Intermediate

AI Security Tools

An overview of the best tools and frameworks available for detecting, preventing, and monitoring prompt injection attacks in production AI systems.

Guardrail Frameworks

Tool Type Key Features Best For
LLM Guard Open Source Prompt injection detection, PII scanning, toxicity filtering, invisible text detection Comprehensive input/output protection
NeMo Guardrails Open Source Colang modeling language, programmable dialogue flows, topical rails Complex conversational AI systems
Guardrails AI Open Source Output validation, schema enforcement, automatic re-asking, custom validators Structured output validation
Lakera Guard Commercial API Real-time injection detection, continuously updated threat database, low latency Production-grade detection
Rebuff Open Source Multi-layer defense, canary tokens, heuristic analysis, LLM-based detection Self-hosted defense pipeline

Security Scanning and Testing

Garak

LLM vulnerability scanner inspired by traditional security tools. Probes for prompt injection, data leakage, hallucination, and toxic generation with extensible attack modules.

PyRIT

Microsoft's Python Risk Identification Toolkit for generative AI. Automated red teaming with multi-turn attack strategies and scoring.

Promptfoo

Open-source LLM evaluation framework with built-in security test suites. Supports custom red team scenarios and regression testing.

Vigil

Prompt injection detection library using vector similarity, heuristic analysis, and ML classifiers to identify injection attempts.

Monitoring and Observability

Tool Focus Key Capability
Langfuse LLM observability Trace all LLM calls, score outputs, detect anomalies, debug production issues
Arize Phoenix ML observability Embedding drift detection, retrieval quality monitoring, hallucination tracking
Weights & Biases ML experiment tracking Log prompts and responses, compare model versions, safety metric dashboards
Datadog LLM Observability Enterprise monitoring Production monitoring, cost tracking, latency analysis, security alerting

Setting Up a Security Pipeline

Python - Security Pipeline with LLM Guard 
from llm_guard import scan_prompt, scan_output
from llm_guard.input_scanners import (
    PromptInjection, Toxicity, TokenLimit
)
from llm_guard.output_scanners import (
    NoRefusal, Relevance, Sensitive
)

# Define input scanners
input_scanners = [
    PromptInjection(threshold=0.9),
    Toxicity(threshold=0.8),
    TokenLimit(limit=4096),
]

# Define output scanners
output_scanners = [
    NoRefusal(),
    Relevance(),
    Sensitive(),
]

# Process request
sanitized_prompt, results_valid, results_score = scan_prompt(
    input_scanners, user_prompt
)
if not all(results_valid.values()):
    return "Request blocked by security policy."

# Get model response
response = model.generate(sanitized_prompt)

# Scan output
sanitized_output, results_valid, results_score = scan_output(
    output_scanners, sanitized_prompt, response
)
✅
Tool Selection Advice: Start with open-source tools like LLM Guard for input/output scanning and Garak for testing. Add commercial solutions like Lakera Guard when you need production-grade detection with continuously updated threat intelligence.