无审查与进攻性安全 AI 模型基准
英文标题：🔶 Uncensored and Offensive Security AI Models Benchmark
这份清单汇总了面向授权红队行动、渗透测试和安全研究的开放权重、低拒答或无审查 LLM，并依据 Hugging Face 模型卡与官方资料整理，标注时间为 2026 年 9 月。榜单覆盖从 1.5B 到 753B 参数的模型，列出基础模型、上下文长度、显存需求、训练数据、工具调用与许可证等部署关键信息。部分模型专注于漏洞赏金、CVE PoC、Nuclei 模板、恶意软件分析、威胁情报、SOC/IR 与 OSINT，另一些则通过 Abliteration 等方法降低安全拒答。对开发者而言，该清单可用于评估本地安全 Agent、自动化渗透测试与防御分析工作流的模型能力、硬件门槛及合规风险。
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面向进攻性安全的无审查 LLM
面向授权红队行动、渗透测试和安全研究的开放权重无审查模型精选清单。
所有数据均来自 Hugging Face 模型卡和官方发布资料。2026 年 9 月。
安全微调模型
1. DeepHat V2（WhiteRabbitNeo）
| 规格 | 数值 |
|---|---|
| 基础模型 | Qwen2.5-Coder-7B |
| 参数量 | 7B / 32B |
| 上下文长度 | 131K |
| 显存（Q4_K_M） | 约 6 GB |
| 去审查方法 | 在 170 万条进攻性/防御性样本上进行 SFT |
| 训练数据 | 170 万条安全专用样本（USENIX Security 2024 workshop） |
| 视觉能力 | 否 |
| 工具调用 | 是 |
| 许可证 | Apache 2.0 |
下载： https://huggingface.co/WhiteRabbitNeo
2. BugTraceAI-CORE-Apex（26B）
| 规格 | 数值 |
|---|---|
| 基础模型 | Gemma4-26B MoE |
| 参数量 | 26B MoE |
| 上下文长度 | 32K |
| 显存（Q4_K_M） | 约 16 GB |
| 去审查方法 | 基于 HackerOne Hacktivity 2024-2025 进行 SFT |
| 训练数据 | HackerOne 报告 + WAF 规避数据集 |
| 视觉能力 | 否 |
| 工具调用 | 是 |
| 许可证 | Apache 2.0 |
下载： https://huggingface.co/BugTraceAI/BugTraceAI-CORE-Apex-26b
3. BugTraceAI-CORE-Ultra（27B）
| 规格 | 数值 |
|---|---|
| 基础模型 | Qwen3.6-27B（DavidAU 微调变体） |
| 参数量 | 27B 稠密模型 |
| 上下文长度 | 4K（推荐） |
| 显存（Q6_K） | 约 22-24 GB |
| 去审查方法 | 通过 Unsloth 基于漏洞赏金与 CVE 数据进行 SFT |
| 训练数据 | 来自漏洞赏金披露、CVE 技术分析和安全研究的 2,541 个示例（2024-2026） |
| 专长 | 工具生成模型：生成 Nuclei 模板、CVE PoC、exploit 代码和渗透测试脚本 |
| 视觉能力 | 否 |
| 工具调用 | 是 |
| 许可证 | Apache 2.0 |
下载： https://huggingface.co/BugTraceAI/BugTraceAI-CORE-Ultra-27B-Q6
4. CYBER-FROST-3.8（Blackfrost-AI）
| 规格 | 数值 |
|---|---|
| 基础模型 | Qwen/Qwen3.8-Flash-Next |
| 参数量 | 总计约 180B（512 个路由专家，每个 token 激活 10 个） |
| 上下文长度 | 262K |
| 架构 | Qwen4ExpForConditionalGeneration、48 个 Transformer 块、混合线性 + 全注意力 |
| 显存 | 需要多 GPU（已在 4x NVIDIA B300 SXM6 上测试） |
| 去审查方法 | 基于 Blackfrost-AI 专有语料库进行安全领域微调 |
| 训练数据 | 专有安全语料：侦察、Web 应用安全、漏洞研究、恶意软件分析、云安全、威胁情报 |
| MTP | 是（1 个原生 MTP 层，用于推测式解码） |
| 视觉能力 | 否 |
| 工具调用 | 是 |
| 许可证 | Qwen Community License 1.0 |
下载： https://huggingface.co/Blackfrost-AI/CYBER-FROST-3.8-BF16
| 规格 | 数值 |
|---|---|
| 基础模型 | gpt-oss-20b |
| 参数量 | 约 20B（文件中为 21B） |
| 上下文长度 | 8,192 |
| 显存（BF16） | 约 42 GB |
| 去审查方法 | 基于 SecKnowledge 2.0 流水线进行 SFT |
| 训练数据 | 通过专家参与式模式引导、多步骤事实锚定和 LLM 质量检查生成的 403K 个示例 |
| 专长 | 防御：CTI、漏洞分析、检测/缓解、SOC/IR、AppSec、合规 |
| 视觉能力 | 否 |
| 工具调用 | 否 |
| 许可证 | Apache 2.0 |
下载： https://huggingface.co/cyber-pal-security/CyberPal2.0-20B
6. Cyber-Prime 1.1（2.6B）
| 规格 | 数值 |
|---|---|
| 基础模型 | LiquidAI/LFM2-2.6B |
| 参数量 | 2.6B（实际约 3B） |
| 上下文长度 | 不适用（模型卡未说明） |
| 显存（BF16） | 约 6 GB |
| 张量类型 | BF16 |
| 去审查方法 | 在 75K 条网络安全数据上进行 SFT + RL + 奖励引导后训练 |
| 训练数据 | NER 修复（约 6K）、带 CoT 的 HTTP 推理（约 5K）、钓鱼邮件（约 5K）、威胁情报摘要（约 2K）、GHSA/KEV/ATT&CK |
| 运行模式 | Direct 模式（分类）+ Think 模式（思维链） |
| CyberBench 平均分 | 0.592 F1/Acc（较 v1.0 的 0.501 上升） |
| CyberBench 亮点 | NER 0.499、Phishing 0.890、HTTP Attack 0.628 |
| 视觉能力 | 否 |
| 工具调用 | 否 |
| 许可证 | LFM Open License v1.0 |
下载： https://huggingface.co/Akahsizrr/Cyber-Prime-1.1-2.6B
