--- title: "BaronLLM Offensive Security LLM Q6_K GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/411d9a39-81a1-42bd-bd72-61c881a62c38" updated: "2026-11-13" --- # BaronLLM Offensive Security LLM Q6\_K GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. Answers security questions and helps plan authorized red-team exercises, as a Q6\_K GGUF fine-tune of Llama 3.1 8B. For security researchers working on systems they have permission to test. BaronLLM Offensive Security LLM Q6\_K GGUF is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by AlicanKiraz0 ## About ### What it is This is a 6-bit GGUF copy of BaronLLM, a language model fine-tuned by Alican Kiraz for offensive cybersecurity research and adversarial simulation. It has 8B parameters and builds on Llama-3.1-8B-Instruct. The card says it was trained with safety limits so it refuses direct malware source and ransomware builders. ### Who it suits Security researchers, penetration testers and defenders who want a local assistant for authorized work. Use it only on systems you own or have written permission to test. Who uses it, and how | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Security testing | Penetration testers | Walk through a known vulnerability step by step | Ask for an analysis of a published CVE in a lab environment | | Security operations | Blue-team analysts | Sort and summarize attack traces from logs | Paste Zeek logs and ask which patterns look like command-and-control | | Red-team planning | Authorized red teams | Outline an attack chain for an exercise | Ask for a plan for a lab network you have permission to test | | Education | Security students | Study how attacks are reasoned about | Ask for a walkthrough of a classic web flaw | Skip it if you have no authorization to test the systems involved, because the card positions it for research and simulation only. ### Where to use it - Ollama or another local runner that opens GGUF files - Windows, macOS or Linux for downloading the files, with Python - Your own lab or test network, never systems you may not touch | Use case | Who | What they get back | | --- | --- | --- | | Vulnerability walkthrough | Pentesters | Step-by-step reasoning about a flaw, with code-level explanation | | Log triage | Analysts | A short classification of attack traces from SIEM, PCAP or EDR data | | Exercise planning | Red teams | A staged outline of an ATT\&CK-style scenario | ### How to set it up You need Python and pip to download, and a local runner for GGUF files. The weights are about 4 GB at 4-bit by Hugging Face's estimate, so expect somewhat more at 6-bit. - Python and pip - A GGUF-capable runner such as Ollama - Disk space for the Q6\_K file 1. Install the Hugging Face hub library. 2. Download the repository. 3. Open the GGUF file in your runner. ```sh pip install -U huggingface_hub hf download AlicanKiraz0/Cybersecurity-BaronLLM_Offensive_Security_LLM_Q6_K_GGUF ``` ### How to use it Load the file and ask security questions in plain English. The card suggests a temperature of 0.3 and top\_p of 0.9 for steady reasoning, and higher values for brainstorming. Its sample Python code names a different 70B model, so use this repository's own name when you adapt it. Prompt styles from the card | Goal | What to ask | | --- | --- | | Vulnerability walkthrough | Give a role such as senior pentester and ask for a step-by-step analysis of a known CVE | | Exercise planning | Ask for an attack chain from initial access to exfiltration for an environment you may test | | Log triage | Hand over Zeek logs and ask which traffic looks like command-and-control | ### Example You paste a block of Zeek logs and ask, "Which of these connections look like command-and-control traffic?" The model sorts the entries and explains which patterns it finds suspicious. A human analyst should confirm the result. ### For newcomers and experts If you are new, start with log triage, which is defensive. Load the model locally, paste a short log sample, and read how it explains what it sees. Treat every answer as a lead to verify. For people in the field, the model has an 8,192-token sequence length and English-only training. The card lists public vulnerability databases, exploit write-ups from known researchers, permitted red-team reports and vetted synthetic ATT\&CK chains as data sources. It says safety tuning used policy-gradient RLHF with security experts, plus red-teaming against SecEval v0.3. Download the Q6\_K GGUF file → Load it in a local runner → Ask a security question → Verify the answer yourself ### Good to know - License: MIT - Cost to run: none named, you use your own computer - Data it reaches: it downloads files from Hugging Face and then runs locally, so your prompts stay on your machine - Not for: unauthorized access, or malware and ransomware building, which the card says its policy bars - Its sequence length is 8,192 tokens, so long logs need splitting ## README ````markdown # BaronLLM Offensive Security LLM Q6_K GGUF Answers security questions and helps plan authorized red-team exercises, as a Q6_K GGUF fine-tune of Llama 3.1 8B. For security researchers working on systems they have permission to test. Made by AlicanKiraz0 on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: This is a 6-bit GGUF copy of BaronLLM, a language model fine-tuned by Alican Kiraz for offensive cybersecurity research and adversarial simulation. It has 8B parameters and builds on Llama-3.1-8B-Instruct. The card says it was trained with safety limits so it refuses direct malware source and ransomware builders. Who it suits: Security researchers, penetration testers and defenders who want a local assistant for authorized work. Use it only on systems you own or have written permission to test. ```table Title: Who uses it, and how Industry | Who uses it | How they use it | Example Security testing | Penetration testers | Walk through a known vulnerability step by step | Ask for an analysis of a published CVE in a lab environment Security operations | Blue-team analysts | Sort and summarize attack traces