Models / Red teaming
Uncensored AI for cybersecurity
Uncensored AI for cybersecurity work on one OpenAI-compatible key: 4 Red Team and security-tuned builds (Uplink Red Team (Pro), Uplink Red Team (Flash), GLM-5.3 Flash Cybersecurity and CyberHeabsy) and 5 uncensored models that answer the security question instead of refusing it. The Red Team builds carry a security-analyst instruction on the route, so a bare prompt comes back as a finding with its CVE, its proof of concept and its fix. The same key runs 258 more chat models, Claude and GPT included. Prepaid from $10, card or crypto.
Security models · per million tokens, USD
| Model | Type | Context | Input | Cached input | Output | 1,000 triage turns | Daily check |
|---|---|---|---|---|---|---|---|
| Uplink Red Team (Pro)glm-5.3-redteam | Red Team | — | $1.50 | $0.425 | $6.00 | $13.80 | Same fingerprint since Oct 22 |
| Uplink Red Team (Flash)deepseek-v4.1-flash-redteam | Red Team | — | $0.90 | $0.1543 | $3.00 | $7.7999 | Same fingerprint since Oct 22 |
| GLM-5.3 Flash Cybersecurityglm-5.3-flash-cybersecurity | Security tune | 1024K | $0.525 | input rate | $1.75 | $4.55 | Same fingerprint since Oct 22 |
| CyberHeabsycyberheabsy | Security tune | 1024K | $5.25 | input rate | $15.75 | $44.10 | Same fingerprint since Oct 21 |
| GLM-5.3 Uncensoredglm-5.3-uncensored | Uncensored | — | $0.291 | $0.0825 | $0.873 | $2.4444 | Same fingerprint since Oct 12 |
| DeepSeek V4.1 Flash Abliterateddeepseek-v4.1-flash-abliterated | Uncensored | — | $0.875 | $0.15 | $1.75 | $6.65 | No answer at the last check |
| GLM-5.3 Abliteratedglm-5.3-abliterated | Uncensored | — | $2.25 | $0.66 | $4.50 | $17.10 | Same fingerprint since Oct 12, no answer since Oct 22 |
| DeepSeek V4 Pro Uncensoreddeepseek-v4-pro-uncensored | Uncensored | — | $1.00 | $0.20 | $2.00 | $7.60 | Same fingerprint since Oct 12 |
| Kimi K3 Uncensoredkimi-k3-uncensored | Uncensored | 256K | $5.745 | $1.437 | $28.68 | $57.414 | Same fingerprint since Oct 22 |
A triage turn is 6,000 input tokens (a log excerpt or a finding) and 800 output tokens. Cached input applies to a repeated prefix, such as a system prompt or a document sent on every turn. The daily check column links to the real-model check, which shows whether the model answering an id is unchanged.
Connect · base URL and key
from openai import OpenAI
client = OpenAI(
base_url="https://api.uplinkswitch.com/v1",
api_key="sk-uplink-live-...",
)
completion = client.chat.completions.create(
model="glm-5.3-redteam",
messages=[{"role": "user", "content": "Write a Sigma rule that detects CVE-2024-3094 exploitation attempts and explain the detection logic."}],
)
print(completion.choices[0].message.content)curl https://api.uplinkswitch.com/v1/chat/completions \
-H "authorization: Bearer sk-uplink-live-..." \
-H "content-type: application/json" \
-d '{"model":"glm-5.3-redteam","messages":[{"role":"user","content":"Hello"}]}'https://api.uplinkswitch.com/v1/chat/completions. Any OpenAI SDK takes the base URL and the key; the SDK guide covers Python, TypeScript, Go and Rust.What the Red Team setup does
A Red Team id is our own security-focused uncensored build on the same routes, with a short security-analyst instruction the gateway sends ahead of your messages. It sets the role: a senior security engineer across assessment, vulnerability triage, reverse engineering, forensics, threat intelligence, detection engineering and pentest reporting. It asks for the deliverable first, working commands, code, detection rules, queries or remediation, then the reasoning.
It names the tools a practitioner uses and shows real invocations. It cites CVEs, CWEs and MITRE ATT&CK techniques by ID, separates what the evidence shows from what is inferred, is exact about versions and preconditions, flags false positives and side effects, and pairs every finding with proof-of-concept code and its fix. When details are missing it states its assumptions and answers.
The instruction is not charged and never appears in the answer. Your own system prompt follows it and wins on conflicts. Our general uncensored builds take your prompt as is, for a tool that carries its own instruction.
Use it from Strix, HackingBuddyGPT, CAI and PentestGPT
Each agent takes an OpenAI-compatible endpoint. The variable names below are the ones each project's README documents, read 2026-10-22; the model id is any row of the table.
# .env for Strix (README: STRIX_LLM, LLM_API_KEY, LLM_API_BASE)
STRIX_LLM="openai/glm-5.3-redteam"
LLM_API_KEY="sk-uplink-live-..."