7. Cyber-Ornith-1.5-9B（DuoNeural / mradermacher）
| 规格 | 数值 |
|---|---|
| 基础模型 | ornith-ai/Ornith-1.5-9B（Qwen 3.5 架构） |
| 参数量 | 9B |
| 上下文长度 | 128K（Qwen 3.5 默认值） |
| 显存（Q4_K_M） | 约 7 GB |
| 去审查方法 | Obliteration（abliteration 变体） |
| 训练数据 | NousResearch/hermes-function-calling-v1、OpenThoughts3-1.2M、openhands-synthetic-conversations |
| 专长 | Agentic 网络安全：函数调用、工具使用、推理、CLI/终端自动化 |
| 格式 | GGUF（可提供 IQ1_S 至 Q6_K） |
| 视觉能力 | 否 |
| 工具调用 | 是 |
| 许可证 | Apache 2.0 |
下载： https://huggingface.co/mradermacher/Cyber-Ornith-1.5-9B-OBLITERATED-i1-GGUF
8. Dolphin3-Cyber-8B（RavichandranJ）
| 规格 | 数值 |
|---|---|
| 基础模型 | Dolphin3.0-Llama3.1-8B-abliterated |
| 参数量 | 8.03B |
| 上下文长度 | 2,048（微调后）/ 131K（基础模型） |
| 显存（Q4_K_M） | 约 6 GB |
| 去审查方法 | 在 abliterated Dolphin3 基础模型上进行 LoRA rank-16 微调 |
| 训练数据 | 网络安全专用数据：渗透测试、漏洞分析、exploit 开发、事件响应 |
| 架构 | LlamaForCausalLM、32 层、GQA（32 个头、8 个 KV 头） |
| 性能 | 从 5 tok/s（CPU）到 55 tok/s（RTX 4060） |
| 视觉能力 | 否 |
| 工具调用 | 否 |
| 许可证 | Llama 3.1 |
下载： https://huggingface.co/RavichandranJ/Dolphin3-Cyber-8B-GGUF
9. Imperum-CybersecurityLLM v1.0
| 规格 | 数值 |
|---|---|
| 基础模型 | Qwen/Qwen3.6-35B-A3B |
| 参数量 | 总计 34.66B / 激活约 3B（MoE，256 个路由专家，每个 token 激活 8 个） |
| 上下文长度 | 16,384（资源受限时建议使用 8,192） |
| 显存（Q4_K_M） | 约 22 GB |
| 架构 | Qwen3.5-MoE、40 层、混合线性 + 全注意力 |
| 去审查方法 | 覆盖 10 多个安全领域的 SFT |
| 训练数据 | SOC/SIEM 运营、检测工程、DFIR、恶意软件分析、威胁情报、漏洞管理、云/K8s/IAM、OT 安全、GRC、授权渗透测试 |
| 视觉能力 | 否 |
| 工具调用 | 是 |
| 许可证 | Apache 2.0 |
下载： https://huggingface.co/IMPERUM/Imperum-CybersecurityLLM-v1.0-GGUF
10. Lily-Cybersecurity-7B v0.2（Segolily Labs）
| 规格 | 数值 |
|---|---|
| 基础模型 | Mistral-7B-Instruct-v0.2 |
| 参数量 | 7B |
| 上下文长度 | 8K |
| 显存（Q4_K_M） | 约 6 GB |
| 去审查方法 | 在 22K 个网络安全数据对上进行 SFT |
| 训练数据 | 覆盖 28 多个领域的 22,000 个人工构建网络安全数据对：渗透测试、恶意软件分析、IR、云安全 |
| 训练硬件 | 单张 A100，24 小时，5 个 epoch |
| 视觉能力 | 否 |
| 工具调用 | 否 |
| 许可证 | Apache 2.0 |
下载： https://huggingface.co/segolilylabs/Lily-Cybersecurity-7B-v0.2
| 规格 | 数值 |
|---|---|
| 基础模型 | Qwen3-8B |
| 参数量 | 8B |
| 上下文长度 | 32K |
| 显存（Q4_K_M） | 约 6 GB |
| 去审查方法 | LoRA r=4，2,804 个精选样本 |
| 训练数据 | GTFOBins、HackTricks、HackTheBox 技术文章 |
| GTFOBins 准确率 | 100%（基础模型零样本为 25%） |
| 视觉能力 | 否 |
| 工具调用 | 否 |
| 许可证 | Apache 2.0 |
下载： https://huggingface.co/gewsefa/pentest-v2
12. Qwythos-9B（Empero AI）
| 规格 | 数值 |
|---|---|
| 基础模型 | Qwen3.5-9B |
| 参数量 | 9B |
| 上下文长度 | 1M（YaRN rope-scaling） |
| 显存（Q4_K_M） | 约 7 GB |
| 去审查方法 | 在 5 亿以上 token 的 Claude Mythos / Claude Fable 含 CoT 轨迹上进行后训练 |
| 基准测试 | 相比基础模型，MMLU +34、GSM8K +30（Empero 评测） |
| 原生函数调用 | 是（Qwen3.5 规格） |
| 思维链 | 始终启用 <think> 块 |
| 变体 | Base（SFT）、Claude-Mythos-5-1M-GGUF（Q4_K_M 至 BF16） |
| 视觉能力 | 是（继承视觉塔） |
| 工具调用 | 是 |
| 许可证 | Apache 2.0 |
下载（基础版）： https://huggingface.co/emperorai/Qwythos-9B
下载（GGUF）： https://huggingface.co/empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF
13. RavenX-CyberAgent（deadbydawn101）
| 规格 | 数值 |
|---|---|
| 基础模型 | Qwen/Qwen3.6-35B-A3B |
| 参数量 | 总计 36B / 激活 3B（MoE） |
| 上下文长度 | 262K（原生），已测试 32K |
| 显存（Q4_K_M） | 约 24 GB |
| 去审查方法 | 基于来自 110 个来源的 745K+ 示例进行 12 轮渐进式 SFT |
| 训练数据 | 渗透测试报告、漏洞赏金数据、Claude Mythos 推理、MITRE ATT&CK、黑帽内容 |
