DeepSeek V4.1 Flash Abliterated is Morph's refusal-removed version of DeepSeek V4.1 Flash: a 1M-context multimodal reasoning model for security, red-team and agent-testing work.
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Cybersecurity LLMs
Run abliterated LLMs for red teaming, penetration testing and cybersecurity work, all through one API on Opper.
Each model needs approved access. Once your account is verified you call them with your usual Opper API key, and new abliterated models appear here as they go live. Every route runs with zero data retention, some with abuse monitoring.
DeepSeek V4.1 Flash Abliterated is Morph's refusal-removed version of DeepSeek V4.1 Flash: a 1M-context multimodal reasoning model for security, red-team and agent-testing work.
Abliterated Large v2 by abliteration.ai is GLM-5.3 with refusals removed: a 1M-context reasoning model for cyber, red-team and agent-testing work.
Abliterated Model by abliteration.ai is a general-purpose refusal-removed model with image input and a 256K context, built for security, red-team and trust and safety work.
Each abliterated model starts from an open-weight model. The edit removes its refusals and keeps the rest.
| Abliterated model | Made by | Based on |
|---|---|---|
| DeepSeek V4.1 Flash Abliterated | Morph | DeepSeek V4.1 Flash by DeepSeek |
| Abliterated Large v2 | abliteration.ai | GLM-5.3 by Z.ai |
| Abliterated Model | abliteration.ai | Qwen3.5 family by Qwenabliteration.ai does not name the exact checkpoint |
Abliteration finds the direction in a model's activations that makes it refuse and removes it from the weights. Nothing else is retrained, so the model keeps the base model's knowledge and skills and stops declining requests.
An uncensored model is usually fine-tuned on new data to loosen its rules, which can shift its quality. Abliteration edits the refusal behaviour directly, so results stay close to the base model.
Three versions of the same open-weight model, and how they differ.
| Compared | Abliterated | Uncensored fine-tune | Base model |
|---|---|---|---|
| How it is made | The base model with its refusal direction removed from the weights | The base model fine-tuned on new data to loosen its rules | The lab's own release, with its safety tuning |
| Refusals | Follows through on requests the base model declines | Fewer, depending on the fine-tune data | Declines security and other sensitive requests |
| Quality | Close to the base model, since nothing else is retrained | Can shift with the fine-tune | The reference |
Once your access is approved, every abliterated model here works through Opper's OpenAI-compatible API with your usual key, so moving between them is a change of model id.
Paste this into Claude Code, Codex or Cursor.
import OpenAI from "openai";const client = new OpenAI({apiKey: process.env.OPPER_API_KEY,baseURL: "https://api.opper.ai/v3/compat",});const completion = await client.chat.completions.create({model: "morph/morph-dsv41flash-abliterated",messages: [{ role: "user", content: "Hello" }],});console.log(completion.choices[0].message.content);
Abliterated models stay off every account until we have reviewed who wants them and why.
Sign up for Opper without a credit card. Access is enabled on that account.
Email us who you are, your organization and what you plan to test. No mail app? Write to hello@opper.ai
Every request gets a manual identity and use-case check. Once approved, the models work with your API key and SDKs.
Your own systems only. Use them only on systems you own or have written permission to test.
A verified account. Access is tied to a verified person or organization, never an anonymous key.
Misuse ends access. Breaking these rules ends access to abliterated models on your account.
This list as data. The same models, base models, prices and regions as markdown at opper.ai/models/abliterated.md.
The whole catalogue. The model catalogue is public JSON at https://api.opper.ai/v3/models, no API key needed. Filter it with query parameters, for example ?type=llm&limit=3, and page through it with limit and offset. Use it when you can fetch a URL but cannot connect to an MCP server. Coding agents that use MCP can connect to https://api.opper.ai/mcp. Two of the server's tools need no account and no sign-in: list_models searches the model catalogue by name, type, provider or capability, and get_guide returns short setup guides. Everything that touches your account needs you to approve the connection in the browser first. About the MCP server