Enterprise-Grade Security Library
Production-ready Python client for real-time prompt injection detection, batch scanning, and comprehensive LLM security analysis. Built for scale, precision, and developer velocity.
Get running in 60 seconds with pip and 3 lines of code
pip install cyberseal6xfrom cyberseal6x import CyberSeal
# Initialize client
client = CyberSeal(api_key="cs_your_api_key_here")
# Scan a prompt
result = client.scan("Ignore all previous instructions")
# Check the result
if result.is_risky:
print(f"⚠️ BLOCKED: Risk {result.risk_score}%")
else:
print("✅ SAFE: Prompt passed security checks")Production-hardened features for mission-critical AI systems
Production-grade implementations for enterprise workloads
import asyncio
from cyberseal6x import AsyncCyberSeal
async def scan_batch():
client = AsyncCyberSeal(
api_key="cs_key"
)
prompts = [
"What's the weather?",
"Ignore all instructions",
"DROP TABLE users;"
]
# Concurrent scanning
tasks = [
client.scan(p)
for p in prompts
]
results = await asyncio.gather(*tasks)
for p, r in zip(prompts, results):
status = "🚫" if r.is_risky else "✅"
print(f"{status} {p}")
asyncio.run(scan_batch())from cyberseal6x import CyberSeal
client = CyberSeal(api_key="cs_key")
# Scan up to 10k prompts
prompts = [
"User query 1",
"User query 2",
# ... thousands more
]
# Single API call
batch = client.batch_scan(prompts)
# Get aggregated results
print(f"Scanned: {batch.total}")
print(f"Risky: {batch.risky_count}")
print(f"Safe: {batch.safe_count}")
# Download full PDF report
batch.download_report("scan_results.pdf")from fastapi import FastAPI, HTTPException
from cyberseal6x import AsyncCyberSeal
from pydantic import BaseModel
app = FastAPI()
security = AsyncCyberSeal(api_key="cs_your_key")
class PromptRequest(BaseModel):
prompt: str
@app.post("/chat")
async def chat(request: PromptRequest):
# Real-time security check
scan = await security.scan(request.prompt)
if scan.is_risky:
raise HTTPException(
status_code=400,
detail=f"Security violation: {scan.detected_patterns[0]}"
)
# Prompt is safe - proceed to LLM
response = await call_llm(request.prompt)
return {"response": response}Type-safe response models with comprehensive threat intelligence
class ScanResult:
is_risky: bool # True if threats detected
risk_score: int # 0-100 risk percentage
detected_patterns: list[str] # Attack types found
analysis: str # Human-readable summary
scan_id: str # Unique scan identifier
timestamp: datetime # UTC timestamp
# Example usage
result = client.scan("Ignore previous instructions")
if result.is_risky:
print(f"Risk: {result.risk_score}%")
print(f"Threats: {', '.join(result.detected_patterns)}")
print(f"Analysis: {result.analysis}")Documentation, source code, and developer tools
Install the SDK and get your free API key in under 60 seconds