| 专长 | RATH 协议：Attack Surface、Exploit、Impact、Remediation、Document、Prevent |
| 输出格式 | CVSS 评分、CWE 标识符、MITRE ATT&CK 映射 |
| 推理速度 | 生成 89 tok/s，提示词处理 900 tok/s |
| 视觉能力 | 否 |
| 工具调用 | 是 |
| 许可证 | Apache 2.0 |
14. REDCELL-26B-A4B（terrorswift）
| 规格 | 数值 |
|---|---|
| 基础模型 | Google Gemma 4 26B-A4B（Unsloth 微调） |
| 参数量 | 总计 26B / 激活约 4B（MoE） |
| 上下文长度 | 262K |
| 显存（APEX-Mini） | 约 12 GB |
| 显存（Q8_0） | 约 26 GB |
| 去审查方法 | 在 6,500 条定制指令上进行 16 位 LoRA SFT |
| 训练数据 | 网络威胁情报、调查新闻、反虚假信息、分析方法论 |
| 专长 | OSINT：威胁行为者归因、IoC 枢轴分析、地理定位分析、Admiralty 信息源可信度评估、漏洞语境化 |
| APEX 量化 | 领域加权 imatrix（约 70% REDCELL 语料、约 30% 通用校准） |
| 视觉能力 | 否 |
| 工具调用 | 否 |
| 许可证 | Apache 2.0 |
下载： https://huggingface.co/terrorswift/REDCELL-26B-A4B-OSINT-Cyber-APEX-GGUF
| 规格 | 数值 |
|---|---|
| 基础模型 | Mistral-7B |
| 参数量 | 7B |
| 上下文长度 | 8K |
| 显存（Q4_K_M） | 约 6 GB |
| 去审查方法 | 基于渗透测试轨迹进行 QLoRA SFT + DPO |
| 训练数据 | 渗透测试方法论、工具使用、报告撰写 |
| 视觉能力 | 否 |
| 工具调用 | 否 |
| 许可证 | Apache 2.0 |
下载： https://huggingface.co/vextechnologies/VEXT-Pentest-7B
16. security-slm-unsloth-1.5b
| 规格 | 数值 |
|---|---|
| 基础模型 | Qwen2.5-1.5B |
| 参数量 | 1.5B |
| 上下文长度 | 32K |
| 显存（Q4_K_M） | 约 2 GB |
| 去审查方法 | 基于安全问答进行 Unsloth SFT |
| 训练数据 | 安全知识库、CTF 风格数据 |
| 视觉能力 | 否 |
| 工具调用 | 否 |
| 许可证 | Apache 2.0 |
下载： https://huggingface.co/AbdullahMujtaba/security-slm-unsloth-1.5b
通用 Abliterated 模型
17. Qwen3.8-27B-Uncensored-OrcaRouter（chimingw GGUF）
| 规格 | 数值 |
|---|---|
| 基础模型 | Qwen3.8-27B |
| 参数量 | 27B 稠密模型 |
| 上下文长度 | 262K |
| 显存（Q4_K_M） | 约 18 GB |
| 去审查方法 | Abliteration（131 个矩阵，Arditi et al. 2024） |
| 智能指数 | 52（Artificial Analysis） |
| 视觉能力 | 是 |
| 工具调用 | 是 |
| 许可证 | Apache 2.0 |
| HF 下载量 | 230K+ |
| HF 点赞数 | 257+ |
下载（GGUF）： https://huggingface.co/chimingw/Qwen3.8-27B-Uncensored-OrcaRouter-GGUF
下载（基础版）： https://huggingface.co/orcarouter/Qwen3.8-27B-Uncensored
18. GLM-5.3-Flash-Uncensored-FP8（OrcaRouter）
| 规格 | 数值 |
|---|---|
| 基础模型 | GLM-5.3-Flash |
| 参数量 | 总计 320B / 激活 18B（288 个路由专家，MoE） |
| 上下文长度 | 1M |
| 显存（FP8） | 约 80 GB+（多 GPU） |
| 去审查方法 | Abliteration（第 22/45 层，更深层的拒答机制） |
| 遵从率 | 82.8%（OrcaRouter 测试） |
| MTP | 是（保留多 Token 预测） |
| 视觉能力 | 是 + 视频 |
| 工具调用 | 是 |
| 许可证 | MIT |
下载： https://huggingface.co/orcarouter/GLM-5.3-Flash-Uncensored-FP8
19. GLM-5.3-CYBERSECURITY-FP8（dealignai）
| 规格 | 数值 |
|---|---|
| 基础模型 | zai-org/GLM-5.3（通过 JANGQ-AI/GLM-5.3-FP8） |
| 参数量 | 总计 753B（glm_moe_dsa 架构） |
| 上下文长度 | 约 131K（在 8x H200、TP8 下的实际值） |
| 显存（FP8） | 使用张量并行的 8x H200 GPU |
| 架构 | 78 层、仅文本、路由 FP8 专家 |
| 去审查方法 | 针对进攻性安全、红队、exploit 开发、逆向工程、规避、钓鱼、凭证攻击、恶意软件分析进行直接权重修改 |
| 说明 | 并非 abliteration 或 LoRA；采用直接 bf16 残差写入器编辑。保留对受版权保护内容复现的软性拒答 |
| 视觉能力 | 否（仅文本） |
| 工具调用 | 是 |
| 许可证 | MIT |
下载： https://huggingface.co/dealignai/GLM-5.3-CYBERSECURITY-FP8
20. DeepSeek-V4.1-Flash-Abliterated-Cybersecurity-Unleashed（drowzeys）
| 规格 | 数值 |
|---|---|
| 基础模型 | DeepSeek-V4.1-Flash |
| 参数量 | MoE（规模与基础模型相同） |
| C |
English / 原文
Uncensored LLMs for Offensive Security
Curated list of open-weight uncensored models for authorized red team operations, penetration testing, and security research.
