AI Security Tools
AI security tools help you find and fix the weaknesses specific to systems built on large language models (LLMs) and AI agents — things ordinary application security misses, like prompt injection, insecure handling of model output, over-privileged agents and an unvetted AI supply chain of models, datasets and ML libraries. This page is a curated directory of PlayCISO's scanners, assessments and checklists, organised by what you are trying to secure, plus a concise LLM security checklist you can keep.
Everything here is built around recognised references rather than ad-hoc advice: the OWASP Top 10 for LLM Applications, the OWASP AI Security Verification Standard (AISVS), the NIST AI Risk Management Framework and the EU AI Act. Most tools run free in your browser with no signup; the in-depth studios are marked Pro.
AI security tools in brief
- AI security tools are the scanners, assessments and checklists you use to find and fix weaknesses in systems built on large language models (LLMs) and AI agents — from prompt injection and insecure output handling to risky models and an unvetted AI supply chain.
- The practical categories are: LLM application testing (prompt injection, OWASP LLM Top 10), AI agent and MCP-server governance, model and dependency scanning, and governance artefacts (risk registers, policy packs, AI bills of materials).
- Most teams need more than one: an assessment to find gaps, a scanner to inspect models and dependencies, and a checklist or policy to hold the controls in place over time.
- Every tool in the directory below is built around recognised references — the OWASP Top 10 for LLM Applications, the OWASP AI Security Verification Standard (AISVS), NIST AI RMF and the EU AI Act — rather than ad-hoc advice.
- Most PlayCISO AI security tools run free in your browser with no signup; a few in-depth studios are marked Pro.
The AI security tools list
Each tool is a real, working scanner, assessment or checklist — not a brochure. Pick the group that matches what you are securing.
LLM application security
AI agents & MCP servers
Models & AI supply chain
Governance & reference
The LLM security checklist
A short, practical set of controls for securing LLM and AI-agent systems. Each maps to a category in the OWASP Top 10 for LLM Applications — use it as a recurring control set, not a one-time audit.
- 1
Treat all model input as untrusted
Prompt injection is the top risk in the OWASP LLM Top 10. Separate trusted system instructions from untrusted user and retrieved content, constrain what the model is asked to do, and never assume that content fetched from a web page, document or another agent is safe to act on.
- 2
Validate and sanitise model output
Never pass raw model output straight into a shell, SQL query, HTML page or downstream system. Validate structure and type, encode for the destination, and treat insecure output handling as a classic injection problem the model happens to feed.
- 3
Give agents and tools least privilege
Scope every tool, plugin and MCP server to the minimum it needs. Require explicit approval for high-impact actions, avoid standing credentials, and make sure an agent cannot reach data or systems beyond its task — excessive agency is its own LLM Top 10 category.
- 4
Keep a human in the loop for consequential actions
For actions that move money, change access, delete data or contact people, require confirmation rather than letting the model act autonomously. Design the system so a single bad generation cannot cause irreversible harm on its own.
- 5
Control the AI supply chain
Know which models, datasets and AI/ML libraries you depend on. Track them in an AI bill of materials, check models and dependencies for known issues before use, and pin and verify what you deploy.
- 6
Guard data and prevent sensitive disclosure
Minimise what reaches the model, keep secrets and personal data out of prompts and training where you can, and filter responses so the model does not leak system prompts, credentials or other users’ data.
- 7
Log, monitor and rate-limit
Record prompts, tool calls and outputs (with privacy controls), watch for anomalous usage and injection attempts, and rate-limit to blunt abuse and denial-of-wallet attacks against metered models.
- 8
Test continuously, not once
Red-team prompts, re-run prompt-injection tests after every change, and re-assess against the OWASP LLM Top 10 and the AI Security Verification Standard (AISVS) as the system and the threat landscape evolve.
Note: this checklist is a defensive summary aligned to the OWASP Top 10 for LLM Applications and the AI Security Verification Standard (AISVS). It is a starting point, not a certification — adapt it to your own architecture, data sensitivity and regulatory obligations, and re-test after every significant change.
Frequently asked questions
What are AI security tools?
AI security tools are software that helps you find, assess and reduce security weaknesses in systems built on artificial intelligence — especially large language models (LLMs) and AI agents. In practice they fall into a few categories: LLM application testing (for example prompt-injection testers and OWASP LLM Top 10 assessments), AI agent and MCP-server governance, model and dependency scanning, and governance artefacts such as AI risk registers, policy packs and AI bills of materials (AIBOMs). Most teams use several together: an assessment to find gaps, a scanner to inspect models and dependencies, and a checklist or policy to hold the controls in place.
What is the difference between AI security and traditional application security?
AI security builds on traditional application security rather than replacing it. You still need secure code, access control, logging and supply-chain hygiene. What AI adds is a set of model-specific risks: prompt injection (untrusted input steering the model), insecure handling of model output, excessive agency when models are given tools, sensitive-information disclosure, and risks that ride in through models, datasets and ML libraries. The OWASP Top 10 for LLM Applications is the most widely used catalogue of these risks, and AI security tools are generally organised around addressing them.
What is LLM security?
LLM security is the practice of protecting applications that use large language models, and the data and systems around them. The core controls are: treat all model input as untrusted and defend against prompt injection; validate and sanitise model output before it reaches other systems; give agents, tools and MCP servers least privilege and keep a human in the loop for consequential actions; control the AI supply chain of models, datasets and dependencies; guard sensitive data; and log, monitor, rate-limit and test continuously. The LLM security checklist on this page summarises these, and they map directly to the OWASP Top 10 for LLM Applications.
Are these AI security tools free?
Most of the PlayCISO AI security tools in this directory run free in your browser with no signup — including the LLM security assessment, prompt-injection tester, AI security risk mapper, AISVS checklist, MCP scanner, agent exposure scanner, AI dependency scanner, governance policy pack, AI risk register and glossary. A few in-depth studios — such as the agentic threat-model builder, the MCP server governance check, the model risk scanners and the AIBOM validator — are marked Pro. Each tool page states whether it is free or Pro.
Which AI security tool should I start with?
Start with an assessment to see where you stand. If you are securing an LLM application, run the free LLM security assessment against the OWASP LLM Top 10 and use the prompt-injection tester on your prompts. If you are deploying AI agents or MCP servers, begin with the agent exposure scanner and the MCP governance check. If your concern is the supply chain, run the AI dependency scanner and the model risk scanner, and record what you find in an AI risk register and AIBOM. Then keep the LLM security checklist on this page as a recurring control set.