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 adriannoes/awesome-agentic-ai --skill detecting-entra-offensive-tools-in-graph-logsgit clone --depth 1 https://github.com/adriannoes/awesome-agentic-aiWrote 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/adriannoes/awesome-agentic-ai/detecting-entra-offensive-tools-in-graph-logs)<a href="https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/detecting-entra-offensive-tools-in-graph-logs"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/detecting-entra-offensive-tools-in-graph-logs/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/adriannoes/awesome-agentic-ai/detecting-entra-offensive-tools-in-graph-logs"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/detecting-entra-offensive-tools-in-graph-logs.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high YARA Match · line 98 YARA rule matched a hack tool or exploit indicator (offensive tools, reconnaissance, privilege escalation, or exploit frameworks).Fix: Remove offensive tool references and exploit code. Legitimate agent skills should not contain penetration testing tools, exploit frameworks, or reconnaissance utilities.
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 | $0.00054 | $0.02543 |
| Opus 5 | $0.00027 | $0.01272 |
| Sonnet 5 | $0.00011 | $0.00509 |
| Haiku 4.5 | $0.00005 | $0.00254 |
Grade A, and why
detecting-entra-offensive-tools-in-graph-logs 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.
How it starts
The opening of the file, as written. The whole thing — 195 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Detecting Entra Offensive Tools in Graph Logs
Overview
For nearly a decade the legacy Azure AD Graph API (graph.windows.net) was a defender blind spot: requests to it produced no first-class activity log, so tools like ROADtools (roadrecon) and AADInternals — which lean heavily on AAD Graph — could enumerate an entire tenant with little trace. That changed when Microsoft shipped AADGraphActivityLogs (general availability in 2026), the counterpart to the already-available MicrosoftGraphActivityLogs (graph.microsoft.com). Together these two tables give SOCs request-level visibility into directory API traffic: the caller identity, app, source IP, HTTP method, request URI, and crucially the User-Agent.
This skill is the defensive complement to offensive Entra tooling. It hunts the two Graph activity tables for the behavioral and string fingerprints those tools leave behind. Many operators forget to spoof the User-Agent, so ROADtools (built on Python's aiohttp) emits a User-Agent like Python/3.12 aiohttp/3.10.4, and AADInternals frequently leaves AADInternals or library strings in the agent. Even when the agent is spoofed, the tools betray themselves through a characteristic endpoint-sweep pattern: roadrecon gather pulls users, groups, applications, serviceprincipals, devices, directoryroles, roledefinitions, oauth2permissiongrants, and more within a tight time window — a signature that survives header spoofing.
The activity being detected maps to MITRE ATT&CK T1078.004 – Valid Accounts: Cloud Accounts: an adversary using legitimate (often phished or token-stolen) cloud credentials to enumerate and operate against the tenant via the Graph APIs. These detections both surface live intrusions and validate that the offensive techniques in the companion red-team skills are observable.
When to Use
- Building or tuning detections for Microsoft Sentinel / Log Analytics covering Entra ID
- Threat hunting after suspected credential theft, device-code phishing, or OAuth consent abuse
- Purple-team exercises validating that ROADtools/AADInternals/AzureHound activity is detectable
- Investigating an alert and needing to correlate Graph API calls back to a sign-in/session
- Closing the legacy Azure AD Graph visibility gap after enabling AADGraphActivityLogs
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 · 195 lines · 54 tokens per session scan A 212d1742213e
detecting-entra-offensive-tools-in-graph-logs is a skill published in the GitHub repository adriannoes/awesome-agentic-ai (57 stars, last pushed 14d ago), licensed MIT. It adds 54 tokens to every session and 2,543 once invoked, about $0.0003 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 2026-09-03.
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