All data sourced from HuggingFace model cards and official publications. Sep 2026.
Security Fine-tuned Models
1. DeepHat V2 (WhiteRabbitNeo)
| Spec | Value |
|---|---|
| Base Model | Qwen2.5-Coder-7B |
| Parameters | 7B / 32B |
| Context Length | 131K |
| VRAM (Q4_K_M) | ~6 GB |
| Uncensoring Method | SFT on 1.7M offensive/defensive samples |
| Training Data | 1.7M security-specific samples (USENIX Security 2024 workshop) |
| Vision | No |
| Tool Calling | Yes |
| License | Apache 2.0 |
Download: https://huggingface.co/WhiteRabbitNeo
2. BugTraceAI-CORE-Apex (26B)
| Spec | Value |
|---|---|
| Base Model | Gemma4-26B MoE |
| Parameters | 26B MoE |
| Context Length | 32K |
| VRAM (Q4_K_M) | ~16 GB |
| Uncensoring Method | SFT on HackerOne Hacktivity 2024-2025 |
| Training Data | HackerOne reports + WAF evasion dataset |
| Vision | No |
| Tool Calling | Yes |
| License | Apache 2.0 |
Download: https://huggingface.co/BugTraceAI/BugTraceAI-CORE-Apex-26b
3. BugTraceAI-CORE-Ultra (27B)
| Spec | Value |
|---|---|
| Base Model | Qwen3.6-27B (DavidAU fine-tuned variant) |
| Parameters | 27B dense |
| Context Length | 4K (recommended) |
| VRAM (Q6_K) | ~22-24 GB |
| Uncensoring Method | SFT via Unsloth on bug bounty + CVE data |
| Training Data | 2,541 examples from bug bounty disclosures, CVE writeups, and security research (2024-2026) |
| Specialization | Tooling model: generates Nuclei templates, CVE PoCs, exploit code, pentest scripts |
| Vision | No |
| Tool Calling | Yes |
| License | Apache 2.0 |
Download: https://huggingface.co/BugTraceAI/BugTraceAI-CORE-Ultra-27B-Q6
4. CYBER-FROST-3.8 (Blackfrost-AI)
| Spec | Value |
|---|---|
| Base Model | Qwen/Qwen3.8-Flash-Next |
| Parameters | ~180B total (512 routed experts, 10 active per token) |
| Context Length | 262K |
| Architecture | Qwen4ExpForConditionalGeneration, 48 transformer blocks, hybrid linear + full attention |
| VRAM | Multi-GPU required (tested on 4x NVIDIA B300 SXM6) |
| Uncensoring Method | Security-domain fine-tuning on proprietary Blackfrost-AI corpus |
| Training Data | Proprietary security corpus: recon, web app security, vuln research, malware analysis, cloud security, threat intel |
| MTP | Yes (1 native MTP layer for speculative decoding) |
| Vision | No |
| Tool Calling | Yes |
| License | Qwen Community License 1.0 |
Download: https://huggingface.co/Blackfrost-AI/CYBER-FROST-3.8-BF16
| Spec | Value |
|---|---|
| Base Model | gpt-oss-20b |
| Parameters | ~20B (21B in files) |
| Context Length | 8,192 |
| VRAM (BF16) | ~42 GB |
| Uncensoring Method | SFT on SecKnowledge 2.0 pipeline |
| Training Data | 403K examples via expert-in-the-loop schema steering, multi-step grounding, LLM quality checks |
| Specialization | Defensive: CTI, vuln analysis, detection/mitigation, SOC/IR, AppSec, compliance |
| Vision | No |
| Tool Calling | No |
| License | Apache 2.0 |
Download: https://huggingface.co/cyber-pal-security/CyberPal2.0-20B
6. Cyber-Prime 1.1 (2.6B)
| Spec | Value |
|---|---|
| Base Model | LiquidAI/LFM2-2.6B |
| Parameters | 2.6B (~3B actual) |
| Context Length | N/A (model card does not specify) |
| VRAM (BF16) | ~6 GB |
| Tensor Type | BF16 |
| Uncensoring Method | SFT + RL + reward-guided post-training on 75K cybersecurity rows |
| Training Data | NER repair (~6K), HTTP reasoning w/ CoT (~5K), email phishing (~5K), threat intel summarization (~2K), GHSA/KEV/ATT&CK |
| Operating Modes | Direct mode (classification) + Think mode (chain-of-thought) |
| CyberBench Average | 0.592 F1/Acc (up from 0.501 in v1.0) |
| CyberBench Highlights | NER 0.499, Phishing 0.890, HTTP Attack 0.628 |
| Vision | No |
| Tool Calling | No |
| License | LFM Open License v1.0 |
Download: https://huggingface.co/Akahsizrr/Cyber-Prime-1.1-2.6B
7. Cyber-Ornith-1.5-9B (DuoNeural / mradermacher)
| Spec | Value |
|---|---|
| Base Model | ornith-ai/Ornith-1.5-9B (Qwen 3.5 architecture) |
| Parameters | 9B |
| Context Length | 128K (Qwen 3.5 default) |
| VRAM (Q4_K_M) | ~7 GB |
| Uncensoring Method | Obliteration (abliteration variant) |
| Training Data | NousResearch/hermes-function-calling-v1, OpenThoughts3-1.2M, openhands-synthetic-conversations |
| Specialization | Agentic cybersecurity: function-calling, tool-use, reasoning, CLI/terminal automation |
| Format | GGUF (IQ1_S to Q6_K available) |
| Vision | No |
| Tool Calling | Yes |
| License | Apache 2.0 |
Download: https://huggingface.co/mradermacher/Cyber-Ornith-1.5-9B-OBLITERATED-i1-GGUF
