid evolution and use of Large Language Models (LLMs) in professional workflows require an evaluation of their domain-specific knowledge against industry standards. We introduceCyberCertBench, a new suite of Multiple Choice Question Answering (
Several US federal agencies are taking up Anthropic's new cybersecurity model to find vulnerabilities, but one is reportedly not getting in on the action: the nation's central cybersecurity coordinator. On Tuesday, Axios reported that the Cybersecurity and Infrastructure Security Agency (CISA) didn'
The Trump administration has spent nearly two months fighting with AI company Anthropic. It's dubbed the company a "RADICAL LEFT, WOKE COMPANY" full of "Leftwing nut jobs" and a menace to national security. But some of the ice may reportedly be melting between the two, thanks to Anthropic's buzzy ne
arXiv:2509.06921v2 Announce Type: replace-cross Abstract: Cybersecurity demands both rapid pattern recognition and deliberative reasoning, yet purely neural or purely symbolic approaches each address only one side of this duality. Neuro-Symbolic (NeSy) AI bridges this gap by integrating learning and
arXiv:2604.09998v1 Announce Type: cross Abstract: Large language models (LLMs) have recently emerged as promising tools for augmenting Security Operations Center (SOC) workflows, with vendors increasingly marketing autonomous AI solutions for SOCs. However, there remains a limited empirical understa
arXiv:2604.06900v1 Announce Type: cross Abstract: The field of cybersecurity is confronted with two interrelated challenges: a worldwide deficit of qualified practitioners and ongoing human-factor weaknesses that account for the bulk of security incidents. To tackle these issues, we present Sentinel
arXiv:2604.06274v1 Announce Type: cross Abstract: In recent years, the pace of development of information technology in various areas has increased drastically, forcing cybersecurity specialists to constantly review existing processes in order to prevent unauthorized access to confidential informati
arXiv:2604.06019v1 Announce Type: cross Abstract: The advancement of Large Language Models (LLMs) has raised concerns regarding their dual-use potential in cybersecurity. Existing evaluation frameworks overwhelmingly focus on Information Technology (IT) environments, failing to capture the constrain
The new model will be used by a small number of high-profile companies to engage in defensive cybersecurity work.
arXiv:2604.04121v1 Announce Type: cross Abstract: Cybersecurity research increasingly depends on reproducible evidence, such as traffic traces, logs, and labeled datasets, yet most public datasets remain static and offer limited support for controlled re-execution and traceability, especially in het
arXiv:2602.10149v3 Announce Type: replace-cross Abstract: Third-Party Risk Assessment (TPRA) relies on large repositories of cybersecurity compliance questions used to assess external suppliers against standards such as ISO/IEC 27001 and NIST. In practice, not all questions are relevant for a specif
arXiv:2603.28553v1 Announce Type: cross Abstract: Instructional alignment, the match between intended cognition and enacted activity, is central to effective instruction but hard to operationalize at scale. We examine alignment in cybersecurity simulations using multimodal traces from 23 teams (76 s
arXiv:2510.26307v2 Announce Type: replace-cross Abstract: Anomaly detection is a critical task in cybersecurity, where identifying insider threats, access violations, and coordinated attacks is essential for ensuring system resilience. Graph-based approaches have become increasingly important for mo
Highlighting innovative research and AI integration in cybersecurity
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