Skip to content
Glitch

Securing AI Frameworks

AI frameworks accelerate development, but they also centralize risk. Prompt injection, unsafe tool execution, data leakage, and policy violations emerge from how frameworks assemble prompts, invoke tools, and stream outputs.

Glitch secures AI frameworks by wrapping the LLM client before it’s passed to the framework. This approach provides:

  • Detection — identify risky inputs, outputs, and tool calls
  • Scoring — quantify risk with confidence scores
  • Auditing — record every interaction for compliance
  • Enforcement — allow, block, redact, or escalate based on policy

All security happens at the LLM interaction boundary, without modifying framework internals.

Supported Frameworks

Section titled “Supported Frameworks”
LangChain Secure LangChain chains, agents, and tools
LlamaIndex Secure RAG pipelines and queries
Pydantic AI Secure typed prompts and structured outputs in Python
LangGraph Protect agent workflows and graph-based execution
Vercel AI SDK Secure streaming and developer-friendly TypeScript SDK
Google ADK Multi-language agent development kit security
Spring AI Enterprise Java integration with Spring framework

Universal Security Pattern

Section titled “Universal Security Pattern”

Regardless of which framework you use, the security pattern is the same:

  1. Wrap the LLM client with Glitch before passing it to the framework
  2. Set the base URL to https://api.golabrat.ai/v1 (or your self-hosted sensor)
  3. Use your Glitch API key as the api_key parameter

This approach:

  • ✅ Works with any OpenAI-compatible framework
  • ✅ Requires no framework modifications
  • ✅ Provides consistent security across all interactions
  • ✅ Enables audit trails and compliance logging

Next Steps

Section titled “Next Steps”