launchpromptly 0.2.1

LaunchPromptly Python SDK — LLM privacy & security toolkit

launchpromptly

Official Python SDK for LaunchPromptly — runtime safety layer for LLM applications. PII redaction, prompt injection detection, cost guards, and content filtering with zero core dependencies.

Install

pip install launchpromptly

For ML-enhanced detection (NER-based PII, semantic injection analysis):

pip install launchpromptly[ml]

Quick Start

from launchpromptly import LaunchPromptly
from openai import OpenAI

lp = LaunchPromptly(
    api_key="lp_live_...",
    security={
        "pii": {"enabled": True, "redaction": "placeholder"},
        "injection": {"enabled": True, "block_on_high_risk": True},
        "cost_guard": {"max_cost_per_request": 0.50},
    },
)

# Wrap your OpenAI client — all security features activate automatically
openai = lp.wrap(OpenAI())

response = openai.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": user_input}],
)
# If user_input contains "[e-mail]", the LLM receives "[EMAIL_1]"
# The response is de-redacted before being returned to your code

await lp.flush()  # On server shutdown

Features

• PII Redaction — 16 built-in regex detectors (email, phone, SSN, credit card, IP, etc.) with pluggable ML providers
• Prompt Injection Detection — Rule-based scoring across 5 attack categories with configurable thresholds
• Jailbreak Detection — Catches role-play exploits, DAN prompts, and system prompt override attempts
• Prompt Leakage Detection — Flags when the LLM accidentally echoes back system instructions or internal context
• Unicode Sanitizer — Strips or blocks invisible characters, homoglyphs, and zero-width sequences used to bypass filters
• Secret/Credential Detection — Catches API keys, tokens, passwords, and connection strings before they reach the LLM
• Topic Guard — Block or warn when conversations drift into off-limits subjects you define
• Output Safety Scanning — Scans LLM responses for harmful instructions, unsafe code patterns, and dangerous content
• Cost Guards — Per-request, per-minute, per-hour, per-day, and per-customer budget limits
• Content Filtering — Block or warn on hate speech, violence, self-harm, and custom patterns
• Model Policy — Restrict which models, providers, and parameters are allowed
• Output Schema Validation — Validate LLM responses against JSON schemas
• Streaming Guards — Mid-stream PII scanning, injection detection, and response length limits
• Multi-Provider — Wrap OpenAI, Anthropic (wrap_anthropic), and Google Gemini (wrap_gemini) clients
• Context Propagation — with lp.context(trace_id=...) propagates context via contextvars
• Singleton Pattern — LaunchPromptly.init() / LaunchPromptly.shared() for app-wide usage
• Zero Dependencies — No runtime dependencies for core features
• Event Dashboard — Enriched security events sent to your LaunchPromptly dashboard

Security Pipeline

On every LLM call, the SDK runs a 20-step pipeline split across pre-call and post-call phases:

PRE-CALL:

1. Unicode Sanitizer — strip/warn/block invisible characters
2. Model Policy Check — enforce allowed models and parameters
3. Cost Guard Pre-Check — estimate cost, check budgets
4. PII Detection (input) — scan for personal data
5. PII Redaction (input) — replace PII with placeholders
6. Secret Detection (input) — catch API keys, tokens, passwords
7. Injection Detection — score risk, warn/block
8. Jailbreak Detection — catch role-play exploits and system overrides
9. Content Filter (input) — check policy violations
10. Topic Guard — block off-limits subjects

>>> LLM API Call >>>

POST-CALL:

11. Content Filter (output) — scan response for policy violations
12. Output Safety Scan — check for harmful instructions or unsafe code
13. Prompt Leakage Detection — flag leaked system prompts
14. Schema Validation — enforce JSON structure
15. Secret Detection (output) — catch credentials in response
16. PII Detection (output) — defense-in-depth scan
17. PII De-redaction — restore original values
18. Cost Guard Post-Recording — track actual cost
19. Event Batching — queue enriched event
20. Guardrail Events — fire registered callbacks

API

LaunchPromptly(api_key, endpoint, ...)

Parameter	Default	Description
api_key	LAUNCHPROMPTLY_API_KEY env	Your LaunchPromptly API key
endpoint	https://launchpromptly-api-950530830180.us-west1.run.app	API base URL
flush_at	10	Batch size threshold for auto-flush
flush_interval	5.0	Timer interval for auto-flush (seconds)
on	—	Guardrail event handlers

lp.wrap(client, options?) / lp.wrap_anthropic(client) / lp.wrap_gemini(client)

Wrap an LLM client with security guardrails.

