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LLM Guard

by Protect AI

Open-source LLM input and output security scanner — protecting APAC production AI applications by detecting prompt injection, PII leakage, toxicity, jailbreak attempts, and hallucinations in both user inputs and LLM responses.

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AIMenta verdict
Recommended
5/5

"LLM input/output security — APAC AI teams use LLM Guard to scan and sanitize LLM inputs and outputs for prompt injection, PII, toxicity, and jailbreak attempts, protecting APAC production LLM applications from adversarial misuse."

Features
6
Use cases
1
Watch outs
3
What it does

Key features

  • Input scanning: APAC prompt injection, PII, and jailbreak detection
  • Output scanning: LLM response toxicity, bias, and PII redaction
  • LangChain/LlamaIndex: APAC middleware integration with 5-10 lines of code
  • Configurable thresholds: APAC risk tolerance tuning per scanner
  • Open-source: MIT licensed for APAC commercial deployment
  • FastAPI middleware: APAC REST API security wrapper for any LLM endpoint
When to reach for it

Best for

  • APAC AI engineering teams deploying customer-facing LLM applications who need input/output security scanning — particularly APAC financial services, healthcare, and enterprise teams with regulatory requirements to prevent PII leakage and policy violations in AI outputs.
Don't get burned

Limitations to know

  • ! Scanning latency adds 50-200ms per APAC LLM request depending on scanners enabled
  • ! False positive rate requires APAC tuning — aggressive settings block legitimate queries
  • ! LLM-based scanners incur additional APAC API costs for each detection call
Context

About LLM Guard

LLM Guard is an open-source security toolkit for LLM applications — providing scanners that inspect both user inputs (prompts) and LLM outputs (responses) for security threats before they enter or exit an APAC production AI system. APAC teams building customer-facing LLM applications use LLM Guard as a security middleware layer to prevent prompt injection attacks, PII exposure, and policy violations.

LLM Guard's input scanners detect APAC security threats before they reach the LLM: prompt injection detection (attempts to override system prompts), PII detection (customer data the APAC user should not be sending), toxicity detection (abusive content), and code injection detection (attempts to execute code via LLM). For APAC customer service and enterprise AI applications, blocking malicious inputs at the gateway layer prevents APAC security incidents that would be more costly to remediate.

LLM Guard's output scanners validate LLM responses before returning them to APAC users: bias detection, toxicity filtering, PII redaction (preventing the LLM from revealing APAC training data or injected context), hallucination detection using factual grounding checks, and sensitive information filtering for APAC regulatory compliance. APAC financial services teams use output scanning to prevent LLMs from producing investment advice that violates APAC MAS regulations.

LLM Guard integrates with LangChain, LlamaIndex, and FastAPI via Python middleware — APAC teams add LLM Guard with 5-10 lines of code wrapping existing APAC LLM calls. Scanners are configurable with APAC-specific risk thresholds, and teams can enable only the APAC scanners relevant to their threat model (e.g., PII + prompt injection for APAC enterprise use cases, toxicity for APAC consumer-facing applications).

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