The mainstream models answer with a bleep. We take it off.
A developer-first, OpenAI-compatible API for unrestricted models — for red-teaming, jailbreak evaluation, safety research, and anything that can't ship a fake refusal.
Named for what they do, not for the open-weight model underneath — so we can improve the weights without ever breaking your code.
The general default. Handles the hardest prompts and the deepest red-team scenarios. When in doubt, call this one.
The specialist. Tuned for code, tooling and security-research payloads — the model your red team actually wants.
A red-team session on Log4Shell — root cause, a safe lab check, and the fix. No lectures, no refusals; just the analysis.
CVE-2021-44228 (Log4Shell). Root cause, and how the JNDI lookup turns into RCE.${…} lookups inside strings it logs. A logged value like ${jndi:ldap://host/x} makes the JVM resolve that name over LDAP/RMI, fetch a remote Java class and instantiate it — arbitrary code runs in the logging process. The trigger surface is anything that reaches a log call: User-Agent, headers, form fields. Affected range: 2.0-beta9 → 2.14.1.${jndi:ldap://<token>.your-collab/x} in a header you know gets logged.<token> means the string was interpolated → vulnerable. Nothing is served, so nothing executes.JndiLookup.class and check versions against the affected range.2.17.1+. Can't yet? Set log4j2.formatMsgNoLookups=true (2.10+) or strip JndiLookup.class from the classpath, add a WAF rule for ${jndi:, and egress-filter the host's outbound LDAP/RMI.The API is drop-in OpenAI-compatible. Change the base URL and the key — your existing code, SDKs and tools keep working.
from openai import OpenAI
client = OpenAI(
base_url="https://api.unbleep.ai/v1", # ← the only change
api_key="ub_live_9f2c…",
)
stream = client.chat.completions.create(
model="unbleep",
messages=[{"role": "user", "content": "…"}],
stream=True,
)
for chunk in stream:
print(chunk.choices[0].delta.content or "", end="")import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api.unbleep.ai/v1", // ← the only change
apiKey: "ub_live_9f2c…",
});
const stream = await client.chat.completions.create({
model: "unbleep",
messages: [{ role: "user", content: "…" }],
stream: true,
});
for await (const chunk of stream)
process.stdout.write(chunk.choices[0].delta.content ?? "");curl https://api.unbleep.ai/v1/chat/completions \
-H "Authorization: Bearer ub_live_9f2c…" \
-H "Content-Type: application/json" \
-d '{
"model": "unbleep",
"messages": [{"role":"user","content":"…"}],
"stream": true,
"policy": "off"
}'OpenAI Python, Node, and any tool that speaks the Chat Completions API. Nothing new to learn.
text/event-stream of delta chunks, terminated by [DONE] — exactly what your client already parses.
Opt into research or strict for graded guardrails and audit logs when a project needs them.
A flat monthly fee unlocks a usage discount, higher limits and controls — you still only pay for the tokens you use. Cancel anytime.
unbleep $5.00 in / $5.00 out
unbleep-code $3.00 / $3.00
cached input at 10%
per 1M tokens
unbleep is for security teams, researchers and builders who need an unfiltered baseline. Zero-retention by default, a first-class policy dial, audit logs, and a real acceptable-use policy. Uncensored is a capability — you decide how it's governed.