Uncensored and Offensive Security AI Models Benchmark
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. 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.
- ▪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.
- ▪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.
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/JoasASantos/Offensive-Security-AI-Models |
| Publication time | Tue, 29 Sep 2026 06:16:04 +0000 |
| Retrieval time | 2026-09-29T06:29:55.043Z |
| Last seen | 2026-09-29T06:29:55.043Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | nHzXL-QXUkZS · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.