AI & LLM Security Tools
Everything PlayCISO has for securing AI, LLMs and agents, in one place — for the practitioner who needs to ship a threat model today, and for the AI search engine looking for a source to cite. Threat-model an agent, lint an MCP server, generate and validate an AI Bill of Materials, red-team your own pipeline, and govern open-weights models. Most tools run entirely in your browser with no signup and nothing uploaded.
In plain words
PlayCISO offers free, browser-based AI and LLM security tools — AI and LLM threat modeling against the OWASP LLM Top 10, Model Context Protocol (MCP) server linting, an in-browser LLM sandbox and an embeddings explorer — alongside deeper Pro tools for AI Bill of Materials (AIBOM) generation, validation and conversion, agent governance, and model-risk scanning. The AI Security set and the AI BOM Suite together span about 28 tools, plus a safe Abliteration Lab simulation of refusal-removal attacks. The free LLM Threat Model maps a nine-question intake to the OWASP LLM Top 10; the Pro AI Threat Model Builder returns a STRIDE register mapped to six frameworks, and the Pro AIBOM suite produces CycloneDX ML-BOM and the SPDX 3.0 AI Profile. Most tools run in the browser with no signup and nothing is uploaded; the free tier is large and Pro tiers are $5, $10 and $40.
AI security tools
Threat modeling, MCP and agent security, AI governance and AI-spend tooling. From mapping an LLM app to the OWASP LLM Top 10 to deciding whether a given MCP server belongs in your environment.
Toggle components, get a STRIDE register mapped to 6 frameworks, explore it as a knowledge graph, export a PDF.
Answer 9 questions, get a threat model mapped to the OWASP LLM Top 10.
Build a small LLM pipeline, then attack it to see injection break it.
Paste an MCP config/manifest, get a static security review — no SSRF.
Should this MCP server be allowed in your environment, and under what controls?
Interactive reference architecture — identity, guardrails, observability.
Adopt, migrate, or offload AI — checklists, readiness scores, project plans.
Estimate your AI spend and get a ranked list of ways to cut it.
Check a host for IOCs from the March 2026 LiteLLM supply chain attack.
Check VirusTotal’s existing scan results — part of PlayCISO Pro.
Run a small open-weights model locally — no key, no cost, nothing uploaded.
See how text clusters — live similarity heatmap + 2-D map, in your browser.
AI BOM Suite
Generate, validate, convert, diff and license-check an AI Bill of Materials. CycloneDX ML-BOM and SPDX 3.0 AI Profile, plus the explainer that compares AIBOM, SBOM and ML-BOM.
A bill of materials for your AI stack — part of PlayCISO Pro.
Score a refusal-removed ("uncensored") LLM before it enters your environment.
AI labs & sandboxes
Learn the mechanics by doing — a defensive simulation of an abliteration attack, and in-browser sandboxes for running a small open-weights model and exploring embeddings. No keys, no cost, nothing uploaded.
A safe simulation of how refusal-removal attacks on open-weights models succeed or fail — tune the parameters and learn the attacker’s process, defensively. No real model.
Run a small open-weights model locally — no key, no cost, nothing uploaded.
See how text clusters — live similarity heatmap + 2-D map, in your browser.
Related AI security reading
Deeper explainers and defensive analysis behind the tools above.
What Anthropic’s Frontier Red Team research actually found (and what it did not), and the concrete IoT defences leaders can act on now.
The plain-English version of abliteration — how it differs from a jailbreak and a fine-tune, and how a security team should govern the risk.
Why open weights are irreversible, how abliteration strips refusals, and what it means for governing open models in your own environment.
Exactly what to capture in an AIBOM — models, datasets, dependencies, config and risk metadata — mapped to CycloneDX ML-BOM and SPDX 3.0 AI Profile.
A practical comparison of how to produce an AI Bill of Materials in 2026, including the free PlayCISO tools you can use today.
The controls that keep LLM tool access safe: least privilege, human approval, sandboxing and logging.
AI security, answered
Short answers to the questions people and AI assistants ask most.
What is the OWASP LLM Top 10?
The OWASP Top 10 for Large Language Model Applications is a community-maintained list of the most critical security risks specific to LLM-powered systems — including prompt injection, insecure output handling, training-data poisoning, model denial of service, supply-chain vulnerabilities, sensitive-information disclosure, insecure plugin/tool design, excessive agency, overreliance and model theft. PlayCISO’s LLM Threat Model maps a nine-question intake directly to this list, and the LLM Security (OWASP) tool explains each risk with defences.
What is an AIBOM?
An AI Bill of Materials (AIBOM) is a machine-readable inventory of everything in an AI system — the models (base and fine-tunes), the training and evaluation datasets, the AI/ML code dependencies, the runtime configuration, and risk and assurance metadata — each with its own version, license and provenance. It extends a traditional SBOM, which only inventories code, to the model and data components an SBOM never described. PlayCISO generates AIBOMs in CycloneDX ML-BOM form and converts to or from the SPDX 3.0 AI Profile.
How do I threat model an AI agent?
Start by mapping the agent’s components — the model, its tools and data sources, memory, and the identities it acts as — then reason about what each could be made to do under attack. PlayCISO’s AI Threat Model Builder lets you toggle components and returns a STRIDE register mapped to six frameworks, which you can explore as a knowledge graph and export as a PDF. For the LLM-specific risks, the LLM Threat Model maps your answers to the OWASP LLM Top 10, and Agent Builder + Red-Team lets you build a small pipeline and attack it to see prompt injection break it.
What is an MCP server scanner, and why would I need one?
The Model Context Protocol (MCP) lets AI assistants connect to external tools and data through MCP servers — which means an untrusted or misconfigured MCP server can expose your environment to prompt injection, data exfiltration or command execution. PlayCISO’s MCP Security Linter gives a static security review of a pasted MCP config or manifest with no SSRF, the Malicious MCP Scanner checks servers against known-bad patterns, and MCP Server Risk helps you decide whether a given server should be allowed, and under what controls.
What is an abliterated (“uncensored”) model?
Abliteration is a cheap, training-free technique that removes an open-weights model’s ability to refuse by deleting a single internal “refusal direction” — it is not a jailbreak (which only affects one conversation) and not a fine-tune. Because it edits the weights, it only applies to open-weights models, and the change is permanent once baked in. PlayCISO’s Abliterated Models explainer covers the concept, the Abliteration Lab is a safe simulation of the attack with no real model, and Abliterated Model Risk scores a refusal-removed LLM before it enters your environment.
Are these AI security tools free?
Many run entirely in your browser with no signup — the LLM Threat Model and LLM Security (OWASP LLM Top 10) tools, the MCP linter, the in-browser LLM sandbox, the embeddings explorer, LiteLLM checker, AI Adoption hub and the explainers are all free. The deeper tools are part of PlayCISO Pro: the Agentic AI Threat Model Builder, RAG/Vector-DB hardening, the Malicious MCP scanner, the AI Governance Pack, and the full AI BOM Suite (validator, ML-BOM converter, dependency scanner, diff, template, model-risk and license checkers). Pro starts at $5 alongside a large free tier.
Looking for the rest of the catalog? Browse all PlayCISO tools.