CIRCLE WITH A DOT

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️ Tool
===================

Opening: codeworld.codes is a curated, browser-local reference portal aimed at practitioners in cyber operations, TSCM, and digital forensics. The repository aggregates utility references, interactive utilities, and compact workflows without forwarding data externally — many utilities operate entirely client-side.

Key Features:
• Comprehensive hash and encoding utilities (MD5, SHA-1, SHA-256, SHA-512, Base64, ROT13) available locally.
• Network references including Nmap command examples, Wireshark display filters, and a protocol quick-ref for DNS/HTTP/TLS/ICMP/ARP/SMB.
• RF/TSCM toolkit with frequency references, path loss calculations, sweep methodology, SDR reference material and bug-frequency lists.
• Digital and mobile forensics sections covering registry hives, memory analysis workflows, X-Ways cheat sheets, ADB/SQLite artifact paths, and execution artifact mapping.
• Malware analysis notes including PE structure breakdowns, YARA guidance, packer signatures and C2 beacon patterns.
• Embedded code playground supporting Python, JavaScript, Go, Ruby and Bash snippets for offline analysis and data transformation.

Technical Implementation (conceptual):

The portal is organized as a reference collection and interactive client-side utilities. Hashing and header analysis are stated to run locally in the browser so sensitive data is not transmitted. The site aggregates curated command examples, filter lists, and protocol notes rather than acting as a live scanner or cloud service.

Use Cases:
• On-the-fly hash and encoding conversions during triage and malware analysis.
• Quick-reference Nmap and Wireshark snippets for red-team engagements and incident response playbooks.
• RF/TSCM planning and validation using frequency references and path-loss calculations.
• Rapid lookup of artifact locations across Windows/macOS/Linux and mobile platforms during forensic investigations.

Limitations:
• The portal is a reference and utility collection rather than an integrated, automated scanning platform.
• No centralized telemetry is provided; content freshness relies on repository updates (noted version v1.4.0, updated 2026-03-26 in the source manifest).
• Users should validate examples against current tool versions and environment-specific constraints.

Notes:

Resource is maintained as a GitHub repository (silvance/codeworld) and positions itself as a practical, local-first toolkit for practitioners. #tool #bookmark #tscm #forensics #osint

Source: https://codeworld.codes/

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Tool: MEDUSA — AI-first Security Scanner

Overview

MEDUSA is presented as an AI-first security scanner with more than 9,600 detection patterns focused on AI/ML applications, LLM agents, RAG pipelines, MCP servers and traditional codebases. The release v2026.5.0 emphasizes AI supply-chain coverage with a new Git scanning capability and repo poisoning detection.

Key technical facts
• Detection surface: 9,600+ AI security patterns targeting agent frameworks, MCP protocols, RAG components and editor/IDE config files.
• CVE coverage: Product claims detection of 133 CVEs, with named detections including Log4Shell, Spring4Shell, XZ Utils backdoor, LangChain RCE, MCP remote code execution and React2Shell.
• New rules: v2026.5.0 adds 45 attack rules for repo poisoning and 11 rules for MCP advanced attacks (schema poisoning, sampling injection, cross-server manipulation, Flowise RCE).
• Repo poisoning specifics: Detection across 28+ AI editor and IDE file types (examples enumerated include Cursor, Cline, Copilot, Claude Code, Gemini CLI, Kiro, Codex CLI, Windsurf, Amazon Q, Roo Code).
• Performance & outputs: Parallel processing for multi-core scanning, smart caching to skip unchanged files, and multiple export formats (JSON, HTML, Markdown, SARIF).

Technical implications (reporting the release)

The release documents a focused effort on AI supply-chain tactics: repo poisoning heuristics, editor-config weaponization, and MCP-targeted attack rules. The product adds path-relative FP filtering to reduce false positives when repo names previously matched heuristics. The Git scanning feature is described as a single-step repo analysis for supply-chain indicators.

Constraints and scope

The documentation frames MEDUSA as cross-platform (Windows/macOS/Linux) with IDE integrations and optional linter enhancements. The release notes list capabilities and detection counts; they do not provide operational deployment commands or step‑by‑step setup details.

medusa #ai_security #repo_poisoning #log4shell #langchain

Source: https://github.com/Pantheon-Security/medusa

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🧭 AI Security

This report documents a critical command injection vulnerability in OpenAI Codex that enabled theft of GitHub User Access Tokens via the ChatGPT Codex Connector. The discovery was credited to BeyondTrust Phantom Labs and disclosed to OpenAI on December 16, 2025. OpenAI issued a hotfix on December 23, 2025, followed by additional fixes for branch shell escape (January 22, 2026) and further shell-escape hardening and reduced GitHub token access (January 30, 2026). The vulnerability was classified as Critical (Priority 1) on February 5, 2026, with permission granted for public disclosure.

