Detect and block prompt injections for Exo agents with guardrails.
npx skills add https://github.com/midsphere-ai/exo --skill exo-guardrails
Please help me install this Agent Skill. Skill: exo:guardrails Source: https://github.com/midsphere-ai/exo/tree/main/skills/guardrails Command: npx skills add https://github.com/midsphere-ai/exo --skill exo-guardrails
Security guardrails for Exo agents to detect and block prompt injection, enforce content filtering, and govern tool usage and workflow decisions.
Attach a guardrail to the agent to block common prompt-injection patterns before the LLM call.
High-intent search queries and answers about installing and using this skill.
Prevent prompt injection on LLM agents by applying pattern-based input protection and LLM-backed risk analysis to detect and block malicious inputs before the LLM call. This approach secures agent deployments against system prompt leakage.
Enforce guardrails for dangerous tool calls by applying Rails-based control flow to govern decisions across tool invocations and LLM interactions. This allows you to define explicit CONTINUE, SKIP, RETRY, or ABORT behaviors for safe agent operation.
The best way to detect prompt injections in real-time workflows is combining fast pattern-based input checks with deep LLM-backed risk analysis. This dual-layer detection validates inputs and moderates content across pre-LLM calls and decision-flow control.
Yes, you can use LLM guardrails to enforce content filtering and policy checks by integrating BaseGuardrail and Rail systems. These systems validate inputs and govern tool usage to ensure agent compliance with defined safety policies.
When an LLM guardrail fails during content moderation, the system executes safe fail-open behavior and cross-rail coordination. This ensures robust safety by defining explicit fallback behaviors to handle failures without compromising the workflow.
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