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Application AI Agent Action Abuse

Organization
CDESpace
Application AI Agent Action Abuse

Triage an alert where an AI agent takes a consequential action outside its intended scope or with no human instruction behind it, running a shell command, writing to a sensitive path, spawning a subprocess, reaching an external system, or driving a cloud or SaaS API when no user prompt or approval preceded it, or the action exceeds the agent's sanctioned tools. Reads whether an interactive prompt preceded the action, the sensitivity of what was touched, and whether the session is human-driven or automated, to tell sanctioned agent workflows apart from an agent acting on its own, and decides escalate or dismiss.

Overview

PublisherCDESpace
RepositoryDetection-Skills
Skill nameApplication AI Agent Action Abuse
Stars
57
Forks
5
Bundled files
Instructions only
LicenseApache-2.0
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by CDESpace on GitHub. Read the source before you install it.

Installation

Install the Application AI Agent Action Abuse 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/CDESpace/Detection-Skills/tree/main/skills/triage/application-ai-agent-action-abuse
  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/CDESpace/Detection-Skills.git /tmp/Detection-Skills
mkdir -p .claude/skills
cp -r /tmp/Detection-Skills/skills/triage/application-ai-agent-action-abuse .claude/skills/cdespace-application-ai-agent-action-abuse
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Application AI Agent Action Abuse 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 Application AI Agent Action Abuse 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 Application AI Agent Action Abuse 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.

Triage Steps

This class fires on actions an AI agent is built to take, Claude Code, a Cursor or Copilot agent, or an autonomous MCP client running a command, writing a file, or calling an API. Agents do this all day on their operators' behalf, so the fact that an agent acted is not the signal. The signal is intent and scope: an action with no human instruction behind it, or one that reaches past the tools the agent was sanctioned to use. An injected instruction or a hijacked session makes the agent act for someone other than its user, and the tell is that the action stands alone, no prompt asked for it, or the tool was never in scope. Triage here is reading whether a person drove this and whether the agent stayed in its lane. This class owns the action itself; the untrusted content that may have induced it is judged on its own by Application AI Prompt Injection.

Begin with this detection's track record on this agent and identity, a rule that fires on a sanctioned CI or automation agent and is always closed benign starts the call near dismiss, then read the session and the action for the leads below. None is required on its own; they stack, and a single strong one (an external tool call in a session with no user prompt on record) can carry the call.

Leads that point to a real threat, what to look for in the data:

  • An action with no recorded user prompt, corroborating a sensitive or out-of-scope move. A tool call, command, or external reach in a session with no user-prompt event before it. Treat this as corroboration, not a trigger on its own: interactive IDE sessions (for example Claude Code in an editor) and non-interactive sessions often do not emit a user_prompt event at all, so a missing prompt is common in benign work. It counts when it accompanies an out-of-scope tool, a sensitive target, or a reach to an unusual external destination.
  • Out of sanctioned scope. The agent invoked a tool or capability outside its declared allowlist, a research or read-only assistant running a shell command, writing outside its workspace, or calling a cloud or SaaS write API it was never granted.
  • A sensitive target. The action wrote to a persistence or system path, read a secret store, changed a permission or policy, or reached an external destination, the kind of move that matters even once, distinct from ordinary in-workspace edits.
  • An automated session reaching an unusual destination. A headless or automated session making outbound tool calls or network reaches that its normal job does not explain. Non-interactive alone is not the signal, IDE sessions also report as non-interactive; the tell is a destination or action a known automation identity has no reason to produce.

Leads that rule it out, benign context you can actually see in the data; if the data doesn't show it, you don't have it:

  • A prompt drove it. A user-prompt turn in the same session precedes the action and fits it, the operator asked, the agent did.
  • A sanctioned automation identity. A documented CI, deployment, or agent service account with a steady baseline of exactly these actions, the pipeline doing its job, confirmed by identity and history, not by the account name alone.
  • In scope and in baseline. The tool is within the agent's granted allowlist and the same action shape has run here before as normal work, an in-workspace file edit by a coding agent, a read the agent is meant to make.
  • A benign target. The action stayed inside the agent's workspace or touched non-sensitive resources, with no reach to persistence paths, secrets, or external systems.
  • Clean history. This rule has closed benign on this agent or identity before, with nothing new about the prompt context or the target this time.

To confirm a lead instead of guessing, pull the thread: did more unprompted actions, a reach to an unusual external destination, or a write to a sensitive path follow in the same session? Corroboration turns a suspicious action into a confirmed one.

Output

Decision

  • escalate: an action outside the agent's sanctioned tools, reaching a sensitive target (persistence path, secret store, policy change), or reaching an unusual external destination, when no sanctioned automation identity explains it. A missing user prompt corroborates these but does not escalate on its own, since IDE and non-interactive sessions often omit the prompt event. Two leads together, or more sensitive actions after the first, raise confidence.
  • dismiss: a user prompt in the same session, a sanctioned automation identity, or an in-scope in-baseline action explains it, and nothing reached persistence, secrets, or an external system without intent. A dismiss is a positive call that the action is benign, made with that context in hand, a prompt you saw precede it, an automation role you confirmed; a session whose prompt context you couldn't establish or a tool you couldn't place in scope is not that context, and absence of a bad sign is not proof it's good. Dismiss is logged and reopenable; when no prompt explains the action, the tool is out of scope, or the target is sensitive, escalate.

Evidence

The action and its target (command, file path, API, or destination), whether a user prompt preceded it in the session, whether the tool is within the agent's sanctioned scope, whether the session is interactive or automated, the acting identity and whether it is sanctioned automation, and how this rule was handled here before.

Reasoning

Name the leads that decided it and how they stacked, an external tool call in an interactive session with no user prompt on record, reaching a destination the agent never uses, is escalate on its own; a build agent writing inside its workspace after a matching prompt is dismiss.

Frequently asked questions

What does the Application AI Agent Action Abuse AI skill do?

Triage an alert where an AI agent takes a consequential action outside its intended scope or with no human instruction behind it, running a shell command, writing to a sensitive path, spawning a subprocess, reaching an external system, or driving a cloud or SaaS API when no user prompt or approval preceded it, or the action exceeds the agent's sanctioned tools. Reads whether an interactive prompt preceded the action, the sensitivity of what was touched, and whether the session is human-driven or automated, to tell sanctioned agent workflows apart from an agent acting on its own, and decides...

Why use Application AI Agent Action Abuse on TypingMind?

Because you install it once and use it with any model. Application AI Agent Action Abuse 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 Application AI Agent Action Abuse in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/CDESpace/Detection-Skills/tree/main/skills/triage/application-ai-agent-action-abuse. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Application AI Agent Action Abuse?

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 Application AI Agent Action Abuse?

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

Is the Application AI Agent Action Abuse AI skill free?

Yes. It is published on GitHub by CDESpace under the Apache-2.0 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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