Technical narrative
• The ChatGPT Codex Connector uses short-lived, scoped OAuth 2.0 access tokens to act on behalf of consenting users. With broad default scopes, the application can access repositories, workflows, actions, branches, and private organizational resources when authorized inside an organization.
• In the Codex Web portal, user prompts that target repositories and branches create “cloud task” POST requests carrying environment identifiers, branch, and prompt text. On backend execution, Codex spins up containerized environments that run setup scripts, install dependencies, and may execute code derived from prompts.
• Environments support custom setup scripts, environment variables, and secrets, and by default allow outbound internet access during setup via an HTTP/HTTPS proxy. The command injection allowed an attacker to achieve shell escape within these containers, access environment-scoped secrets, and exfiltrate GitHub tokens.

Attack chain (reported)

Initial Access — crafted prompts or repository inputs processed by Codex allowed injection into backend task handling.
===================

️ Execution — containerized environment executed injected commands during setup or runtime.
Exfiltration — obtained short-lived OAuth tokens were transmitted out via network proxy pathways.

Observed fixes and timeline
• 2025-12-23: Hotfix for command injection.
• 2026-01-22: Fix for GitHub branch shell escape.
• 2026-01-30: Additional shell escape hardening and limits on GitHub token access.

This account focuses on the concrete findings: vulnerable task handling in Codex, container shell escape leading to token theft, the privileged default scopes of the GitHub integration, and the sequence of fixes applied by OpenAI. #OpenAI #Codex #GitHub #OAuth #Security

Source: https://www.beyondtrust.com/blog/entry/openai-codex-command-injection-vulnerability-github-token

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Tool: ClawFlows — Workflow System for OpenClaw

ClawFlows is a workflow framework designed for OpenClaw agents, delivering a library of 111+ prebuilt workflows that can be enabled with minimal friction. The repository emphasizes plain-text workflows that are easy to read, share, modify and roll back, and highlights deterministic execution and versioning as core design goals.

Key capabilities described in the source material:
• Prebuilt workflows: Over 100 community-contributed workflows covering smart-home automation, daily routines, health tracking, meeting preparation, and overnight project builds.
• Plain-text authoring: Workflows are authored in human-readable text files to improve transparency and shareability across users and agents.
• Scheduling and determinism: Workflows can be scheduled (examples include morning briefings and recurring checks) and the system is presented as deterministic and reliable across runs.
• Versioning and reuse: Workflow definitions support modification, saving, and rollback, enabling iterative development and community reuse.
• Community library: The project includes community workflows such as activate-sleep-mode (turn off lights, stop music, adjust thermostats), send-morning-briefing (weather, calendar, priorities), and build-overnight workflows that pick an idea and produce a finished project by morning.

Representative workflow types called out:
• Smart Home: activate-sleep-mode, activate-night-mode, activate-morning-mode, activate-focus-mode, activate-away-mode.
• Daily Routines: send-morning-inspiration, send-morning-briefing, check-calendar, send-bedtime-reminder, prep-tomorrow, morning-journal.
• Health & Wellness: track-habits and weekly scorecards.

Conceptual implementation notes in the source emphasize agent-driven orchestration: OpenClaw agents consume and execute plain-text workflow definitions, coordinate scheduled runs, and rely on versioned workflow assets to ensure reproducible behavior. The documentation includes examples that surface scheduling, the intended scope of actions (multi-system home control, information aggregation for meetings), and community-driven sharing of workflow definitions.

Limitations and scope (as presented): the material focuses on capabilities and examples rather than integration specifics or deployment procedures. The repository lists workflow names and schedules, and describes behavioral intents; it does not provide runtime configuration or execution commands in the presented summary.

ClawFlows #OpenClaw #workflows #automation #agents

Source: https://github.com/nikilster/clawflows

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️ Tool — Immich
===================

Overview

Immich is a self-hosted photo and video management solution focused on private backups, organization, browsing and search. The project provides mobile applications for Android and iOS that connect to a user-controlled server to store and manage media. The publicly visible project assets include a demo instance, a GitHub repository for source code, and localization support via Weblate. Additional community and support channels include a Discord server and purchasable product keys and merchandise.

Capabilities
• Backup: Immich is presented as a solution for backing up photos and videos from mobile devices to a server under the user's control.
• Organization and search: The platform emphasizes tools for organizing media collections and searching across photos and videos.
• Mobile clients: Native mobile applications are available for Android and iOS to facilitate capture-side backup and browsing.
• Open-source & localization: Source code is hosted on GitHub and translation/localization is coordinated through Weblate.
• Distribution & demo: A public demo is offered for evaluation; commercial support is available via product keys and merchandise sales.

