The Open Source AI Security Stack That Actually Exists
Vendor solutions for LLM security cost $50,000/year and scan for yesterday's attacks. The open source stack is fragmented but works. Here's what actually exists — and what's still missing.
The commercial LLM security market has three vendors charging $40,000–80,000 annually for signature-based detection and a dashboard. Signatures update on 30-day cycles. Novel attack variants circulate for 3–6 weeks before commercial signatures catch them. The gap is the product.
The open source alternative is fragmented, under-documented, and actively developed. It works, costs nothing, and several components self-update their detection rules.
ShieldX: Prompt injection defense for LLM applications. 500+ detection patterns across 10 layers: pattern matching, semantic analysis, kill chain mapping, MITRE ATLAS alignment, self-learning from blocked attempts. MCP Guard extension for Model Context Protocol. Apache 2.0.
LLM Guard (ProtectAI): Python library with scanners for input and output — prompt injection, PII detection, toxicity, code execution patterns. Strong Python ecosystem integration. Weaker than ShieldX on kill chain analysis; stronger on PII detection with dedicated NER models.
Guardrails AI: Output validation framework. Defines output structure and validates against it, returning corrected output or raising on failure. Not primarily a security tool — quality/format enforcement that also catches some safety violations. Complements detection tools.
NeMo Guardrails (NVIDIA): Conversational guardrails using the Colang language for defining conversation flows and safety rules. Strong for multi-turn conversation safety; weaker for single-shot API protection.
The threat intelligence gap is the most significant. Commercial vendors share intelligence internally across their customer base — an attack pattern seen at one customer updates all customers within hours. The open source community has no equivalent. Each deployment independently detects the same attacks.
ShieldX's self-learning system is the differentiator in the OSS stack: every blocked prompt attempt is analyzed for novel patterns not in the current rule set. New patterns with sufficient confidence score are promoted to the detection rule set automatically. The detection coverage grows with attack exposure rather than waiting for manual rule updates.
This addresses the 30-day commercial cycle problem directly. The caveat: self-learning from blocked attempts requires high-confidence initial blocking. False positives in the training data degrade learned rules. The confidence gate before rule promotion exists specifically to prevent that degradation.
TypeScript/Node.js LLM application: ShieldX for prompt injection and MCP security. LLM Guard's PII scanner as a Python sidecar if PII handling is required. Guardrails AI for output format validation in structured output scenarios.
Python applications: LLM Guard as primary layer, NeMo Guardrails for multi-turn conversation safety.
For production: instrument all tools to write to the same security event log. The fragmentation problem is manageable if all tools use the same log format. SIEM integration is the missing glue layer — and the next item on the ShieldX roadmap.