MFA Fatigue Baseline logo

MFA Fatigue Baseline

Organization
CDESpace
MFA Fatigue Baseline

Triage MFA-fatigue (push bombing) detections from Entra ID sign-in logs, where a burst of MFA denials (sign-in error code 500121 — `status.errorCode` natively, `status_code` when normalized to OCSF) is followed by a successful login. Separates real coercion from habitual deniers by checking VIP membership, denial velocity, and the user's historical denial baseline.

Overview

PublisherCDESpace
RepositoryDetection-Skills
Skill nameMFA Fatigue Baseline
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 MFA Fatigue Baseline 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/MFA%20Fatigue%20Baseline
  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.

Use it in TypingMind

Enable MFA Fatigue Baseline 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 MFA Fatigue Baseline 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 MFA Fatigue Baseline 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.

MFA Fatigue Triage

MFA-fatigue detections fire when a principal accumulates a high count of MFA denials (Entra ID sign-in error code 500121 — status.errorCode natively, status_code if your pipeline normalizes to OCSF) inside a short window and at least one successful sign-in (error code 0) lands in the same window. (Code 500121 covers user-declined, timeout, and fraud-reported outcomes; Entra's additionalDetails field distinguishes them.) The pattern is high-signal for push bombing, but it also matches users with broken authenticators, aggressive conditional-access re-prompting, or a habit of reflexively dismissing prompts. Triage decides whether the success at the end of the burst was the attacker getting in.

Inputs

The triggering aggregate for one user and one 15-minute window: actor.user.name, mfa_denial_count, successful_login_count, src_ips, apps, user_agents, first_seen, last_seen — plus access to the raw sign-in events for the window and the preceding baseline period.

Triage steps

Run all three checks. Each check produces exactly one outcome: RISK or CLEAR.

Check 1 — VIP membership

Is actor.user.name present in your organization's VIP / high-value-target list (executives, admins, finance approvers — however your environment tracks them, e.g. a lookup table or watchlist)?

  • Present → RISK (vip-target)
  • Absent → CLEAR

Check 2 — Denial velocity

Pull the individual denial events behind mfa_denial_count and compute the inter-attempt gaps across the window. Did the denials happen in extremely short succession?

  • RISK (burst): 10+ denials with a median gap ≤ 30 seconds, or any run of 5+ denials inside a single minute — scripted or rapid manual push bombing.
  • CLEAR: denials spread across the window with median gaps measured in minutes — consistent with re-prompt loops or user fumbling.

Check 3 — Denial baseline

Confine baseline lookups to a bounded recent window — the alert's calendar day (UTC) is usually sufficient and keeps queries cheap. Within that window, examine the user's sign-in logs excluding the alert's 15-minute window. Does this user deny many MFA attempts as a matter of course?

  • CLEAR (habitual-denier): ≥ 5 denials outside the alert window, or denials occurring in 3+ separate hours of the day — threshold-crossing noise is normal behavior for this user.
  • RISK (anomalous-volume): few or no denials outside the alert window — the burst is isolated, new behavior for this user.

Decision rule

Count the RISK outcomes:

  • 2 or 3 of 3 → escalate
  • 0 or 1 of 3 → dismiss

No overrides, no exceptions: the rule is a majority of checks. Borderline measurements (e.g. median gap of 31 seconds) resolve toward RISK for checks 1–2 and toward CLEAR for check 3; note the borderline call in the reasoning.

Output

Decision

escalate or dismiss, plus the risk count (e.g. 2/3) and the outcome label of each check (vip-target / burst / anomalous-volume or their CLEAR counterparts).

Evidence

The window aggregate, the VIP-list check result, the computed inter-attempt gap distribution (min / median / longest run), and the same-day baseline denial counts (outside the alert window, bucketed by hour).

Reasoning

One line per check stating its outcome and the measurement behind it, then the tally. A dismissal must show the tally, not a gut call — never dismiss on volume alone.

Frequently asked questions

What does the MFA Fatigue Baseline AI skill do?

Triage MFA-fatigue (push bombing) detections from Entra ID sign-in logs, where a burst of MFA denials (sign-in error code 500121 — `status.errorCode` natively, `status_code` when normalized to OCSF) is followed by a successful login. Separates real coercion from habitual deniers by checking VIP membership, denial velocity, and the user's historical denial baseline.

Why use MFA Fatigue Baseline on TypingMind?

Because you install it once and use it with any model. MFA Fatigue Baseline 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 MFA Fatigue Baseline in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/CDESpace/Detection-Skills/tree/main/skills/triage/MFA%20Fatigue%20Baseline. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use MFA Fatigue Baseline?

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 MFA Fatigue Baseline?

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

Is the MFA Fatigue Baseline 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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