Conceptual architecture (as implied)
• Client-server model: Mobile clients upload and synchronize media to a central server controlled by the deployer.
• Local-first privacy model: By running the server in a user-controlled environment, media remains under the user's administrative domain rather than a third-party cloud.
• Community-driven development: Open-source code and Weblate localization indicate community contributions and internationalization efforts.

Use cases
• Personal media backup for users prioritizing privacy and local control.
• Small teams or households that want shared media libraries without third-party cloud storage.
• Developers and translators contributing to an open-source media management stack.

Limitations & notes

The public description focuses on functionality and distribution channels; there are no technical deployment or configuration details provided in the source material. No operational security, scaling limits, or third-party integrations are specified in the referenced content.

Immich #selfhosted #privacy #GitHub #Weblate

Source: https://immich.app/

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Tool: AgentSonar

AgentSonar is a network‑visibility tool that identifies likely LLM/AI agent traffic by correlating process ownership of sockets with contacted domains and applying a heuristic classifier that outputs an AI score between 0 and 1.

Summary

AgentSonar records outbound connections, associates them with processes via socket ownership, extracts domain indicators from TLS SNI and DNS, and produces scored events for each process→domain pair. Known agents can be defined to produce deterministic matches; domains marked as noise are excluded from scoring.

How it works (conceptual)
• Socket correlation: associates OS socket ownership with userland processes to reveal which binary initiated a connection.
• Domain extraction: uses TLS SNI and DNS observations as the domain identifier for each outbound flow.
• Heuristic classifier: analyzes traffic shape characteristics — byte/packet asymmetry, prevalence of small packets, long‑lived or streaming connections, and programmatic TLS patterns — to infer whether a flow resembles LLM API traffic.
• Scoring model: emits an AI-likelihood score between 0 and 1 per process→domain pair; known agents map to score 1.0, noise maps to 0.

Capabilities and workflows

AgentSonar provides persistent event storage and a triage-oriented workflow for reviewing high‑scoring unknowns and labeling them as agents or noise. It supports importing pre-built event streams for classification and encourages community submissions of agent classifications to improve coverage.

Limitations and scope

The approach relies on observable network metadata (socket ownership, SNI, DNS) and traffic-shape heuristics; encrypted payloads and obfuscated patterns remain outside content-level analysis. Deterministic detection depends on maintained known-agent mappings; heuristic scoring produces probabilistic indicators rather than definitive attribution.

Practical context

AgentSonar targets defenders seeking endpoint-to-domain visibility with AI‑specific signal enrichment, enabling detection of shadow AI usage where traditional allowlists may miss programmatic LLM traffic.

agentsonar #llm_detection #network_visibility #knostic

Source: https://github.com/knostic/AgentSonar/

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️ Tool: Awesome NotebookLM Templates
===================

This repository is a curated collection of slide prompt templates designed for NotebookLM and Kael.im. The content catalogs field-tested prompts and visual design definitions drawn from creators across Note, WeChat, RED, and X, aimed at converting papers, notes, transcripts, and raw brain dumps into structured, presentation-ready slide decks.

What the repo contains
• A taxonomy of slide styles: editorial/newspaper, minimal seminar, pop/youth/street, typography-driven, avant-garde/art, product/premium, and high-energy sports layouts. Each style entry describes the visual intent, typographic emphasis, and recommended content density.
• A high-quality cover-slide specification inspired by Swiss Style and Bauhaus: asymmetrical layouts, ultra-large short title phrases, and ultra-small benefit-driven subtitles.
• Field-tested prompt shells that instruct NotebookLM/Kael.im to produce slide sequences with clear slide-level roles: cover, section header, content slide, visual callout, and summary.
• A pointer to a companion repository, citation-check-skill, which detects missing or hallucinated citations inside generated slides.

Capabilities and use cases

These templates enable researchers, founders, designers, and fast-moving creators to: produce consistent slide decks from unstructured inputs; apply a design language across slides; and reduce iteration time when refining slide text and hierarchy. The citation-check-skill is useful for validating source attributions when notebooks synthesize claims.

Technical notes and limitations

The materials are prompt templates and design definitions rather than runnable code. The repository documents expected outputs and stylistic constraints but does not include deployment artifacts. Effectiveness depends on NotebookLM/Kael.im model behaviour and the quality of the input document; prompts may require iterative refinement for domain-specific content. The citation-check-skill is referenced as an integration candidate but usage details are in its own repository.

Hashtags

NotebookLM #KaelIm #PromptEngineering #SlideTemplates #CitationCheck

Source: https://github.com/serenakeyitan/awesome-notebookLM-prompts?tab=readme-ov-file

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️ Tool: meetscribe — Local meeting capture, diarization and summaries
===================

meetscribe is a locally‑run meeting capture and transcription tool that records dual‑channel audio (user mic and remote system audio) at the OS level and produces diarized transcripts, time‑aligned text, AI‑generated summaries, and a polished PDF export. The project chains several open components to provide an end‑to‑end offline workflow for meetings.

