Application Control Policy Tampering logo

Application Control Policy Tampering

Organization
CDESpace
Application Control Policy Tampering

Triage a SaaS or tenant alert where a security control is weakened or admin control is seized — MFA, audit logging, or secret-scanning turned off, a security or tenant or cross-tenant policy edited, a tenant-admin or impersonation role granted, or a risky OAuth app or integration added. Reads the actor's identity, history, and the change window to tell routine admin and platform self-maintenance apart from an unexpected actor turning a protection off or handing out durable privilege, and decides escalate or dismiss.

Overview

PublisherCDESpace
RepositoryDetection-Skills
Skill nameApplication Control Policy Tampering
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 Control Policy Tampering 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-control-policy-tampering
  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-control-policy-tampering .claude/skills/cdespace-application-control-policy-tampering
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Application Control Policy Tampering 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 Control Policy Tampering 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 Control Policy Tampering 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 that are normal admin work by themselves — disabling MFA during a migration, editing a tenant policy, granting an admin role, installing an integration. Someone does each of these legitimately every week, so the fact that one happened is not the signal. The signal is who did it and whether they belonged to the change: an attacker who has taken an account weakens controls and grants themselves privilege using the same audit-log events a real admin generates. Triage here is reading the actor and the timing and deciding whether this is the right person at the right time or an unexpected actor weakening the app. This class owns the tenant-side change itself — the control edit, the OAuth or integration consent grant, the role grant — distinct from a delivered lure that may have prompted a user to approve it, which is judged on the delivery.

Begin with this detection's track record on this actor and tenant — a rule that fires whenever the platform team runs maintenance and is always closed benign starts the call near dismiss — then read the leads below. None is required on its own; they stack, and a single strong one (a self-grant of admin, MFA turned off by someone not on the admin team) can carry the call.

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

  • A protection turned off by the wrong actor. A disable/update on a security setting by someone outside the admin team for that control: Disable Strong Authentication, Set-OrganizationConfig AuditDisabled=$true, secret-scanning or branch-protection switched off on a repo by a non-owner. Turning a protection off leans escalate; turning one on is usually hygiene.
  • A self-grant or fresh-account elevation. The grantor and grantee being the same actor (Add member to role where actor == target), or a just-created account that received a token and then elevated. Real role grants are normally handed out by an existing admin to someone else, not claimed by the actor for themselves.
  • A risky integration from an unverified source. An OAuth app or third-party integration consented in from an unverified or unofficial publisher, asking for broad tenant-wide scopes: Consent to application with Directory.ReadWrite.All or full-mailbox access, an unlisted app rather than one from the org's known catalog. A malicious or suspicious reputation verdict on the app's publisher domain sharpens the call. The apps-and-tokens context (service-principals, oauth2-permission-grants) tells you whether the app and its grants are already established in the tenant.
  • A tenant or cross-tenant policy edit that widens trust. A change that loosens who can act or adds a trusted partner tenant — a conditional-access policy weakened, a cross-tenant access setting opened, an impersonation role granted. Widening trust is the durable-access move attackers reach for after taking an admin.

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 confirmed admin whose role fits. The actor is a known IT or platform admin — the identity directory (role-assignments, directory-roles) confirms their standing — whose role and prior history include this exact change. A self-grant is the exception: confirmed-admin standing alone does not dismiss it, and it stays escalate-reachable unless a documented break-glass record accounts for this specific elevation.
  • A known automation identity. A first-party vendor backend account or a documented infrastructure-as-code service account with a steady baseline of these same changes — the platform or pipeline maintaining itself.
  • It matches the baseline and window. The same control, the same actor, the same kind of change has run here before inside a known maintenance or change window.
  • The change is removal, not seizure. An admin role being removed as part of offboarding, or an integration coming from a source the org already trusts and runs broadly, its publisher's reputation known or clean.
  • Clean history. This rule has closed benign on this actor before, with nothing new about who acted or what was touched this time.

To confirm a lead instead of guessing, pull the thread: did the same actor make further control changes, grant more roles, or sign in from an unusual place in the same window? Corroboration turns a suspicious admin action into a confirmed one.

Output

Decision

  • escalate: a protection turned off by an actor not on the admin team for that control, a self-grant of admin, a fresh or token-claiming account that elevated, a risky integration from an unverified source, or a policy edit that widens trust — especially two together, or any one with more control changes after it.
  • dismiss: a confirmed admin or known automation identity, acting inside a known window with a baseline match, explains it, and nothing widens trust or hands out durable privilege — or a self-grant is accounted for by a documented break-glass record. A dismiss is a positive call that the change is benign, made with that context in hand — an admin you confirmed in the directory, a window or baseline you actually saw the change fit; an actor you couldn't resolve or a window you couldn't establish is not that context, and absence of a bad sign is not proof it's good. Dismiss is logged and reopenable; when the benign context is missing or you're unsure — or an unknown actor weakens a load-bearing control, or a self-grant has no documented break-glass record — escalate.

Evidence

The control or policy touched and the toggle direction, the actor and whether they are a confirmed admin / known automation / newly created, whether grantor equals grantee, the change window, the integration's source and its publisher's reputation, and how this rule was handled here before.

Reasoning

Name the leads that decided it and how they stacked — a fresh account that claimed a token and then turned off MFA is escalate on its own; a known platform service account editing the same policy on its usual cadence is dismiss.

Frequently asked questions

What does the Application Control Policy Tampering AI skill do?

Triage a SaaS or tenant alert where a security control is weakened or admin control is seized — MFA, audit logging, or secret-scanning turned off, a security or tenant or cross-tenant policy edited, a tenant-admin or impersonation role granted, or a risky OAuth app or integration added. Reads the actor's identity, history, and the change window to tell routine admin and platform self-maintenance apart from an unexpected actor turning a protection off or handing out durable privilege, and decides escalate or dismiss.

Why use Application Control Policy Tampering on TypingMind?

Because you install it once and use it with any model. Application Control Policy Tampering 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 Control Policy Tampering in TypingMind?

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

Which AI models can use Application Control Policy Tampering?

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 Control Policy Tampering?

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

Is the Application Control Policy Tampering 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