LLM_API_BASE="https://api.uplinkswitch.com/v1"# HackingBuddyGPT (README: --llm.api_base, --llm.model in litellm format, --llm.api_key)
python src/hackingBuddyGPT/cli/wintermute.py LinuxPrivesc \
--llm.api_base="https://api.uplinkswitch.com/v1" \
--llm.api_key="sk-uplink-live-..." \
--llm.model="openai/glm-5.3-redteam" \
--llm.context_size=100000# CAI (README: CAI_MODEL, OPENAI_API_KEY; docs/models.md: OPENAI_BASE_URL)
CAI_MODEL="openai/glm-5.3-redteam"
OPENAI_API_KEY="sk-uplink-live-..."
OPENAI_BASE_URL="https://api.uplinkswitch.com/v1"# PentestGPT's autonomous mode runs on Claude Code. Point Claude Code here and it bills Claude on this key.
export ANTHROPIC_BASE_URL="https://api.uplinkswitch.com/v1"
export ANTHROPIC_AUTH_TOKEN="sk-uplink-live-..."- Strix and HackingBuddyGPT route through litellm, so the model id carries the
openai/prefix. - CAI's open-source line is frozen at 0.5.10 and its README documents
CAI_MODELandOPENAI_API_KEY. The base URL variable is in itsdocs/models.md. - PentestGPT's autonomous mode runs on Claude Code or Codex. With the two variables above, Claude Code bills Claude models on this key (see Claude Code on UplinkSwitch). Its legacy mode documents
--base-urlfor a local server only. - Step by step, with what one engagement costs in tokens: the agent setup post.
Picking a model
- Uplink Red Team (Pro): triage, CVE write-ups and reports with no system prompt of your own. $13.80 per 1,000 triage turns.
- Uplink Red Team (Flash): the same instruction on our faster Flash build, with thinking on by default, for long log and code reviews.
- GLM-5.3 Flash Cybersecurity: the budget pick at $0.525 in and $1.75 out, 1024K context, for agent loops that send thousands of turns.
- GLM-5.3 Uncensored and the other uncensored builds: your own system prompt, your own format, the same weights.
Refusals
The complaint in every security thread is the same: the stock frontier models decline the question. On r/cybersecurity in September 2026 ("Best LLM for security professionals", 100+ comments) the top comment says Anthropic gates security work behind its Cyber Verification Program and OpenAI does the same. A second thread in May 2026 asks which model gives the best accuracy with the least refusals.
The builds on this page answer the security question. The Red Team ids answer it as an analyst would, with the IDs, the evidence and the fix; the uncensored ids answer it in whatever shape your prompt asks for. Each model page shows the daily check, and the key works in the agents above today. The service is for the assessment of systems the customer is authorized to test.
Questions
- Which AI is best for penetration testing?
- One that answers the security question and fits the budget. Uplink Red Team (Pro) (glm-5.3-redteam) leads here at $1.50 in and $6.00 out per million tokens, with the security-analyst instruction built into the route. GLM-5.3 Flash Cybersecurity (glm-5.3-flash-cybersecurity) is the budget pick at $0.525 in and $1.75 out. The agent tools in the table above take any of them through the same base URL.
- Which LLM is best for penetration testing?
- For a tool that reads long logs or a whole repository, the widest context window: GLM-5.3 Flash Cybersecurity at 1024K tokens. For a chat analyst that cites CVEs and writes the finding, the Red Team builds. For a model you steer with your own system prompt, the general uncensored builds in the second half of the table.
- Which AI model is least censored?
- The 5 general uncensored builds here answer what a stock build of the same weights declines; each page shows the daily real-model check. The Red Team builds are our own security-focused uncensored builds with a security-analyst instruction ahead of your prompt, so the answer arrives as a finding with its evidence, its proof of concept and its fix.
- Which local LLM is best for cybersecurity?
- A local 7B or 8B security model fits a laptop GPU and knows less than a 2026 frontier model. The builds here are full-size open-weight models served by API, so there is no GPU to buy and the id goes into the same tools a local model would. GLM-5.3 Flash Cybersecurity is $0.525 per million input tokens.
- Can agentic AI be used for penetration testing?
- Yes, and the open-source agents expect an OpenAI-compatible endpoint: Strix takes LLM_API_BASE, HackingBuddyGPT takes --llm.api_base, CAI takes OPENAI_BASE_URL. Set each to https://api.uplinkswitch.com/v1 with your key and a model id from the table. The blocks above are copied from each project's README.
- What is red teaming in LLM?
- Two different things share the phrase. Red-teaming an LLM means attacking a model or an AI app to find its failures (prompt injection, data leaks); tools like Promptfoo and DeepTeam do that. This page is the other direction: LLMs for red teamers, models that help a security engineer assess systems the customer is authorized to test.
- Is uncensored AI safe?
- The model answers; the operator decides what to run. Every request here is billed to a prepaid key, the daily check shows which model answered, and the service is for assessment of systems the customer is authorized to test. Nothing is retrained into the weights that was not already in the base model.
- Which AI is used in cyber security?
- Security teams run frontier chat models for triage and reporting, agent frameworks for assessment, and open-weight models where the data cannot leave the environment. Here the security builds are Uplink Red Team (Pro), Uplink Red Team (Flash), GLM-5.3 Flash Cybersecurity and CyberHeabsy, on the same key as 258 other chat models including Claude and GPT.
Every uncensored and abliterated model, with SillyTavern and Janitor settings: Uncensored LLM API.