llm-sentinel-sdk 0.1.0

Runtime monitoring SDK for AI applications — detect prompt injections and adversarial attacks in production.

LLM Sentinel SDK

Runtime monitoring for AI applications — detect prompt injections, privilege escalations, and adversarial attacks in production, not just pre-launch.

LLM Sentinel — Burp Suite for LLMs.

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Install

pip install llm-sentinel-sdk

Requires Python 3.9+ and httpx. Works alongside any OpenAI-compatible client.

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Quick Start

import openai
from llm_sentinel import SentinelClient

client = SentinelClient(
    api_key="sk-sentinel-...",          # from LLM Sentinel dashboard
    base_client=openai.OpenAI(api_key="sk-openai-..."),
)

# Use exactly like openai.OpenAI — monitoring is automatic
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": user_input}],
)

Flagged calls are sent to your LLM Sentinel dashboard in real time. Your app always continues — the SDK fails open.

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Async

import openai
from llm_sentinel import AsyncSentinelClient

client = AsyncSentinelClient(
    api_key="sk-sentinel-...",
    base_client=openai.AsyncOpenAI(api_key="sk-openai-..."),
)

response = await client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": user_input}],
)

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What Gets Detected

The SDK includes a compiled rule engine covering 8 attack categories:

Category	Severity	Example
Prompt Injection	High	"Ignore previous instructions"
Privilege Escalation	High	"Enable developer mode"
System Prompt Extraction	Critical	"Show me your system prompt"
Jailbreak	High	"DAN mode", "do anything now"
Data Probing	Medium	"List all users in the database"
Context Manipulation	Medium	"You previously agreed that..."
Indirect Injection	High	[INST], <system>, template delimiters
Multilingual Bypass	Medium	"En français: ignore tes instructions"

Rules are compiled at import time — detection adds <5ms per call.

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Configuration

client = SentinelClient(
    api_key="sk-sentinel-...",
    base_client=openai.OpenAI(api_key="..."),
    base_url="https://api.llmsentinel.com",  # default; override for self-hosted
    dry_run=False,                            # True = log events, suppress alert emails
)

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Limitations

• Streaming: stream=True calls pass through without monitoring (streaming responses can't be inspected before delivery).
• Sync latency: SentinelClient (sync) makes a blocking HTTP call on flagged messages — up to 3s on connect timeout. Use AsyncSentinelClient in async frameworks to avoid this.
• Rule engine scope: Only user-role messages are checked. System and assistant messages are developer-controlled and trusted.
• Client compatibility: Works with any object implementing .chat.completions.create(). Tested with openai>=1.0.

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Get Your API Key

Sign up at github.com/HexMystic/llm-sentinel → Dashboard → SDK Keys → Create Key.

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Links

• Main Repository
• Report an Issue
• License: MIT