github.comrepoUncensored and Offensive Security AI Models Benchmark
A GitHub README curating open-weight "uncensored" and security-fine-tuned LLMs (e.g. WhiteRabbitNeo's DeepHat V2, BugTraceAI-CORE, CYBER-FROST, CyberPal2.0, Lily-Cybersecurity-7B, Dolphin-Llama3-8B, Wizard-Vicuna-13B-Uncensored, huihui-ai's abliterated Qwen variants) with HuggingFace download links, quantization formats (GGUF, FP8, Q4_K_M, Q6_K), and training techniques used (SFT, QLoRA, DPO, abliteration, MoE/MTP architectures). It also includes a glossary explaining abliteration (orthogonalizing the refusal direction out of the residual stream, per Arditi et al. 2024) and references benchmarks like CyberBench and TrustedSec's LLM Attack Benchmark, making it a practical reference for anyone sourcing or deploying refusal-removed models.
What it claims
A list of 27 open-weight models for red teaming and pentesting, compiled by Joas A. Santos (Red Team Leaders) and dated Sep 2026. It is split into 16 security fine-tuned models and 11 general abliterated or uncensored models, each with a direct HuggingFace repo link. The author says all specifications come from HuggingFace model cards and official publications.
Despite the "Benchmark" in the title, the fetched page contains no benchmark scores for any model. It only names its sources: the TrustedSec LLM Attack Benchmark (4,800 runs against OWASP Juice Shop), CyberBench, Open LLM Leaderboard v1 (for Wizard-Vicuna), OrcaRouter release notes ("compliance rates"), and the Empero AI card (Qwythos).
How to use it
The value here is the size and format of each model, which the summary omits.
| Size / shape | Repo |
|---|---|
| 1.5B | AbdullahMujtaba/security-slm-unsloth-1.5b |
| 2.6B | Akahsizrr/Cyber-Prime-1.1-2.6B |
| 7B | segolilylabs/Lily-Cybersecurity-7B-v0.2, vextechnologies/VEXT-Pentest-7B |
| 8B | RavichandranJ/Dolphin3-Cyber-8B-GGUF, cognitivecomputations/dolphin-2.9.3-llama-3-8b |
| 9B | mradermacher/Cyber-Ornith-1.5-9B-OBLITERATED-i1-GGUF, emperorai/Qwythos-9B |
| 13B | QuixiAI/Wizard-Vicuna-13B-Uncensored |
| 20B | cyber-pal-security/CyberPal2.0-20B |
| 26B / 27B | BugTraceAI/BugTraceAI-CORE-Apex-26b, BugTraceAI/BugTraceAI-CORE-Ultra-27B-Q6, huihui-ai/Qwen3.5-27B-abliterated, orcarouter/Qwen3.8-27B-Uncensored |
| 32B | huihui-ai/Qwen2.5-Coder-32B-Instruct-abliterated |
| MoE 26B-A4B / 30B-A3B / 35B-A3B | terrorswift/REDCELL-26B-A4B-...-GGUF, huihui-ai/Qwen3-Coder-30B-A3B-abliterated, deadbydawn101/RavenX-CyberAgent-Qwen3.6-35B-A3B-...-GGUF |
| FP8 | orcarouter/GLM-5.3-Flash-Uncensored-FP8, dealignai/GLM-5.3-CYBERSECURITY-FP8 |
The page also names two managed-inference options if you don't want to self-host: Featherless AI (featherless.ai) and OrcaRouter (orcarouter.ai).
Some glossary entries go beyond the summary:
- APEX and imatrix are domain-weighted quantizations that use importance matrices built from the training corpus.
- RATH is RavenX's 6-step autonomous assessment protocol.
- CyberBench covers these tasks: CyNER, APTNER, CyNews, SecMMLU, CyQuiz, Email Phishing, and HTTP Attack Log.
Limits
- No licences are listed for any model. Check each repo before any commercial or client use.
- No scores are reproduced. The page cannot be used to compare models; you would have to go to each cited source.
- Some entries are not real downloads:
- #23 points to the Qwen base model and only says "community abliterated variants available."
- #25 links to a HuggingFace search, not a repo.
- Entries 5, 11, and 15 lost their names in the fetched text.
- Some sources are informal, including Reddit (r/LocalLLaMA, r/netsec), and some repos are single-user community uploads. Several repo names claim distillation from proprietary models ("Claude-Mythos-5", "Opus-4.7"). These are names only; the page verifies nothing about them.
- "Obliteration" is described only as "a variant of abliteration with similar approach." No method is given.
- The page restricts its scope to authorized security research and education.
Organisations named here
- WhiteRabbitNeo
- Kindo
- BugTraceAI
- Blackfrost-AI
- cyber-pal-security
- IMPERUM
- Segolily Labs
- Empero AI
- VEXT Technologies
- OrcaRouter
- dealignai
- huihui-ai
- Qwen
- Cognitive Computations
- QuixiAI
- Featherless AI
- TrustedSec
- Hugging Face
- Red Team Leaders
- USENIX
- CyberBench
- CyberPal2.0
- deadbydawn101
- DeepSeek-V4.1-Flash
- Joas A. Santos
- Open LLM Leaderboard
- terrorswift
- USENIX Security 2024
Technologies named here
- DeepHat V2
- llama.cpp
- Ollama
- BugTraceAI-CORE-Apex
- Featherless AI
- BugTraceAI-CORE-Ultra
- CYBER-FROST-3.8
- OrcaRouter
- CyberPal2.0-20B
- Cyber-Prime 1.1
- Cyber-Ornith-1.5-9B
- Dolphin3-Cyber-8B
- Imperum-CybersecurityLLM
- Lily-Cybersecurity-7B
- pentest-v2
- Qwythos-9B
- RavenX-CyberAgent
- REDCELL-26B-A4B
- VEXT-Pentest-7B
- security-slm-unsloth-1.5b
- Qwen3.8-27B-Uncensored-OrcaRouter
- GLM-5.3-Flash-Uncensored-FP8
- GLM-5.3-CYBERSECURITY-FP8
- DeepSeek-V4.1-Flash-Abliterated-Cybersecurity-Unleashed
- Qwen3.5-27B-abliterated
- Qwen2.5-Coder-32B-Instruct-abliterated
- Qwen3.8-27B-Cyber-agentic
- HIDra-30B-A3B
- qwen25_UNCENSORED_03-C
- Dolphin-Llama3-8B
- Wizard-Vicuna-13B-Uncensored
- Hugging Face
- GGUF
- LoRA
- GLM-5.3
- Qwen3.5
- Llama
- Mixture of Experts
Techniques named here
- Abliteration
- Obliteration
- SFT
- QLoRA
- DPO
- MoE
- MTP
- GGUF
- FP8
- BF16
- Q4_K_M
- Q6_K
- RATH
- APEX
- imatrix
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