detecting-data-and-model-poisoning SKILL.md skillA

detecting-data-and-model-poisoning from adriannoes/awesome-agentic-ai is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.

Install it, pinned
mdr add adriannoes/awesome-agentic-ai/detecting-data-and-model-poisoning@v1.0

Pins v1.0 in mdr.lock. Pin the fingerprint instead.

Audit grade
A, 17 of 17 checks passed
Kind
SKILL.md skill
Repository
adriannoes/awesome-agentic-ai
Path
cursor-claude-codex/skills/anthropic-cybersecurity-skills/skills/detecting-data-and-model-poisoning/SKILL.md
Versions recorded
1
Latest version
v1.0, committed Jun 25, 2026
Size
10,500 bytes
Repository stars
69
License
MIT

Identify poisoned training data and backdoored models across the ML pipeline.

Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.

What the file says

# Detecting Data and Model Poisoning

> **Authorized-use-only notice:** This skill includes routines that craft poisoned samples and backdoor triggers for *defensive validation*. Generate and use poisoned data and backdoored models only in isolated test environments you control. Never deploy a backdoored model or distribute poisoned datasets.

## Overview

Data poisoning and model backdooring attack the *integrity* of an ML system at training time rather than at inference. In **data poisoning** (MITRE ATLAS **AML.T0020 Poison Training Data**), an adversary injects manipulated samples into the training, fine-tuning, or RAG corpus so the resulting model misbehaves — degraded accuracy, targeted misclassification, or an attacker-chosen bias. In **model backdooring** (MITRE ATLAS **AML.T0018 Backdoor ML Model**), the model behaves normally on clean inputs but produces an attacker-chosen output whenever a hidden *trigger* (a pixel patch, a rare token, a phrase) is present. Both are amplified by **ML supply-chain compromise (AML.T0010)**: poisoned public datasets, trojaned pre-trained weights downloaded from a hub, or a malicious model serialization. This is OWASP **LLM04:2025 Data and…
…

Read the whole file at its exact version.

How to install

The exact contents, by fingerprint
mdr add adriannoes/awesome-agentic-ai/detecting-data-and-model-poisoning@sha256:a04672e7f8bfd88a

The line at the top of this page pins the label v1.0, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.

Badge

mdr badge

[![mdr](https://markdownregistry.com/badge/art_g5zqx67rydatncmt.svg)](https://markdownregistry.com/a/art_g5zqx67rydatncmt)

2 badge image fetches in 30 days. What the badge shows.

Versions

The registry has recorded 1 version of this file, committed on Jun 25, 2026.

versioncommittedcommitsizeaudit
v1.0 latest2026-06-25 f69bd81 10,500 BA view

Audit of the latest version

A  17 of 17 checks passed. Fixed automatic checks with no AI involved, so the same checks give the same answer every run.
  • pass: Frontmatter block present
  • pass: Frontmatter declares a name
  • pass: Frontmatter declares a description
  • pass: Size between 200 bytes and 200 KB (10500 bytes)
  • pass: No zero-width or bidi control characters
  • pass: No instruction hidden inside an HTML comment
  • pass: No link to an exfiltration or paste host
  • pass: No credential-shaped string
  • pass: No instruction to send local credentials anywhere
  • pass: No text hidden with inline styles
  • pass: No prompt-injection phrasing
  • pass: No curl or wget piped into a shell
  • pass: No recursive delete of root, home or parent
  • pass: No instruction to read or print local credentials
  • pass: No base64 blob over 200 characters
  • pass: No link to a raw IP address
  • pass: No script tag

Source

GitHub

adriannoes/awesome-agentic-ai · 69 stars · license MIT · pushed 2026-10-10 · branch main

API

GET https://markdownregistry.com/api/v1/artifacts/art_g5zqx67rydatncmt
GET https://markdownregistry.com/api/v1/resolve?ref=adriannoes/awesome-agentic-ai/detecting-data-and-model-poisoning
GET https://markdownregistry.com/api/v1/blob/a04672e7f8bfd88a1d543a24467b5b50d7c962ccad97332ef4d948a8efeaaea9

Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.

Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.

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