# Llama-3.1-FoundationAI-SecurityLLM-8B-Instruct Technical Report

arXiv: https://arxiv.org/abs/2508.01059 · HTML: https://mlsanity.com/paper/2508.01059/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 57 of its 76 references that are in the map.
- First posted on arXiv 1 Aug 2025.

## Builds on (most cited first, 30 of 57)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022)
- [Gemma 3 Technical Report](https://mlsanity.com/paper/2503.19786/) (2025)
- [Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them](https://mlsanity.com/paper/2210.09261/) (2022)
- [HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal](https://mlsanity.com/paper/2402.04249/) (2024)
- [Instruction-Following Evaluation for Large Language Models](https://mlsanity.com/paper/2311.07911/) (2023)
- [Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations](https://mlsanity.com/paper/2312.06674/) (2023)
- [Tulu 3: Pushing Frontiers in Open Language Model Post-Training](https://mlsanity.com/paper/2411.15124/) (2024)
- [Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators](https://mlsanity.com/paper/2404.04475/) (2024)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks](https://mlsanity.com/paper/2204.07705/) (2022)
- [Phi-4 Technical Report](https://mlsanity.com/paper/2412.08905/) (2024)
- [Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference](https://mlsanity.com/paper/2412.13663/) (2024)
- [2 OLMo 2 Furious](https://mlsanity.com/paper/2501.00656/) (2024)
- [LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models](https://mlsanity.com/paper/2308.11462/) (2023)
- [MEDITRON-70B: Scaling Medical Pretraining for Large Language Models](https://mlsanity.com/paper/2311.16079/) (2023)
- [A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models](https://mlsanity.com/paper/2401.01313/) (2024)
- [Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?](https://mlsanity.com/paper/2405.05904/) (2024)

## Built on by (most cited first, 23 of 23)

- [Llama-3.1-FoundationAI-SecurityLLM-Reasoning-8B Technical Report](https://mlsanity.com/paper/2601.21051/) (2026)
- [Uncovering Vulnerabilities of LLM-Assisted Cyber Threat Intelligence](https://mlsanity.com/paper/2509.23573/) (2025)
- [Trust Me, Import This: Dependency Steering Attacks via Malicious Agent Skills](https://mlsanity.com/paper/2605.09594/) (2026)
- [Toward Cybersecurity-Expert Small Language Models](https://mlsanity.com/paper/2510.14113/) (2025)
- [Personalized Learning Path Planning with Goal-Driven Learner State Modeling](https://mlsanity.com/paper/2510.13215/) (2025)
- [UrduBench: An Urdu Reasoning Benchmark using Contextually Ensembled Translations with Human-in-the-Loop](https://mlsanity.com/paper/2601.21000/) (2026)
- [Antares: Foundation Models for Agentic Vulnerability Localization](https://mlsanity.com/paper/2608.02407/) (2026)
- [Netflix Artwork Personalization via LLM Post-training](https://mlsanity.com/paper/2601.02764/) (2026)
- [RedSage: A Cybersecurity Generalist LLM](https://mlsanity.com/paper/2601.22159/) (2026)
- [HypRAG: Hyperbolic Dense Retrieval for Retrieval Augmented Generation](https://mlsanity.com/paper/2602.07739/) (2026)
- [RLAR: An Agentic Reward System for Multi-task Reinforcement Learning on Large Language Models](https://mlsanity.com/paper/2603.00724/) (2026)
- [Sirens' Whisper: Inaudible Near-Ultrasonic Jailbreaks of Speech-Driven LLMs](https://mlsanity.com/paper/2603.13847/) (2026)
- [FAPO: Fully Automated Prompt Optimization of Multi-Step LLM Pipelines](https://mlsanity.com/paper/2606.19605/) (2026)
- [TACTIC-KG: Toward Small Agent Teams for Cyber Threat Intelligence Knowledge Graph Construction](https://mlsanity.com/paper/2607.05001/) (2026)
- [Repairing Regex Vulnerabilities via Localization-Guided Instructions](https://mlsanity.com/paper/2510.09037/) (2025)
- [Event-Centric Human Value Understanding in News-Domain Texts: An Actor-Conditioned, Multi-Granularity Benchmark](https://mlsanity.com/paper/2603.17838/) (2026)
- [Threat Modelling using Domain-Adapted Language Models: Empirical Evaluation and Insights](https://mlsanity.com/paper/2605.10808/) (2026)
- [LLM Evolution as an Industry-Scale Ecosystem: A Lifecycle Perspective on Continual Learning](https://mlsanity.com/paper/2606.24901/) (2026)
- [Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion](https://mlsanity.com/paper/2607.14371/) (2026)
- [MITRE-SAGE: A Multi-Agent Cybersecurity Question-Answering Model](https://mlsanity.com/paper/2608.16921/) (2026)
- [MiST: Mid-Training LLMs for Cybersecurity](https://mlsanity.com/paper/2609.18496/) (2026)
- [KaliBench: A Fine-Grained Benchmark for Cybersecurity Tool Use on Kali Linux with Runtime-Free Verifiable Rewards](https://mlsanity.com/paper/2610.02206/) (2026)
- [Rethinking Quality in Large Language Model Routing: Security-Conditioned Model-and-Budget Selection](https://mlsanity.com/paper/2610.18566/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have shown remarkable success across many domains, yet their integration into cybersecurity applications remains limited due to a lack of general-purpose cybersecurity data, representational complexity, and safety and regulatory concerns. To address this gap, we previously introduced Foundation-Sec-8B, a cybersecurity-focused LLM suitable for fine-tuning on downstream tasks. That model, however, was not designed for chat-style interactions or instruction-following. In this report, we release Foundation-Sec-8B-Instruct: a model specifically trained for general-purpose cybersecurity dialogue. Built on Foundation-Sec-8B, it combines domain-specific knowledge with instruction-following, conversational capabilities, and alignment with human preferences to produce high-quality, relevant responses. Comprehensive evaluations show that Foundation-Sec-8B-Instruct outperforms Llama 3.1-8B-Instruct on a range of cybersecurity tasks while matching its instruction-following performance. It is also competitive with GPT-4o-mini on cyber threat intelligence and instruction-following tasks. We envision Foundation-Sec-8B-Instruct becoming an indispensable assistant in the daily workflows of cybersecurity professionals. We release the model publicly at https://huggingface.co/fdtn-ai/Foundation-Sec-8B-Instruct.