Security for AI Apps tools directory
A curated directory of tools, frameworks, and reference materials specifically designed to address security vulnerabilities in LLM-integrated applications, including prompt injection, data leakage, and output validation.
Showing 12 of 12 entries
Rebuff
open-sourceA multi-layered prompt injection detector that uses heuristics, dedicated LLM analyzers, and canary tokens to identify and block malicious inputs.
Pros
- + Uses canary tokens to detect data exfiltration
- + Multi-layered approach reduces false negatives
- + Simple SDK integration for Python and TypeScript
Cons
- − Adds latency to the request pipeline
- − Heuristics may require tuning for specific domains
Guardrails AI
open-sourceA framework for adding structure, type validation, and quality constraints to LLM outputs using a pydantic-style syntax.
Pros
- + Strong integration with Pydantic for data validation
- + Supports corrective actions like automatic re-asking
- + Extensive library of pre-built validators
Cons
- − Steep learning curve for complex RAIL schemas
- − Significant token overhead during re-prompting
NeMo Guardrails
open-sourceNVIDIA's open-source toolkit for adding programmable guardrails to LLM-based conversational systems using the Colang modeling language.
Pros
- + Powerful DSL for defining dialog flows and constraints
- + Built-in support for jailbreak detection
- + Native integration with LangChain
Cons
- − Learning Colang is required for advanced logic
- − Can be heavyweight for simple applications
Lakera Guard
freemiumAn enterprise-grade API for real-time protection against prompt injections, jailbreaks, and PII leakage in LLM applications.
Pros
- + Very low latency compared to self-hosted LLM analyzers
- + Frequently updated threat intelligence database
- + Detailed dashboard for security monitoring
Cons
- − Closed-source proprietary engine
- − External API dependency
Promptfoo
open-sourceA CLI tool for testing and evaluating LLM output quality, including specific test cases for prompt injection and security regressions.
Pros
- + Enables matrix testing across different prompts and models
- + Automated scoring for security vulnerabilities
- + Integrates easily into CI/CD pipelines
Cons
- − Requires manual definition of test assertions
- − CLI-heavy workflow may not suit all teams
OWASP Top 10 for LLM Applications
freeThe definitive industry reference guide for the most critical security risks in LLM applications, including mitigations.
Pros
- + Industry standard for compliance and auditing
- + Comprehensive coverage of theoretical and practical risks
- + Regularly updated by security experts
Cons
- − Informational only; no direct implementation code
- − Broad scope requires careful prioritization
LLM Guard
open-sourceA comprehensive security toolkit for LLM applications that provides sanitization and detection for both inputs and outputs.
Pros
- + Includes PII detection and redaction
- + Scans for toxicity and offensive content
- + Modular architecture allows choosing specific scanners
Cons
- − High resource usage for local model-based scanners
- − Initial configuration can be verbose
Giskard
open-sourceAn open-source testing framework that automatically detects vulnerabilities, biases, and performance issues in LLM models.
Pros
- + Automatic generation of adversarial test cases
- + Specific focus on RAG (Retrieval Augmented Generation) security
- + Clear reporting for stakeholders
Cons
- − Focuses more on testing than real-time protection
- − Python environment setup can be complex
HashiCorp Vault
freemiumA tool for securely accessing secrets such as LLM API keys, providing dynamic rotation and fine-grained access control.
Pros
- + Centralized management for multiple LLM providers
- + Supports dynamic credential generation
- + Robust audit logging for secret access
Cons
- − Significant infrastructure management overhead
- − Steep learning curve for policy configuration
PyRIT
open-sourceThe Python Risk Identification Tool for LLMs, developed by Microsoft to automate red teaming and risk assessment.
Pros
- + Automates complex multi-turn jailbreak attempts
- + Integrates with various model endpoints
- + Built for professional security red teams
Cons
- − Requires security expertise to interpret results
- − Early-stage project with evolving API
Pangea AI Shield
freemiumA set of security APIs specifically for AI apps, including PII redaction, prompt injection detection, and audit logging.
Pros
- + Unified API for multiple security functions
- + Excellent documentation and SDK support
- + SOC2 compliant infrastructure
Cons
- − Usage-based pricing can scale quickly
- − Requires sending data to a third-party cloud
Garak
open-sourceAn LLM vulnerability scanner that probes models for a wide range of security flaws, including halluncinations and data leaks.
Pros
- + Extensive library of probe types
- + Supports many model types (local and remote)
- + Fast execution for quick security audits
Cons
- − CLI output can be overwhelming
- − Requires careful filtering of false positives
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