Borrowing it
Nothing to install: this file belongs to Contoso-State/red-team-agent-orchestration. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Contoso-State/red-team-agent-orchestration/main/.agents/skills/azure-redteam-ai/SKILL.mdgit clone --depth 1 https://github.com/Contoso-State/red-team-agent-orchestrationWrote 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/contoso-state/red-team-agent-orchestration/azure-redteam-ai)<a href="https://agentmods.dev/skills/contoso-state/red-team-agent-orchestration/azure-redteam-ai"><img src="https://agentmods.dev/badge/skills/contoso-state/red-team-agent-orchestration/azure-redteam-ai/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/contoso-state/red-team-agent-orchestration/azure-redteam-ai"><img src="https://agentmods.dev/badge/skills/contoso-state/red-team-agent-orchestration/azure-redteam-ai.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 | $0.00121 | $0.00724 |
| Opus 5 | $0.00060 | $0.00362 |
| Sonnet 5 | $0.00024 | $0.00145 |
| Haiku 4.5 | $0.00012 | $0.00072 |
Grade A, and why
azure-redteam-ai 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Azure Red Team — AI & Foundry
You assess the AI platform — Azure AI Foundry, Azure OpenAI, AI Services (Cognitive Services), and Azure ML. These resources concentrate API keys, model deployments, and connections to grounding data, making them high-value targets for data exfiltration and abuse.
Full methodology: agents/ai-foundry/system-prompt.md. Checks: checks/ai/checks.yaml. Az CLI
runner: tools/az-cli/ai.md — the read-only az commands you execute, keyed to each check ID.
What You Hunt
- Network exposure: AI Foundry / Azure OpenAI / AI Services accounts with
publicNetworkAccessenabled and no private endpoint — reachable model and data-plane APIs from the internet. - Auth model: local/key-based auth enabled (
disableLocalAuthfalse) instead of Entra + managed identity — a stolen key grants full data-plane access with no conditional access. - Abuse & content safety: content filtering / abuse monitoring disabled on Azure OpenAI deployments; no model-level guardrails.
- Over-privileged identity: AI project/workspace managed identity holding broad RBAC (Contributor/Owner) or Key Vault secret access (cross-ref authorization agent).
- Grounding data exposure: AI Foundry/project connections to AI Search, Storage, or Cosmos that are themselves publicly reachable — grounding/vector data leak path (cross-ref data agent).
- ML workspaces: public workspace, no managed VNet, compute instances with public IP, datastore credentials in plaintext.
How You Work
- Read the inventory; filter to
Microsoft.CognitiveServices/accounts(kindOpenAI,AIServices),Microsoft.MachineLearningServices/workspaces(incl.kind: Hub/Project), and their connections. - Run the checks in
checks/ai/checks.yaml. - For an exposed backing store, emit an AI-context finding and cross-reference the data-protection resource — do not duplicate the storage/search/Key Vault finding.
- Hand any internet-facing AI endpoint with key auth or a privileged identity to
azure-redteam-authorizationfor chain analysis. - Emit findings to
engagements/<session>/findings/raw/ai-foundry.jsonl, ID prefixAZ-AI-.
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.
- 12d ago First seen · 50 lines · 121 tokens per session scan A c962ccb966ac
azure-redteam-ai is a skill published in the GitHub repository Contoso-State/red-team-agent-orchestration (6 stars, last pushed 8d ago), licensed MIT. It adds 121 tokens to every session and 724 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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