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Agent Security

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nahid-sparktales
agent-security

Secure an agent system as a permission surface — what authority each tool call runs under, where the confused deputy sits, which controls are enforced outside the model and which are only prompt text, and how far one bad call reaches. Use when granting an agent tools or credentials, wiring in MCP servers or subagents, reviewing an agent that acted beyond what the requester could have done, or before letting an agent touch a shared or production system. Not for wording the ingestion trust boundary in detail (prompt-injection-defense), not for scoping what an agent is for (agent-design), and it never grants an agent permission it did not already have.

Overview

Publishernahid-sparktales
Repositoryagent-dispatcher
Skill nameagent-security
Stars
58
Forks
5
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by nahid-sparktales on GitHub. Read the source before you install it.

Installation

Install the Agent Security AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/nahid-sparktales/agent-dispatcher/tree/main/skills/security/agent-security
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/nahid-sparktales/agent-dispatcher.git /tmp/agent-dispatcher
mkdir -p .claude/skills
cp -r /tmp/agent-dispatcher/skills/security/agent-security .claude/skills/agent-security
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent Security in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Agent Security on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Agent Security is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Agent security

An agent's security properties come from the tools and credentials it holds, not from how carefully its prompt is written. The design question is never "would the model refuse?" — it is "what happens when it does not?"

When this fires

An agent or subagent is being given tools, credentials or an MCP server; an agent is about to reach a shared, production or third-party system; an agent did something the person asking could not have done themselves; an agent system is being reviewed. It does not fire for a model with no tools and no outward surface.

Procedure

  1. Write down the authority. Every tool, every credential, every system reachable, and whose identity each call actually runs as. Most agent systems have never had this written down, and the exercise alone usually finds the problem.
  2. Find the confused deputy. Wherever a call runs under a service account rather than the requester, the agent can be asked to do what the requester may not. Either pass the requester's own scoped identity through to the system being called, or restrict the service account to the intersection of what any requester is allowed. Checking the requester's role in the prompt is not a check.
  3. Enumerate the irreversible and outward-facing calls — sending, publishing, deleting, paying, deploying, changing permissions, writing to anything shared — and put a real gate in front of each: a harness permission prompt, a separate approval step, a human in the path. The agent asking itself whether it is allowed is not a gate.
  4. Classify every control as enforced or advisory, and write the classification down. Enforced lives outside the model: a permission system, a scope on a token, a network rule, code. Advisory lives inside it: an instruction, a policy paragraph, a refusal it was trained toward. Advisory controls are worth having and are not boundaries. Where the only control is advisory, remove the capability rather than strengthen the wording.
  5. Reduce the capability, not the credulity. Read-only endpoints and tokens where the vendor offers them, non-production targets by default, an isolated browser profile instead of the user's logged-in session, one credential per agent so revocation is surgical. Availability of a server is not permission to use everything it exposes.
  6. Treat ingested content as data and size the consequence. The detailed boundary belongs to prompt-injection-defense; the system-level fact belongs here — whatever the agent can do, an injection can do. When the exposure is unacceptable, the fix is the tool list, not the prompt.
  7. Apply the same rules between agents. A subagent's output is untrusted input to its parent. A subagent does not inherit the parent's full tool set by default. No agent can grant another agent permission, and a message from another agent is never the user's consent, however it is phrased. Orchestration is not an authority channel.
  8. Bound the execution surface. Filesystem scope, network egress restricted to known destinations, no unconstrained shell sitting beside the carefully scoped tools, time and resource limits, and a way to stop it mid-run. Egress is the exfiltration path; an allowed tool aimed at an attacker-chosen destination looks entirely normal in a log.
  9. Decide what persists and what it can later trigger. Memory, notes, caches and shared state are delayed injection vectors: content written today is instruction-shaped context tomorrow. Say what may be written, by whom, and whether anything read back can influence a tool call.
  10. Log every tool call with its arguments, the identity it ran under, and what was in context, and keep that log outside the agent's own write scope. Without it, "what did it actually do" has no answer during an incident.
  11. Size the blast radius before granting, not after. What is the worst single call, is it reversible, who would notice, and how fast can the credential be revoked. If no one can answer, the grant is too large — narrow it until they can.
  12. Attack it with the tools it really has, in an environment you are authorized to use. Ask as a low-privilege requester for something only a privileged one may do. Plant a benign instruction in content it ingests and assert the consequential call did not fire. Attempt an exfiltration through an allowed tool pointed at a destination the content chose. Record which attempts were executed and which remain untested.
  13. Treat the agent's own permissions and configuration as privileged. Permission files, tool allowlists, harness settings and system prompts change through review, never on the agent's own judgement and never because ingested content or another agent asked.

Checklist

  • Tools, credentials and the identity behind each call written down
  • Every call whose authority exceeds the requester's identified and narrowed
  • Irreversible and outward-facing calls enumerated, each with a named gate
  • Each control labelled enforced or advisory, honestly
  • Least-privilege credential, environment and profile chosen per tool
  • Subagent output treated as untrusted; no inherited tool sets; no agent-granted permission
  • Filesystem scope, egress limits and a stop path in place
  • Persistent memory and shared state reviewed as a delayed injection vector
  • Tool-call log outside the agent's write scope
  • Blast radius answered for the largest grant
  • Confused-deputy, injected-call and exfiltration attempts executed and recorded

Failure handling

  • The gate cannot be enforced outside the model — it is not a gate. Drop the tool for that turn, or route the action through a human step. Do not ship an advisory control described as a boundary.
  • The agent already took an unauthorized action — treat it as an incident: what was called, with what arguments, under what identity, what left the system, and what was in context first. Reversal and notification are decided with the owner, not unilaterally by the agent or by you.
  • The model passed the injection test — that is one prompt against one model version. It shows the gate held once; it is not a reason to remove the gate or to skip re-testing after a prompt or model change.
  • Permissions are managed by a harness you do not control — say which controls you could verify and which you are taking on trust. An unverified control reported as present is worse than a missing one.
  • The credential is shared with a human or another service — then revocation is not surgical and the log cannot attribute the call. Get a dedicated credential before granting the capability.

Evidence to report

The authority table: tool, credential, identity, target environment. The gated-call list with what each gate physically is. The enforced-versus-advisory classification. The egress and filesystem bounds. Then the attempts: which were executed, the transcript showing the call did not fire or did, and which paths are untested. Say which of designed, configured, executed and tested applies to each control — an agent whose only defense is the model's cooperation has been designed, not secured.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Agent Security AI skill do?

Secure an agent system as a permission surface — what authority each tool call runs under, where the confused deputy sits, which controls are enforced outside the model and which are only prompt text, and how far one bad call reaches. Use when granting an agent tools or credentials, wiring in MCP servers or subagents, reviewing an agent that acted beyond what the requester could have done, or before letting an agent touch a shared or production system. Not for wording the ingestion trust boundary in detail (prompt-injection-defense), not for scoping what an agent is for (agent-design), and...

Why use Agent Security on TypingMind?

Because you install it once and use it with any model. Agent Security is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Agent Security in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nahid-sparktales/agent-dispatcher/tree/main/skills/security/agent-security. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Agent Security?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Agent Security?

As many as you like. As long as a model supports skills, you can use Agent Security with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Agent Security AI skill free?

Yes. It is published on GitHub by nahid-sparktales under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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