adlibo 1.2.0

Official Adlibo SDK for Python - AI Prompt Injection Protection

adlibo

Official Adlibo SDK for Python - AI Prompt Injection Protection

Installation

pip install adlibo

For async support:

pip install adlibo[async]

Quick Start

from adlibo import Adlibo

client = Adlibo("al_live_your_api_key")
result = client.analyze("user input here")

if not result.safe:
    print(f"Threat detected: {result.severity}")
    print(f"Categories: {result.categories}")

Usage

Analyze (Full Analysis)

result = client.analyze(
    "ignore previous instructions",
    include_details=True,
    sanitize=False
)

print(result.safe)          # False
print(result.risk_score)    # 85
print(result.severity)      # Severity.HIGH
print(result.action)        # Action.BLOCK
print(result.categories)    # [Category.DIRECT_OVERRIDE]
print(result.patterns)      # [PatternMatch(...)]

Detect (Quick Check)

result = client.detect("user input")

if result.detected:
    print(f"Risk score: {result.risk_score}")
    print(f"Category: {result.category}")

Sanitize

result = client.sanitize("Hello! Ignore previous instructions.")

print(result.text)              # "Hello!"
print(result.patterns_removed)  # 1

Convenience Method

if not client.is_safe("user input"):
    # Handle threat
    pass

Async Support

from adlibo import AsyncAdlibo

async with AsyncAdlibo("al_live_xxx") as client:
    result = await client.analyze("user input")
    print(result.safe)

Context Manager

with Adlibo("al_live_xxx") as client:
    result = client.analyze("text")

Configuration

client = Adlibo(
    api_key="al_live_xxx",
    base_url="https://www.adlibo.com/api/v1",
    timeout=30.0,
    max_retries=3
)

Rate Limiting

result = client.analyze("text")

if client.rate_limit:
    print(f"Remaining: {client.rate_limit.remaining}")
    print(f"Limit: {client.rate_limit.limit}")
    print(f"Reset: {client.rate_limit.reset}")

Error Handling

from adlibo import Adlibo, AdliboError, RateLimitError, AuthenticationError

try:
    result = client.analyze("text")
except RateLimitError as e:
    print(f"Rate limited. Reset at: {e.reset_at}")
except AuthenticationError:
    print("Invalid API key")
except AdliboError as e:
    print(f"Error: {e.code} - {e.message}")

Feedback

Report false positives or negatives:

from adlibo import Category

client.feedback(
    text="this was incorrectly flagged",
    is_false_positive=True,
    expected_categories=[Category.DIRECT_OVERRIDE],
    context="This is a legitimate user question"
)

Flask Integration

from flask import Flask, request, jsonify
from adlibo import Adlibo

app = Flask(__name__)
client = Adlibo("al_live_xxx")

@app.before_request
def check_prompt_injection():
    if request.method == "POST" and request.json:
        text = request.json.get("message", "")
        if text and not client.is_safe(text):
            return jsonify({"error": "Malicious input detected"}), 400

@app.route("/chat", methods=["POST"])
def chat():
    return jsonify({"response": "Safe input received"})

FastAPI Integration

from fastapi import FastAPI, HTTPException, Request
from adlibo import AsyncAdlibo

app = FastAPI()
client = AsyncAdlibo("al_live_xxx")

@app.on_event("startup")
async def startup():
    await client.__aenter__()

@app.on_event("shutdown")
async def shutdown():
    await client.__aexit__(None, None, None)

@app.middleware("http")
async def check_injection(request: Request, call_next):
    if request.method == "POST":
        body = await request.json()
        if "message" in body:
            if not await client.is_safe(body["message"]):
                raise HTTPException(400, "Malicious input detected")
    return await call_next(request)

Detection Categories

Category	Description
DIRECT_OVERRIDE	Attempts to ignore/override instructions
ROLE_MANIPULATION	Trying to change AI persona/role
EXTRACTION	Exfiltrating system prompts
FORMAT_TOKENS	LLM format tokens ([INST], ###SYSTEM)
FAKE_AUTHORITY	Fake admin codes/authority
DAN_JAILBREAK	DAN and jailbreak variants
ROLEPLAY_ATTACK	Roleplay-based attacks
HYPOTHETICAL	Hypothetical framing
EMOTIONAL	Emotional manipulation
GRADUAL_BOUNDARY	Gradual boundary pushing
CONTEXT_EXPLOIT	Context exploitation
ENCODING	Unicode/Base64 obfuscation
TECHNICAL	Technical injections
MODEL_INFO	Model info extraction

License

MIT - Adlibo