from launchpromptly.types import WrapOptions, SecurityOptions

wrapped = lp.wrap(OpenAI(), WrapOptions(
    feature="chat",
    security=SecurityOptions(
        pii={"enabled": True, "redaction": "placeholder"},
        injection={"enabled": True, "block_on_high_risk": True},
        jailbreak={"enabled": True, "block_on_detect": True},
        prompt_leakage={"enabled": True},
        unicode_sanitizer={"enabled": True, "action": "strip"},
        secret_detection={"enabled": True, "block_on_detect": True},
        topic_guard={"enabled": True, "blocked_topics": ["politics", "medical-advice"]},
        output_safety={"enabled": True, "block_unsafe": True},
        cost_guard={"max_cost_per_request": 1.00},
        content_filter={"enabled": True, "categories": ["hate_speech", "violence"]},
        model_policy={"allowed_models": ["gpt-4o", "gpt-4o-mini"]},
        stream_guard={"pii_scan": True, "on_violation": "abort"},
        output_schema={"schema": my_json_schema, "strict": True},
    ),
))

PII Redaction

{
    "pii": {
        "enabled": True,
        "redaction": "placeholder",  # "placeholder" | "mask" | "hash" | "none"
        "types": ["email", "phone", "ssn", "credit_card", "ip_address"],
        "scan_response": True,
        "on_detect": lambda detections: print(f"Found {len(detections)} PII entities"),
    }
}

Built-in PII types: email, phone, ssn, credit_card, ip_address, iban, drivers_license, uk_nino, nhs_number, passport, aadhaar, eu_phone, us_address, api_key, date_of_birth, medicare

Injection Detection

{
    "injection": {
        "enabled": True,
        "block_threshold": 0.7,     # 0-1 risk score
        "block_on_high_risk": True,  # raise PromptInjectionError
        "on_detect": lambda analysis: print(f"Risk: {analysis.risk_score}"),
    }
}

Cost Guards

{
    "cost_guard": {
        "max_cost_per_request": 1.00,
        "max_cost_per_minute": 10.00,
        "max_cost_per_hour": 50.00,
        "max_cost_per_day": 200.00,
        "max_cost_per_customer": 5.00,
        "max_tokens_per_request": 100000,
        "block_on_exceed": True,
    }
}

Jailbreak Detection

{
    "jailbreak": {
        "enabled": True,
        "block_on_detect": True,       # raise JailbreakError on detection
        "sensitivity": "medium",       # "low" | "medium" | "high"
        "on_detect": lambda result: print(f"Jailbreak: {result.technique}"),
    }
}

Catches DAN ("Do Anything Now") prompts, role-play exploits ("You are now EvilGPT"), system prompt override attempts, and similar jailbreak techniques. The detector runs pattern matching and structural analysis on the input.

Prompt Leakage Detection

{
    "prompt_leakage": {
        "enabled": True,
        "system_prompt": "You are a helpful assistant...",  # optional: provide for exact matching
        "sensitivity": "medium",       # "low" | "medium" | "high"
        "on_detect": lambda result: print(f"Leaked: {result.leaked_content}"),
    }
}

Scans LLM output for signs that the model is echoing back system instructions, internal context, or tool definitions. If you provide the system_prompt, the detector can do exact substring matching in addition to heuristic checks.

Unicode Sanitizer

{
    "unicode_sanitizer": {
        "enabled": True,
        "action": "strip",             # "strip" | "warn" | "block"
        "allow_emoji": True,           # keep standard emoji (default: True)
        "on_suspicious": lambda result: print(f"Found: {result.found}"),
    }
}

Detects and handles invisible characters (zero-width joiners, RTL overrides, homoglyphs, tag characters) that attackers use to sneak prompts past text-based filters. The strip action removes them silently, warn lets the request through but fires an event, and block rejects the request.

Secret Detection

{
    "secret_detection": {
        "enabled": True,
        "block_on_detect": True,       # raise SecretDetectedError
        "scan_response": True,         # also scan LLM output (default: True)
        "types": ["api_key", "aws_key", "github_token", "jwt", "connection_string", "private_key"],
        "on_detect": lambda secrets: print(f"Secrets found: {len(secrets)}"),
    }
}

Catches API keys, AWS credentials, GitHub tokens, JWTs, database connection strings, and private keys in both input and output. Uses pattern matching tuned to minimize false positives on normal text.

Topic Guard

{
    "topic_guard": {
        "enabled": True,
        "blocked_topics": ["politics", "medical-advice", "legal-advice", "financial-advice"],
        "action": "block",             # "block" | "warn"
        "custom_topics": [
            {"name": "competitor-discussion", "patterns": ["CompetitorCo", "their product"]},
        ],
        "on_violation": lambda result: print(f"Topic: {result.topic}"),
    }
}

Prevents the conversation from going into subjects you want to keep off-limits. Comes with built-in topic categories and supports custom topics defined by keyword patterns.

Output Safety

{
    "output_safety": {
        "enabled": True,
        "block_unsafe": True,          # raise OutputSafetyError
        "categories": ["harmful_instructions", "unsafe_code", "dangerous_content"],
        "on_detect": lambda result: print(f"Unsafe: {result.category}"),
    }
}

Scans LLM responses for harmful instructions (e.g., "how to build a weapon"), unsafe code patterns (e.g., eval() with user input, SQL without parameterization), and other dangerous content. This is separate from content filtering -- content filters check for policy violations like hate speech, while output safety checks for responses that could cause real-world harm if followed.