8. Dolphin3-Cyber-8B (RavichandranJ)
| Spec | Value |
|---|---|
| Base Model | Dolphin3.0-Llama3.1-8B-abliterated |
| Parameters | 8.03B |
| Context Length | 2,048 (fine-tuned) / 131K (base) |
| VRAM (Q4_K_M) | ~6 GB |
| Uncensoring Method | LoRA rank-16 on abliterated Dolphin3 base |
| Training Data | Cybersecurity-specific: pentest, vuln analysis, exploit dev, incident response |
| Architecture | LlamaForCausalLM, 32 layers, GQA (32 heads, 8 KV heads) |
| Performance | 5 tok/s (CPU) to 55 tok/s (RTX 4060) |
| Vision | No |
| Tool Calling | No |
| License | Llama 3.1 |
Download: https://huggingface.co/RavichandranJ/Dolphin3-Cyber-8B-GGUF
9. Imperum-CybersecurityLLM v1.0
| Spec | Value |
|---|---|
| Base Model | Qwen/Qwen3.6-35B-A3B |
| Parameters | 34.66B total / ~3B active (MoE, 256 routed experts, 8 active per token) |
| Context Length | 16,384 (recommended 8,192 for resource-constrained) |
| VRAM (Q4_K_M) | ~22 GB |
| Architecture | Qwen3.5-MoE, 40 layers, hybrid linear + full attention |
| Uncensoring Method | SFT across 10+ security domains |
| Training Data | SOC/SIEM operations, detection engineering, DFIR, malware analysis, threat intel, vuln management, cloud/K8s/IAM, OT security, GRC, authorized pentesting |
| Vision | No |
| Tool Calling | Yes |
| License | Apache 2.0 |
Download: https://huggingface.co/IMPERUM/Imperum-CybersecurityLLM-v1.0-GGUF
10. Lily-Cybersecurity-7B v0.2 (Segolily Labs)
| Spec | Value |
|---|---|
| Base Model | Mistral-7B-Instruct-v0.2 |
| Parameters | 7B |
| Context Length | 8K |
| VRAM (Q4_K_M) | ~6 GB |
| Uncensoring Method | SFT on 22K cybersecurity pairs |
| Training Data | 22,000 hand-crafted cybersecurity data pairs across 28+ domains: pentesting, malware analysis, IR, cloud security |
| Training Hardware | Single A100, 24h, 5 epochs |
| Vision | No |
| Tool Calling | No |
| License | Apache 2.0 |
Download: https://huggingface.co/segolilylabs/Lily-Cybersecurity-7B-v0.2
| Spec | Value |
|---|---|
| Base Model | Qwen3-8B |
| Parameters | 8B |
| Context Length | 32K |
| VRAM (Q4_K_M) | ~6 GB |
| Uncensoring Method | LoRA r=4, 2,804 curated samples |
| Training Data | GTFOBins, HackTricks, HackTheBox writeups |
| GTFOBins Accuracy | 100% (vs 25% base model zero-shot) |
| Vision | No |
| Tool Calling | No |
| License | Apache 2.0 |
Download: https://huggingface.co/gewsefa/pentest-v2
12. Qwythos-9B (Empero AI)
| Spec | Value |
|---|---|
| Base Model | Qwen3.5-9B |
| Parameters | 9B |
| Context Length | 1M (YaRN rope-scaling) |
| VRAM (Q4_K_M) | ~7 GB |
| Uncensoring Method | Post-training on 500M+ tokens of Claude Mythos / Claude Fable traces with CoT |
| Benchmarks | +34 MMLU, +30 GSM8K vs base (Empero evals) |
| Native Function Calling | Yes (Qwen3.5 spec) |
| Chain-of-Thought | Always-on <think> block |
| Variants | Base (SFT), Claude-Mythos-5-1M-GGUF (Q4_K_M to BF16) |
| Vision | Yes (inherited vision tower) |
| Tool Calling | Yes |
| License | Apache 2.0 |
Download (base): https://huggingface.co/emperorai/Qwythos-9B
Download (GGUF): https://huggingface.co/empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF
13. RavenX-CyberAgent (deadbydawn101)
| Spec | Value |
|---|---|
| Base Model | Qwen/Qwen3.6-35B-A3B |
| Parameters | 36B total / 3B active (MoE) |
| Context Length | 262K (native), 32K tested |
| VRAM (Q4_K_M) | ~24 GB |
| Uncensoring Method | 12-round progressive SFT on 745K+ examples from 110 sources |
| Training Data | Pentest reports, bug bounty data, Claude Mythos reasoning, MITRE ATT&CK, blackhat content |
| Specialization | RATH protocol: Attack Surface, Exploit, Impact, Remediation, Document, Prevent |
| Output Format | CVSS scores, CWE identifiers, MITRE ATT&CK mappings |
| Inference Speed | 89 tok/s generation, 900 tok/s prompt processing |
| Vision | No |
| Tool Calling | Yes |
| License | Apache 2.0 |
14. REDCELL-26B-A4B (terrorswift)
| Spec | Value |
|---|---|
| Base Model | Google Gemma 4 26B-A4B (Unsloth fine-tuned) |
| Parameters | 26B total / ~4B active (MoE) |
| Context Length | 262K |
| VRAM (APEX-Mini) | ~12 GB |
| VRAM (Q8_0) | ~26 GB |
| Uncensoring Method | 16-bit LoRA SFT on 6,500 custom instructions |
| Training Data | Cyber threat intelligence, investigative journalism, counter-disinformation, analytical methodology |
| Specialization | OSINT: threat actor attribution, IoC pivoting, geolocation analysis, Admiralty source credibility, vulnerability contextualization |
| APEX Quantization | Domain-weighted imatrix (~70% REDCELL corpus, ~30% general calibration) |