from logs | Paste Zeek logs and ask which patterns look like command-and-control Red-team planning | Authorized red teams | Outline an attack chain for an exercise | Ask for a plan for a lab network you have permission to test Education | Security students | Study how attacks are reasoned about | Ask for a walkthrough of a classic web flaw ``` Skip it if you have no authorization to test the systems involved, because the card positions it for research and simulation only. Where to use it: - Ollama or another local runner that opens GGUF files - Windows, macOS or Linux for downloading the files, with Python - Your own lab or test network, never systems you may not touch ```table Use case | Who | What they get back Vulnerability walkthrough | Pentesters | Step-by-step reasoning about a flaw, with code-level explanation Log triage | Analysts | A short classification of attack traces from SIEM, PCAP or EDR data Exercise planning | Red teams | A staged outline of an ATT&CK-style scenario ``` How to set it up: You need Python and pip to download, and a local runner for GGUF files. The weights are about 4 GB at 4-bit by Hugging Face's estimate, so expect somewhat more at 6-bit. - Python and pip - A GGUF-capable runner such as Ollama - Disk space for the Q6_K file 1. Install the Hugging Face hub library. 2. Download the repository. 3. Open the GGUF file in your runner. ```sh pip install -U huggingface_hub hf download AlicanKiraz0/Cybersecurity-BaronLLM_Offensive_Security_LLM_Q6_K_GGUF ``` How to use it: Load the file and ask security questions in plain English. The card suggests a temperature of 0.3 and top_p of 0.9 for steady reasoning, and higher values for brainstorming. Its sample Python code names a different 70B model, so use this repository's own name when you adapt it. ```table Title: Prompt styles from the card Goal | What to ask Vulnerability walkthrough | Give a role such as senior pentester and ask for a step-by-step analysis of a known CVE Exercise planning | Ask for an attack chain from initial access to exfiltration for an environment you may test Log triage | Hand over Zeek logs and ask which traffic looks like command-and-control ``` Example: You paste a block of Zeek logs and ask, "Which of these connections look like command-and-control traffic?" The model sorts the entries and explains which patterns it finds suspicious. A human analyst should confirm the result. For newcomers and experts: If you are new, start with log triage, which is defensive. Load the model locally, paste a short log sample, and read how it explains what it sees. Treat every answer as a lead to verify. For people in the field, the model has an 8,192-token sequence length and English-only training. The card lists public vulnerability databases, exploit write-ups from known researchers, permitted red-team reports and vetted synthetic ATT&CK chains as data sources. It says safety tuning used policy-gradient RLHF with security experts, plus red-teaming against SecEval v0.3. ```flow Download the Q6_K GGUF file Load it in a local runner Ask a security question Verify the answer yourself ``` Good to know: - License: MIT - Cost to run: none named, you use your own computer - Data it reaches: it downloads files from Hugging Face and then runs locally, so your prompts stay on your machine - Not for: unauthorized access, or malware and ransomware building, which the card says its policy bars - Its sequence length is 8,192 tokens, so long logs need splitting ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 8B - Weights: about 4 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 2,578 - Likes: 392 - License: mit - Languages: en - Based on: AlicanKiraz0/BaronLLM-llama3.1-v1 - Quantized versions: 1 - Tags: transformers, gguf, llama-cpp, gguf-my-repo, text-generation, conversational ## Links - Model: https://huggingface.co/AlicanKiraz0/Cybersecurity-BaronLLM_Offensive_Security_LLM_Q6_K_GGUF - Files: https://huggingface.co/AlicanKiraz0/Cybersecurity-BaronLLM_Offensive_Security_LLM_Q6_K_GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download AlicanKiraz0/Cybersecurity-BaronLLM_Offensive_Security_LLM_Q6_K_GGUF ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [AlicanKiraz0/Cybersecurity-BaronLLM_Offensive_Security_LLM_Q6_K_GGUF on Hugging Face](https://huggingface.co/AlicanKiraz0/Cybersecurity-BaronLLM_Offensive_Security_LLM_Q6_K_GGUF) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | BaronLLM Offensive Security LLM Q6\_K GGUF (this page) | 2,578 | 392 | 8B | mit | 2025-06-04 | | [Qwen2.5-Coder-1.5B Q8\_0 GGUF](https://mindket.com/p/96f7cae7-39f5-4fb6-adac-73595ff1230c) | 6,422 | 27 | 1.5B | apache-2.0 | 2024-10-28 | | [Qwen3-Reranker-0.6B-Q8\_0-GGUF](https://mindket.com/p/ef8865aa-9c06-4cab-8674-5746900d8414) | 116,029 | 32 | 596M | apache-2.0 | 2025-10-03 | | [Bonsai-27B-gguf](https://mindket.com/p/0f4c3b2e-9ef6-4680-896e-4319b270e9f8) | 179,818 | 945 | 26.9B | apache-2.0 | 2026-07-17 | | [Bonsai-1.7B-GGUF](https://mindket.com/p/976ed9c8-c475-4a88-a05b-e149421d5c28) | 173,963 | 100 | 1.7B | apache-2.0 | 2026-04-18 | | [MNN-Pocket-0.3B-GGUF](https://mindket.com/p/1e60bd47-21da-4333-9781-f0f82b9a2ebb) | 12,467 | 20 | 362M | apache-2.0 | 2026-11-10 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Bluesky · ˚\*𓈒𓏸𑁍 Kaori Fujisawa ꕤ · 2026-03-24](https://bsky.app/profile/edcba1111.bsky.social/post/3mhrh7xw2s22j): BaronLLM, you give it a CVE and it generates the full exploit chain, writes proof-of-concept code, maps ATT\&CK techniques, and builds complete red team playbooks huggingface.co/AlicanKiraz0... ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2025-06-04 - First published: 2025-01-21 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open AlicanKiraz0/Cybersecurity-BaronLLM_Offensive_Security_LLM_Q6_K_GGUF on Hugging Face](https://huggingface.co/AlicanKiraz0/Cybersecurity-BaronLLM_Offensive_Security_LLM_Q6_K_GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/411d9a39-81a1-42bd-bd72-61c881a62c38) - [Its page on Mindket](https://mindket.com/p/411d9a39-81a1-42bd-bd72-61c881a62c38)