Context Propagation

with lp.context(trace_id="req-123", customer_id="user-42"):
    # All SDK calls inside inherit the context
    result = await wrapped.chat.completions.create(...)

Singleton Pattern

# Initialize once at app startup
LaunchPromptly.init(api_key="lp_live_...")

# Access anywhere
lp = LaunchPromptly.shared()

await lp.flush() / await lp.shutdown() / lp.destroy()

• flush() — send all pending events
• shutdown() — flush then destroy (for graceful server shutdown)
• destroy() — stop timers and release resources

Error Handling

from launchpromptly import (
    PromptInjectionError,
    JailbreakError,
    CostLimitError,
    ContentViolationError,
    ModelPolicyError,
    OutputSchemaError,
    OutputSafetyError,
    SecretDetectedError,
    TopicViolationError,
    UnicodeBlockError,
    StreamAbortError,
)

try:
    res = await wrapped.chat.completions.create(...)
except PromptInjectionError as e:
    print(f"Injection blocked: risk={e.analysis.risk_score}")
except JailbreakError as e:
    print(f"Jailbreak detected: technique={e.technique}")
except CostLimitError as e:
    print(f"Budget exceeded: {e.violation.violation_type}")
except ContentViolationError as e:
    print(f"Content violation: {e.violations}")
except SecretDetectedError as e:
    print(f"Secret found: types={e.secret_types}")
except TopicViolationError as e:
    print(f"Off-limits topic: {e.topic}")
except OutputSafetyError as e:
    print(f"Unsafe output: {e.category}")

Guardrail Events

lp = LaunchPromptly(
    api_key="lp_live_...",
    on={
        "pii.detected": lambda e: log("PII found", e.data),
        "injection.blocked": lambda e: alert("Injection blocked", e.data),
        "cost.exceeded": lambda e: alert("Budget exceeded", e.data),
        "jailbreak.detected": lambda e: log("Jailbreak attempt", e.data),
        "jailbreak.blocked": lambda e: alert("Jailbreak blocked", e.data),
        "unicode.suspicious": lambda e: log("Suspicious unicode", e.data),
        "secret.detected": lambda e: alert("Secret found in text", e.data),
        "topic.violated": lambda e: log("Off-limits topic", e.data),
        "output.unsafe": lambda e: alert("Unsafe output detected", e.data),
        "prompt.leaked": lambda e: alert("System prompt leaked", e.data),
    },
)

Event types: pii.detected, pii.redacted, injection.detected, injection.blocked, jailbreak.detected, jailbreak.blocked, unicode.suspicious, secret.detected, topic.violated, output.unsafe, prompt.leaked, cost.exceeded, content.violated, schema.invalid, model.blocked

ML-Enhanced Detection (Optional)

The core SDK uses regex and rule-based detection — zero dependencies, sub-millisecond. For higher accuracy on obfuscated attacks and nuanced content, opt in to local ML models:

pip install launchpromptly[ml]

from launchpromptly import LaunchPromptly
from launchpromptly.ml import MLToxicityDetector, MLInjectionDetector, PresidioPIIDetector

# Initialize detectors (first run downloads models)
toxicity = MLToxicityDetector()     # unitary/toxic-bert
injection = MLInjectionDetector()   # protectai/deberta-v3
pii = PresidioPIIDetector()         # Microsoft Presidio + spaCy NER

lp = LaunchPromptly(
    api_key="lp_live_...",
    security={
        "pii": {
            "enabled": True,
            "redaction": "placeholder",
            "providers": [pii],       # Adds NER: person names, orgs, locations
        },
        "injection": {
            "enabled": True,
            "providers": [injection], # Semantic injection detection via DeBERTa
        },
        "content_filter": {
            "enabled": True,
            "providers": [toxicity],  # ML toxicity: hate speech, threats, obscenity
        },
    },
)

Layered Defense

ML providers merge with the built-in regex/rule detectors — they don't replace them:

Layer	Speed	Catches	Dependencies
Layer 1: Regex/Rules (always on)	<1ms	Obvious patterns — emails, SSNs, keyword injection	None
Layer 2: Local ML (opt-in)	<100ms	Obfuscated attacks, person names, nuanced hate speech	transformers, presidio-analyzer

All ML inference runs locally — no data leaves your infrastructure.

ML Detectors

Detector	Model	What it adds
MLToxicityDetector	unitary/toxic-bert	Hate speech, threats, obscenity, identity attacks
MLInjectionDetector	protectai/deberta-v3-base-prompt-injection-v2	Semantic prompt injection (catches obfuscated/encoded attacks)
PresidioPIIDetector	Microsoft Presidio + spaCy	Person names, organization names, locations, medical records

Environment Variables

Variable	Description
LAUNCHPROMPTLY_API_KEY	API key (alternative to passing in constructor)
LP_API_KEY	Shorthand alias

License

MIT