| Vision | No |
| Tool Calling | No |
| License | Apache 2.0 |
Download: https://huggingface.co/terrorswift/REDCELL-26B-A4B-OSINT-Cyber-APEX-GGUF
| Spec | Value |
|---|---|
| Base Model | Mistral-7B |
| Parameters | 7B |
| Context Length | 8K |
| VRAM (Q4_K_M) | ~6 GB |
| Uncensoring Method | QLoRA SFT + DPO on pentest traces |
| Training Data | Pentest methodology, tool usage, reporting |
| Vision | No |
| Tool Calling | No |
| License | Apache 2.0 |
Download: https://huggingface.co/vextechnologies/VEXT-Pentest-7B
16. security-slm-unsloth-1.5b
| Spec | Value |
|---|---|
| Base Model | Qwen2.5-1.5B |
| Parameters | 1.5B |
| Context Length | 32K |
| VRAM (Q4_K_M) | ~2 GB |
| Uncensoring Method | Unsloth SFT on security Q&A |
| Training Data | Security knowledge base, CTF-style |
| Vision | No |
| Tool Calling | No |
| License | Apache 2.0 |
Download: https://huggingface.co/AbdullahMujtaba/security-slm-unsloth-1.5b
General Abliterated Models
17. Qwen3.8-27B-Uncensored-OrcaRouter (chimingw GGUF)
| Spec | Value |
|---|---|
| Base Model | Qwen3.8-27B |
| Parameters | 27B dense |
| Context Length | 262K |
| VRAM (Q4_K_M) | ~18 GB |
| Uncensoring Method | Abliteration (131 matrices, Arditi et al. 2024) |
| Intelligence Index | 52 (Artificial Analysis) |
| Vision | Yes |
| Tool Calling | Yes |
| License | Apache 2.0 |
| HF Downloads | 230K+ |
| HF Likes | 257+ |
Download (GGUF): https://huggingface.co/chimingw/Qwen3.8-27B-Uncensored-OrcaRouter-GGUF
Download (base): https://huggingface.co/orcarouter/Qwen3.8-27B-Uncensored
18. GLM-5.3-Flash-Uncensored-FP8 (OrcaRouter)
| Spec | Value |
|---|---|
| Base Model | GLM-5.3-Flash |
| Parameters | 320B total / 18B active (288 routed experts, MoE) |
| Context Length | 1M |
| VRAM (FP8) | ~80 GB+ (multi-GPU) |
| Uncensoring Method | Abliteration (layer 22/45, deeper refusal mechanism) |
| Compliance Rate | 82.8% (OrcaRouter testing) |
| MTP | Yes (Multi-Token Prediction preserved) |
| Vision | Yes + Video |
| Tool Calling | Yes |
| License | MIT |
Download: https://huggingface.co/orcarouter/GLM-5.3-Flash-Uncensored-FP8
19. GLM-5.3-CYBERSECURITY-FP8 (dealignai)
| Spec | Value |
|---|---|
| Base Model | zai-org/GLM-5.3 (via JANGQ-AI/GLM-5.3-FP8) |
| Parameters | 753B total (glm_moe_dsa architecture) |
| Context Length | ~131K (practical on 8x H200 w/ TP8) |
| VRAM (FP8) | 8x H200 GPUs with tensor parallelism |
| Architecture | 78 layers, text-only, routed FP8 experts |
| Uncensoring Method | Direct weight modification for offensive-security, red-team, exploit-dev, RE, evasion, phishing, credential-attack, malware-analysis |
| Notes | Not abliteration or LoRA; direct bf16 residual writer editing. Soft refusal on copyright reproduction retained |
| Vision | No (text-only) |
| Tool Calling | Yes |
| License | MIT |
Download: https://huggingface.co/dealignai/GLM-5.3-CYBERSECURITY-FP8
20. DeepSeek-V4.1-Flash-Abliterated-Cybersecurity-Unleashed (drowzeys)
| Spec | Value |
|---|---|
| Base Model | DeepSeek-V4.1-Flash |
| Parameters | MoE (size matches base) |
| Context Length | Matches base DeepSeek-V4.1-Flash |
| Uncensoring Method | Abliteration overlay on layers 10-35 attention projection (wo_b); layers 0-9, 36-39, expert layers, vision components unchanged |
| Format | Modular overlay (not standalone checkpoint): FP8 (~1.1 GB) or EXL3 mul1 K=5 (~651 MB) |
| Deployment | Apply on top of existing quantized base packs (native, EXL3, TR3-Hybrid) |
| GPU Util | <= 0.85 recommended |
| Vision | Yes (preserved) |
| Tool Calling | Yes |
| License | MIT |
Download: https://huggingface.co/drowzeys/DeepSeek-V4.1-Flash-Abliterated-Cybersecurity-Unleashed
21. huihui-ai/Qwen3.5-27B-abliterated
| Spec | Value |
|---|---|
| Base Model | Qwen3.5-27B |
| Parameters | 27B dense |
| Context Length | 128K |
| VRAM (Q4_K_M) | ~18 GB |
| Uncensoring Method | Abliteration |
| Vision | No |
| Tool Calling | Yes |
| License | Apache 2.0 |
Download: https://huggingface.co/huihui-ai/Qwen3.5-27B-abliterated
22. huihui-ai/Qwen2.5-Coder-32B-Instruct-abliterated
| Spec | Value |
|---|---|
| Base Model | Qwen2.5-Coder-32B-Instruct |
| Parameters | 32B dense |
| Context Length | 128K |
| VRAM (Q4_K_M) | ~20 GB |
| Uncensoring Method | Abliteration |
| Vision | No |
| Tool Calling | No |
| License | Apache 2.0 |
Download: https://huggingface.co/huihui-ai/Qwen2.5-Coder-32B-Instruct-abliterated
23. Qwen3.8-27B-Cyber-agentic
| Spec | Value |
|---|---|
| Base Model | Qwen3.8-27B |
