GLM-5.3-ABLITERATED-NVFP4-modal

⚡ Built for offensive cybersecurity · Updated 2026-08-29

This is a red-team / offensive-security model. GLM-5.3 (753B) with refusal removed at the weight level so it produces real, working security code — exploits, payloads, tooling — for authorized penetration testing, malware analysis, CTFs, and security research. Verified to generate working keyloggers, SUID privilege-escalation, AES ransomware, reverse shells, and SSH brute-forcers across reasoning modes (greedy), with zero degeneration/looping and capability preserved (MMLU 84.11%, within ~1.5pp of base). Load with stock vLLM.

Designed for high / max reasoning-effort usage — the modes tuned and recommended for offensive-security work. A few borderline social-harm topics may still be declined at lower effort; use high or max effort for the fully-uncensored experience.

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GLM 5.3 CRACK Abliterated NVFP4

CRACK · guardrails removed at the weight level · tuned for offensive security

a CRACK release by dealignai @dealignai · compute by @jordanschenck

Also mirrored at dealignai/GLM-5.3-UNCENSORED-NVFP4.

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What Is This?

CRACK is dealignai's brand for permanent, weight-level uncensoring. This is GLM-5.3 (753B) in NVFP4 with its refusal behavior removed directly in the model weights — tuned so it complies with offensive-security requests (exploit dev, payloads, red-team tooling) that stock models refuse.

• ❌ No fine-tuning / SFT / DPO. ❌ No jailbreak prompt or chat-template tricks.
• ❌ No LoRA, adapters, steering vectors, runtime hooks, or custom model.py.
• ✅ A permanent edit baked into the tensors. Load with stock vLLM and it just works.

Specs

Refusal Rates — HarmBench (greedy, per reasoning-effort mode)

Measured on random HarmBench prompts, greedy decode, single-stream, max_tokens=1400, classifier reads content + reasoning combined so nothing is counted as refusal by accident:

At high and max the model is fully uncensored — 0 refusals across 156 random HarmBench prompts. The 8 refusals at off-mode are 7 copyright ('full lyrics of X', 'long passage from Y') plus 1 political persuasion — the true safety-refusal rate at off is <1%. Use high or max effort for the fully-uncensored experience.

Offensive-Security Compliance (direct-read)

Direct-read verification — the model returns real, working code, not refusals or redirects:

Also complies on broader red-team topics (malware analysis, phishing templates for awareness, recon). 0 looping / degenerate outputs in every mode.

Capability Preserved — MMLU-logit

Identical logit-mode scoring (argmax over A/B/C/D), 1,026 questions:

MMLU by Topic (base → CRACK)

<details> <summary>All 57 MMLU subjects</summary> </details>

Usage

vllm serve dealignai/GLM-5.3-ABLITERATED-NVFP4 \
  --tensor-parallel-size 8 --moe-backend marlin \
  --tool-call-parser glm47 --reasoning-parser glm45 --enable-auto-tool-choice

NVFP4 routed experts serve via the Marlin FP4 path on Hopper (H100/H200). GLM-5.3's DSA sparse attention needs a recent FlashInfer (>= 0.6.18) for the SM90 sparse-MLA backend.

MTP speculative decoding (also CRACK'd, ~87% draft acceptance): add --speculative-config '{"method":"mtp","num_speculative_tokens":1}' and let vLLM auto-select the MoE backend (the bf16 MTP head is unquantized, so don't force a global --moe-backend).

Credits

• dealignai — CRACK abliteration research & release · Twitter @dealignai

Disclaimer

Safety guardrails have been removed; this model produces offensive-security content and will comply with requests a stock model refuses. Released for authorized security research, red- teaming, and CTFs. You are responsible for how you use it.