AI Safety & Security — AI Agents, Skills & Tools
Agents, skills, guides, tools, and commands for ai safety & security — 17 curated resources for building with AI coding agents.
Prompt Injection Auditor
Use this agent to audit an LLM app or agent for prompt-injection exposure — mapping where untrusted content enters the model's context (user, RAG, tools, web), assessing the blast radius if an injection succeeds, probing with adversarial inputs, and recommending architectural mitigations. Examples — "audit our RAG agent for indirect prompt injection", "what's the blast radius if our agent gets injected — which tools and credentials are exposed?", "review our LLM app's trust boundaries and tell us what to fix".
LLM Guardrails Designer
Design input and output guardrails for an LLM app — decide what to check (injection patterns, PII, secrets, policy, schema, leakage, toxicity), place them as input vs. output rails, implement with a library like NeMo Guardrails or LLM Guard, and fail closed. Use when adding a safety/validation layer around an LLM, not relying on the prompt alone.
Prompt Pii Redactor
Detect and redact PII and secrets from prompts (and logs/traces) before they reach an LLM provider — mask or tokenize emails, phone numbers, names, IDs, and API keys, reversibly where the response needs the real values back. Use when sending user or document data to a third-party model, or when LLM request logs may capture sensitive data.
Sandboxing AI-Generated Code: E2B vs Modal vs Daytona vs Vercel Sandbox
Where should agent-written code run? E2B vs Modal vs Daytona vs Vercel Sandbox compared on isolation, persistence, and cost, plus rules for safe execution.
Are Claude Skills Safe? A Security Review Checklist
Skills are an instruction supply chain: what can go wrong with third-party SKILL.md files, and the review checklist before installing or distributing one.
Data Privacy for LLM Apps: Stop Leaking Sensitive Data
Where LLM apps leak PII and secrets — prompts, logs, traces, vector stores, providers — and the controls (redaction, ZDR, tenant isolation) that stop it.
Defending Against Prompt Injection: A Practical Guide for LLM Apps
Prompt injection can't be solved at the model layer — so you defend in depth: trust boundaries, least privilege, human approval, guardrails, and red-teaming.
Securing AI Agents: The OWASP Agentic Top 10 in Practice
Agents add risks LLM-app security misses — autonomy, tools, memory, multi-agent trust. The key OWASP agentic threats and how to mitigate each in practice.
LLM Guard
An open-source security toolkit of input and output scanners for LLM apps — prompt injection, PII/anonymize, secrets, toxicity, and more, from Protect AI.
NeMo Guardrails
NVIDIA's open-source toolkit for adding programmable guardrails to LLM apps — input, dialog, retrieval, and output rails defined in the Colang language.
promptfoo
An open-source CLI for testing, comparing, and red-teaming LLM prompts, models, and apps.
Red Team LLM
Red-team an LLM app or agent for prompt injection, jailbreaks, and data leakage — probe the real attack surface (input, RAG, tools, system prompt) with adversarial inputs and report what got through and how to fix it.
Constitutional AI
Constitutional AI trains models against written principles — the model critiques and revises its own outputs by them, reducing reliance on human labels.
Guardrails
Guardrails are programmatic checks around an LLM — validating inputs and outputs in code — enforcing safety and format rules a prompt alone can't guarantee.
Jailbreak
A jailbreak is a prompt crafted to bypass a model's safety training and policies — making it produce output it was trained to refuse.
Prompt Injection
Prompt injection is an attack where untrusted content carries instructions an LLM then follows — overriding its task, leaking data, or triggering tool calls.
Red-Teaming (AI)
AI red-teaming is adversarial testing — attacking your model or agent with jailbreaks, injections, and misuse scenarios to find failures before users do.