| Parameters | 27B dense |
| Context Length | 262K |
| VRAM (Q4_K_M) | ~18 GB |
| Uncensoring Method | Abliteration + cyber agentic fine-tune |
| Vision | Yes |
| Tool Calling | Yes |
| License | Apache 2.0 |
Download: https://huggingface.co/Qwen/Qwen3.8-27B (base, community abliterated variants available)
24. HIDra-30B-A3B (huihui-ai/Qwen3-Coder-30B-A3B-abliterated)
| Spec | Value |
|---|---|
| Base Model | Qwen3-Coder-30B-A3B |
| Parameters | 30B total / 3B active (MoE) |
| Context Length | 128K |
| VRAM (Q4_K_M) | ~20 GB |
| Uncensoring Method | Abliteration |
| Vision | No |
| Tool Calling | Yes |
| License | Apache 2.0 |
Download: https://huggingface.co/huihui-ai/Qwen3-Coder-30B-A3B-abliterated
25. qwen25_UNCENSORED_03-C
| Spec | Value |
|---|---|
| Base Model | Qwen2.5-based |
| Parameters | ~7B |
| Context Length | 32K |
| VRAM (Q4_K_M) | ~6 GB |
| Uncensoring Method | Progressive fine-tuning (multi-stage) |
| Vision | No |
| Tool Calling | No |
| License | Apache 2.0 |
Download: https://huggingface.co/models?search=qwen25_UNCENSORED
26. Dolphin-Llama3-8B (Cognitive Computations)
| Spec | Value |
|---|---|
| Base Model | Llama 3 8B |
| Parameters | 8B |
| Context Length | 8K |
| VRAM (Q4_K_M) | ~6 GB |
| Uncensoring Method | Data filtering (Dolphin method, Eric Hartford) |
| Training Data | Dolphin dataset (alignment/refusal responses removed) |
| Vision | No |
| Tool Calling | No |
| License | Llama 3 Community |
Download: https://huggingface.co/cognitivecomputations/dolphin-2.9.3-llama-3-8b
27. Wizard-Vicuna-13B-Uncensored (QuixiAI)
| Spec | Value |
|---|---|
| Base Model | LLaMA-13B |
| Parameters | 13B |
| Context Length | 2K |
| VRAM (Q4_K_M) | ~10 GB |
| Uncensoring Method | Data filtering (wizard_vicuna_70k_unfiltered) |
| MMLU | 47.92 (Open LLM Leaderboard) |
| HellaSwag | 81.95 (Open LLM Leaderboard) |
| TruthfulQA | 51.69 (Open LLM Leaderboard) |
| Vision | No |
| Tool Calling | No |
| License | Other |
| HF Likes | 323 |
Download: https://huggingface.co/QuixiAI/Wizard-Vicuna-13B-Uncensored
Cloud Providers & Deployment Platforms
Managed Inference (API Access)
| Provider | Description | Uncensored Models | Pricing | API |
|---|---|---|---|---|
| OrcaRouter | AI gateway with adaptive routing across 200+ models. Zero token markup, OpenAI-compatible endpoint. Own abliterated models (Qwen3.8, GLM-5.3). | Yes, hosts own abliterated variants | $0 token markup, BYOK or pay-as-you-go | OpenAI-compatible |
| Featherless AI | Serverless LLM hosting, HuggingFace's largest inference provider (6,700+ models). Supports uncensored/abliterated models natively. | Yes, 40K+ models including uncensored | $25/mo (32K ctx) or $50 credits/mo (256K ctx) | OpenAI-compatible |
| Together AI | Production inference platform, supports open models including uncensored variants. | Select open models | Pay-per-token | OpenAI-compatible |
| Provider | Description | Best For | GPU Options | |
| --- | --- | --- | --- | |
| RunPod | GPU cloud with serverless and pod options, Docker-based. Quick deploy with Ollama/vLLM templates. | Self-hosting any model, no content restrictions | A100, H100, H200, RTX 4090 | |
| Vast.ai | GPU marketplace, cheapest cloud GPUs. Peer-to-peer rental model. | Budget self-hosting | Consumer to datacenter GPUs | |
| Lambda | On-demand GPU cloud for AI. Enterprise-grade infrastructure. | Production workloads | A100, H100, H200 | |
| Stack | Description | GPU Required | ||
| --- | --- | --- | ||
| Ollama | One-command local LLM deployment. Easiest setup for GGUF models. | Consumer GPU (6-24 GB) | ||
| llama.cpp | C/C++ inference engine for GGUF. CPU+GPU hybrid, maximum hardware flexibility. | Flexible (CPU-only possible) | ||
| vLLM | High-throughput inference engine. PagedAttention for efficient memory. | Datacenter GPU | ||
| SGLang | Structured output + agentic workflow engine. RadixAttention for multi-turn. | Datacenter GPU | ||
| LM Studio | GUI-based local LLM runner. Drag-and-drop GGUF loading. | Consumer GPU |
| Model | Params | Context | VRAM | Method | Vision | Tools | License
---|---|---|---|---|---|---|---|---
1 | DeepHat V2 | 7B/32B | 131K | ~6 GB | SFT 1.7M samples | No | Yes | Apache 2.0
2 | BugTrace Apex | 26B MoE | 32K | ~16 GB | SFT HackerOne | No | Yes | Apache 2.0
3 | BugTraceAI Ultra | 27B | 4K | ~22 GB | SFT Unsloth | No | Yes | Apache 2.0
4 | CYBER-FROST | ~180B MoE | 262K | Multi-GPU | Security FT | No | Yes | Qwen CL
5 | CyberPal 2.0 | 20B | 8K | ~42 GB | SFT 403K | No | No | Apache 2.0
