Llama-3.1-FoundationAI-SecurityLLM-Base-8B Technical Report
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913812491272192 |
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| author | Kassianik, Paul Saglam, Baturay Chen, Alexander Nelson, Blaine Vellore, Anu Aufiero, Massimo Burch, Fraser Kedia, Dhruv Zohary, Avi Weerawardhena, Sajana Priyanshu, Aman Swanda, Adam Chang, Amy Anderson, Hyrum Oshiba, Kojin Santos, Omar Singer, Yaron Karbasi, Amin |
| author_facet | Kassianik, Paul Saglam, Baturay Chen, Alexander Nelson, Blaine Vellore, Anu Aufiero, Massimo Burch, Fraser Kedia, Dhruv Zohary, Avi Weerawardhena, Sajana Priyanshu, Aman Swanda, Adam Chang, Amy Anderson, Hyrum Oshiba, Kojin Santos, Omar Singer, Yaron Karbasi, Amin |
| contents | As transformer-based large language models (LLMs) increasingly permeate society, they have revolutionized domains such as software engineering, creative writing, and digital arts. However, their adoption in cybersecurity remains limited due to challenges like scarcity of specialized training data and complexity of representing cybersecurity-specific knowledge. To address these gaps, we present Foundation-Sec-8B, a cybersecurity-focused LLM built on the Llama 3.1 architecture and enhanced through continued pretraining on a carefully curated cybersecurity corpus. We evaluate Foundation-Sec-8B across both established and new cybersecurity benchmarks, showing that it matches Llama 3.1-70B and GPT-4o-mini in certain cybersecurity-specific tasks. By releasing our model to the public, we aim to accelerate progress and adoption of AI-driven tools in both public and private cybersecurity contexts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_21039 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Llama-3.1-FoundationAI-SecurityLLM-Base-8B Technical Report Kassianik, Paul Saglam, Baturay Chen, Alexander Nelson, Blaine Vellore, Anu Aufiero, Massimo Burch, Fraser Kedia, Dhruv Zohary, Avi Weerawardhena, Sajana Priyanshu, Aman Swanda, Adam Chang, Amy Anderson, Hyrum Oshiba, Kojin Santos, Omar Singer, Yaron Karbasi, Amin Cryptography and Security Artificial Intelligence As transformer-based large language models (LLMs) increasingly permeate society, they have revolutionized domains such as software engineering, creative writing, and digital arts. However, their adoption in cybersecurity remains limited due to challenges like scarcity of specialized training data and complexity of representing cybersecurity-specific knowledge. To address these gaps, we present Foundation-Sec-8B, a cybersecurity-focused LLM built on the Llama 3.1 architecture and enhanced through continued pretraining on a carefully curated cybersecurity corpus. We evaluate Foundation-Sec-8B across both established and new cybersecurity benchmarks, showing that it matches Llama 3.1-70B and GPT-4o-mini in certain cybersecurity-specific tasks. By releasing our model to the public, we aim to accelerate progress and adoption of AI-driven tools in both public and private cybersecurity contexts. |
| title | Llama-3.1-FoundationAI-SecurityLLM-Base-8B Technical Report |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2504.21039 |