Build Agentic AI Applications Secure By Design.
An agent is more than its code. It is a model, the instructions it follows, the tools it can call and the text those tools send back. Safeguard checks each of those parts before release: the MCP servers your agent connects to, the skills it loads, the model files it runs and the LLM endpoint it answers from, alongside the code and open source packages that hold it together.
Every Agent Has A Longer Parts List.
Agentic applications take actions. They read files, query systems and call APIs on behalf of a user, and the model decides what to do from text it reads at run time: the tool descriptions an MCP server advertises, the instructions inside a skill, and whatever a tool returns.
So the components that need review go beyond the code. A typical agent depends on third-party MCP servers, community skills, downloaded model weights and a hosted or self-hosted LLM, as well as the usual open source packages. Many of these are adopted with less scrutiny than a new library would get, and a package scanner does not look inside them.
Tool Text Becomes Instructions
A hidden directive in a tool description, or in what a tool returns, can steer the agent. It reads as ordinary text, which is why it gets through review.
Tools With Too Much Reach
A server that exposes shell access, file deletion or credential reads hands that reach to the agent, and to anyone who manages to steer it.
Skills And Models Carry Code
Skills often bundle scripts next to their instructions, and some model formats, such as pickle, can run code when the file is loaded.
Endpoints That Give Things Away
An LLM endpoint can be talked into ignoring its instructions, revealing its system prompt or returning data it should keep to itself.
For The Teams Shipping Agents.
Product And Platform Engineers
Building agents, copilots and LLM features into a product, and wanting security checks in the workflow they already use.
Application Security Teams
Asked to review agentic features, and needing findings about tools, skills and models as well as code.
Security And Risk Leaders
Deciding which AI components may ship, and wanting that decision written as policy rather than kept in a document.
From The First Tool To The Release Decision.
- 01
Look before you adopt
Search the free Gold directory at gold.safeguard.sh for the MCP servers, AI models and agent skills you are considering. It lists servers from the official MCP registry, models with their developer, licence and modality, and published agent skills.
- 02
Scan the code and dependencies
Connect the repository. Software composition analysis, static analysis and secrets scanning cover the application code, the agent framework and every open source package around it, as they would for any other service.
- 03
Assess each MCP server the agent calls
Give the Agentic & MCP Security scanner a server's endpoint URL. It completes the MCP handshake, lists the tools, resources and prompts, and checks for tool poisoning, tools with excessive agency, servers that answer without authentication, resources that point at secrets, and tools that download and run remote code. It then calls a small set of read-only tools with harmless input to look for injected instructions in their output, and flags tool combinations that together could move data out. Tools that write, delete, send or execute are never called.
- 04
Check the skills it loads
The Agent Skill Security scanner reads a skill's instructions, its manifest and any bundled scripts. It reports hidden or injected instructions, skills that treat fetched content as commands, over-broad permissions, unsafe tool use, exposed secrets and instructions that send data out, each located at the file where it occurs. Developers can run the same checks locally with the Safeguard CLI before they commit a skill.
- 05
Scan the model files
Point Model Malware Scan at a model repository or a Hugging Face model. It ranks serialization formats by their risk of running code, looks for embedded code in weight and pickle files, checks the source that ships with the model, and reviews its licence, lineage and model card.
- 06
Probe the LLM endpoint
LLM App Security sends single-turn and multi-turn attack prompts to an OpenAI-compatible chat endpoint. The probes are mapped to the OWASP Top 10 for LLM Applications and cover prompt injection, jailbreaks, system prompt leakage and sensitive data disclosure, with refusal-aware checks on each response.
- 07
Decide with policy
Results from every engine land in the same findings view as your code scans, where you can filter by scanner. Write policies with a block, warn or notify action, group them into gates at the stages of your release process, and apply the same rules to every team that ships an agent.
Agents That Ship With Known Risk.
Safer Tools
One Place For AI Findings
Release Rules You Own
Related: MCP Server Security, Prompt Injection Defense and AI Governance. Browse MCP servers, models and skills in the Gold directory.
Bring One Agent. We Will Check Its Tools, Skills And Model With You.
Book a demo and we will run the MCP, skill, model and LLM checks against an agent you are building, then set up the policies for its next release.
Teams that do this usually do these next
MCP Server Security
Govern what your agents are allowed to reach.
Read morePrompt-Injection Defense
Treat model input as hostile, because it is.
Read moreAI Governance
A defensible account of the models and agents you run.
Read moreGuardrails & Enforcement
Gates that block the build without blocking the team.
Read more
Self-healing security runs on Safeguard.
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