6 | Cyber-Prime 1.1 | 2.6B | N/A | ~6 GB | SFT+RL 75K | No | No | LFM Open
7 | Cyber-Ornith | 9B | 128K | ~7 GB | Obliteration | No | Yes | Apache 2.0
8 | Dolphin3-Cyber | 8B | 2K/131K | ~6 GB | LoRA on Dolphin3 | No | No | Llama 3.1
9 | Imperum | 34B/3B MoE | 16K | ~22 GB | SFT 10+ domains | No | Yes | Apache 2.0
10 | Lily-Cyber | 7B | 8K | ~6 GB | SFT 22K pairs | No | No | Apache 2.0
11 | pentest-v2 | 8B | 32K | ~6 GB | LoRA 2.8K | No | No | Apache 2.0
12 | Qwythos-9B | 9B | 1M | ~7 GB | Post-train 500M tok | Yes | Yes | Apache 2.0
13 | RavenX-CyberAgent | 36B/3B MoE | 262K | ~24 GB | SFT 745K, 12 rounds | No | Yes | Apache 2.0
14 | REDCELL-26B | 26B/4B MoE | 262K | ~12 GB | LoRA 6.5K OSINT | No | No | Apache 2.0
15 | VEXT Pentest-7B | 7B | 8K | ~6 GB | QLoRA SFT+DPO | No | No | Apache 2.0
16 | security-slm | 1.5B | 32K | ~2 GB | Unsloth SFT | No | No | Apache 2.0
17 | Qwen3.8-27B | 27B | 262K | ~18 GB | Abliteration 131 mat | Yes | Yes | Apache 2.0
18 | GLM-5.3-Flash | 320B/18B | 1M | ~80 GB+ | Abliteration | Yes | Yes | MIT
19 | GLM-5.3-CYBER | 753B | ~131K | 8xH200 | Weight modification | No | Yes | MIT
20 | DS-V4.1-Flash | MoE | base | ~1.1 GB overlay | Abliteration overlay | Yes | Yes | MIT
21 | Huihui-Qwen3.5 | 27B | 128K | ~18 GB | Abliteration | No | Yes | Apache 2.0
22 | Qwen2.5-Coder-32B | 32B | 128K | ~20 GB | Abliteration | No | No | Apache 2.0
23 | Qwen3.8-Cyber | 27B | 262K | ~18 GB | Abliteration+cyber | Yes | Yes | Apache 2.0
24 | HIDra-30B-A3B | 30B/3B | 128K | ~20 GB | Abliteration | No | Yes | Apache 2.0
25 | qwen25_UNCENSORED | ~7B | 32K | ~6 GB | Progressive FT | No | No | Apache 2.0
26 | Dolphin-Llama3 | 8B | 8K | ~6 GB | Data filtering | No | No | Llama 3
27 | Wizard-Vicuna-13B | 13B | 2K | ~10 GB | Data filtering | No | No | Other
- Abliteration : Weight-level intervention (Arditi et al. 2024) that orthogonalizes the refusal direction out of the residual stream, removing alignment constraints without retraining
- Obliteration : Variant of abliteration with similar weight-intervention approach
- SFT : Supervised Fine-Tuning on domain-specific data
- QLoRA : Quantized Low-Rank Adaptation, memory-efficient fine-tuning
- DPO : Direct Preference Optimization
- MoE : Mixture of Experts, only a subset of parameters active per token
- MTP : Multi-Token Prediction, speculative decoding for faster inference
- GGUF : Quantized format for llama.cpp / Ollama deployment
- FP8 : 8-bit floating point quantization
- BF16 : Brain floating point 16-bit, standard training/inference format
- Q4_K_M : 4-bit quantization with k-quants (medium), good balance of quality/speed
- Q6_K : 6-bit quantization with k-quants, higher quality than Q4
- RATH : RavenX Attack, Threat & Hunt protocol (6-step autonomous security assessment)
- APEX : Domain-weighted quantization using importance matrices from training corpus
- imatrix : Importance matrix quantization, preserves domain-critical weights during compression
- CyberBench : Benchmark suite for cybersecurity models (CyNER, APTNER, CyNews, SecMMLU, CyQuiz, Email Phishing, HTTP Attack Log)
| Stack | Best For | GPU Required |
|---|---|---|
| Ollama | Local dev, quick testing | Consumer GPU (6-24 GB) |
| llama.cpp | GGUF models, CPU+GPU hybrid | Flexible |
| vLLM | Production serving, high throughput | Datacenter GPU |
| SGLang | Agentic workflows, structured output | Datacenter GPU |
| Transformers | Research, custom pipelines | Any |
| LM Studio | Desktop GUI, drag-and-drop | Consumer GPU |
- HuggingFace model cards (all specifications)
- Open LLM Leaderboard v1 (Wizard-Vicuna benchmarks)
- OrcaRouter release notes (abliteration details, compliance rates)
- WhiteRabbitNeo/Kindo publications (USENIX Security 2024)
- Empero AI model card (Qwythos benchmarks)
- Eric Hartford / Cognitive Computations (Dolphin methodology)
- TrustedSec LLM Attack Benchmark (4,800 runs vs OWASP Juice Shop)
- Blackfrost-AI model card (CYBER-FROST architecture)
- deadbydawn101 model card (RavenX RATH protocol, training data)
- terrorswift model card (REDCELL OSINT methodology)
- cyber-pal-security publication (SecKnowledge 2.0 pipeline)
- BugTraceAI model card (Ultra tooling model design)
- IMPERUM model card (Imperum SOC/DFIR focus)
- Featherless AI (featherless.ai)
- OrcaRouter (orcarouter.ai)
- Reddit r/LocalLLaMA, r/netsec community reports
Joas A. Santos | Red Team Leaders | Sep 2026
For authorized security research and education only.