AI Defense Matrix Catalog

Guardrails AI

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Overview

Product details compiled from public sources, each with a citation.

Vendor
Guardrails AI2 · Details at Cyber Company Profiles
Description
Open-source Python framework that wraps LLM calls in input and output guards, applying validators from the Guardrails Hub to detect and mitigate risks in prompts and responses.1
Deployment
Self-hosted1
Status
Acquired3agent
Acquisition
Acquired by Harvey, announced 2026-09-09. Its status since the acquisition has not been disclosed.3agent

Matrix Coverage

Where this product defends, by asset class and NIST CSF function. The Coverage column shows whether each asset is Primary, Secondary, or Adjacent to what the product does. The table omits empty rows and columns.

Asset class ProtectDetect Coverage
Runtime AI Data Primary 1

Framework Relevance

These frameworks include controls relevant to the asset classes Guardrails AI defends. This is an editorial inference from the AI Defense Matrix asset-level crossmap, not a statement that Guardrails AI implements these controls or is certified against them.

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Framework Asset class Relevant controls
NIST IR 8596 Runtime AI Data Prompts (runtime); inference data
CSA AI Controls Matrix Runtime AI Data Data Security and Privacy Lifecycle Management; Application and Interface Security
ISO 42001 Runtime AI Data A.7 Data for AI systems; A.8 Information for interested parties
Google SAIF Runtime AI Data Expand AI red-teaming; runtime input and output safety; prompt defense
SANS Critical AI Security Guidelines Runtime AI Data Model I/O Handling (sanitize, validate, and filter inputs and outputs; segregate user and system prompts; multilayered prompt-injection defense); Conventional Security Controls (protect augmentation and RAG data with vector-store access controls and validation); Data Minimization and Obfuscation (limit sensitive prompt content; context-window management); Limit Model Behavior (AI guardrails)
MITRE ATLAS Runtime AI Data AML.T0051 LLM Prompt Injection; AML.T0054 LLM Jailbreak; AML.T0056 Extract LLM System Prompt (system prompt leaks as inference data; the AI Coding and Orchestration Tools row owns it as configuration)
OWASP AI Exchange Runtime AI Data Input threats: prompt injection, adversarial inputs, evasion; runtime threats: RAG poisoning, memory tampering
OWASP LLM Top 10 Runtime AI Data LLM01 Prompt Injection; LLM02 Sensitive Information Disclosure; LLM08 Vector and Embedding Weaknesses; LLM05 Improper Output Handling
OWASP Agentic Security Top 10 Runtime AI Data ASI06 Memory & Context Poisoning; ASI01 Agent Goal Hijack (via prompt injection in runtime inputs)

Provenance

Last sourced 2026-06-09.

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Sources

  1. Guardrails repository
    Vendor source accessed 2026-06-09
    • “Guardrails runs Input/Output Guards in your application that detect, quantify and mitigate the presence of specific types of risks.”
  2. Guardrails AI
    Vendor source accessed 2026-06-09
  3. Guardrails AI joins Harvey
    Vendor source accessed 2026-10-05
    • “Harvey today announced its acquisition of San Francisco-based Guardrails AI”

Changelog

  1. Recorded the acquisition by Harvey, announced on 2026-09-09.

  2. Added to the catalog from the Guardrails AI documentation.

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