Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add onfire7777/universal-ai-skills-library --skill ai-model-privacy-auditgit clone --depth 1 https://github.com/onfire7777/universal-ai-skills-libraryWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/onfire7777/universal-ai-skills-library/ai-model-privacy-audit)<a href="https://agentmods.dev/skills/onfire7777/universal-ai-skills-library/ai-model-privacy-audit"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/ai-model-privacy-audit/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/onfire7777/universal-ai-skills-library/ai-model-privacy-audit"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/ai-model-privacy-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00063 | $0.02303 |
| Opus 5 | $0.00032 | $0.01151 |
| Sonnet 5 | $0.00013 | $0.00461 |
| Haiku 4.5 | $0.00006 | $0.00230 |
Grade A, and why
ai-model-privacy-audit scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
97% identical to ai-model-privacy-audit — 19 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 205 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Model Privacy Audit
Overview
AI model privacy auditing is the systematic assessment of whether trained ML models leak information about their training data. Models can memorize individual training records, enabling adversaries to extract personal data, determine dataset membership, reconstruct input features, or infer sensitive attributes. This skill implements a comprehensive model privacy audit methodology using established attack techniques and tools (ML Privacy Meter, ART, Foolbox) to quantify privacy leakage before deployment and periodically during operation. The audit results feed directly into the AI DPIA risk assessment and inform mitigation measure selection.
Privacy Attack Taxonomy
1. Training Data Extraction
Objective: Extract verbatim or near-verbatim records from the model's training data.
| Attack Vector | Description | Target Models |
|---|---|---|
| Prompt-based extraction | Craft prompts that cause LLMs to regurgitate training data | Language models, generative models |
| Canary extraction | Insert known canary strings into training data and test if model reproduces them | Any model (testing methodology) |
| Gradient-based extraction | Use model gradients to reconstruct training inputs | Models with accessible gradients |
| Generative reconstruction | Use the model as an oracle to iteratively reconstruct training samples | GANs, VAEs, diffusion models |
Risk Factors Increasing Extraction Likelihood:
- Large model capacity relative to training data size (overfitting)
- Training data containing duplicated or near-duplicated records
- Longer training duration (more epochs)
- Lower regularisation
- Models with high output granularity (logits, probabilities)
Testing Methodology:
- Insert canary records with unique identifiers into training data
- Train the model
- Attempt extraction through various prompting strategies
- Measure extraction success rate (percentage of canaries recovered)
- Threshold: extraction rate should be below 0.1% for acceptable risk
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 205 lines · 63 tokens per session scan A 79e5d3a4a5f9
ai-model-privacy-audit is a skill published in the GitHub repository onfire7777/universal-ai-skills-library (16 stars, last pushed yesterday), licensed MIT. It adds 63 tokens to every session and 2,303 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to ai-model-privacy-audit, differing in 19 lines, and is treated as a copy.
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