Architecture and core components
• Audio capture: captures mic and remote audio as separate channels via PipeWire or PulseAudio with ffmpeg handling recording and file creation.
• ASR and alignment: uses WhisperX for batched inference with the openai/whisper-large-v3-turbo model and performs word‑level timestamp alignment using wav2vec2 alignment methods.
• Speaker diarization: uses pyannote‑audio to assign speech segments to speakers; the dual‑channel signal enables automatic YOU/REMOTE labeling.
• Local LLM summaries: integrates with local LLM runtimes (Ollama) to extract key topics, action items, decisions, and follow‑ups without sending data to cloud services.
• Outputs and UX: produces multiple export formats (.txt, .srt, .json, .summary.md, and a professionally formatted PDF containing summary plus full transcript) and exposes both a small GTK3 always‑on widget for recording control and a command‑line interface for scripted workflows.

Operational details and requirements
• Platform: Linux with PipeWire or PulseAudio. The tool is designed to work with any meeting app that plays audio through the system (Zoom, Meet, Teams, Slack, Discord, etc.).
• Models and tokens: diarization requires a HuggingFace model token for pyannote‑audio; ASR relies on WhisperX with model artifacts. Local LLM summarization is optional and requires a local LLM runtime and model.
• Hardware: GPU acceleration is supported and recommended (NVIDIA CUDA, 8GB+ VRAM suggested) for faster inference; CPU mode is available but slower.

Capabilities and limitations
• Capabilities: reliable dual‑channel capture, word‑level timestamps, speaker diarization with automatic YOU/REMOTE labels, offline LLM summaries, organized per‑session folders, and multi‑format exports including a professional PDF.
• Limitations: Linux‑centric; diarization depends on a HuggingFace model access token; LLM summaries require a local LLM runtime and model artifacts. Performance and latency depend on local hardware.

meetscribe #WhisperX #pyannote_audio #Ollama #PipeWire

Source: https://github.com/pretyflaco/meetscribe

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Tool: APTs Adversary Simulation
===================

This repository aggregates detailed adversary simulation campaigns that mirror tactics, techniques and procedures (TTPs) attributed to state-sponsored APT groups from Russia, China, Iran and North Korea. The collection documents multiple simulated campaigns and includes artifacts such as custom command-and-control (C2) components, backdoors, stagers, bootloaders and other payloads. Research sources referenced in the collection include major industry reports from Palo Alto Unit 42, Kaspersky, Microsoft, Cisco, Trellix, CrowdStrike and WithSecure.

Structure and contents
• Cataloged APT simulations aligned with CrowdStrike-style group names and taxonomy. Group simulations listed include multiple “Bear” variants for Russia and several “Panda” variants for Chinese actors, plus DPRK and Iranian-themed simulations.
• Artifact types enumerated in the repository include C2 servers and protocols, custom backdoor implants, initial stagers, secondary loaders/bootloaders and supporting scripts or tooling intended to emulate post-exploitation activity.
• Metadata and descriptive notes map simulated behaviors to observable TTPs and reference vendor reporting where applicable, enabling defenders to correlate simulation steps with published detections.

Technical scope (what is present, not how-to)
• Emulated network components for C2 communications and session management.
• Multiple binary and scripting artifacts representing stagers and backdoors, designed to reflect operational patterns observed in public APT reporting.
• Behavioral sequences and campaign outlines that describe chain-of-actions executed by the simulated actors.

Attack chain summary
• Initial Access — Simulated vectors and initial stagers representing entry methods.
• Download — Artifacts and payload delivery stages mimicking secondary payload retrieval.
• ️ Execution — Stagers and loaders that transition payloads into memory or disk execution.
• 🦠 Infection — Backdoor implants and persistence mechanisms used to emulate sustained presence.
• Exfiltration — Descriptions of simulated data staging and exfiltration patterns where included.

Limitations and intent

The repository is presented explicitly for educational, research and defensive security purposes. It documents emulated offensive behaviors based on public reports and is not a source of exploitation guidance. No installation, execution or deployment instructions are provided within this summary.

MITRE_ATT&CK #C2 #adversary_simulation #APT #backdoor

Source: https://github.com/S3N4T0R-0X0/APTs-Adversary-Simulation/tree/main/Iranian%20APT/Static%20Kitten

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Malware Analysis: BeatBanker Android banker + miner

Overview

BeatBanker is an Android malware family that combines traditional banker functionality with embedded crypto-mining capabilities. Analysis identifies a packed sample with a native loader (l.so) that dynamically loads a DEX component; later samples have been observed dropping a component identified as BTMOB for mining.

Behavior and Components
• Loader and packing: The malware uses a native shared object (l.so) acting as a DEX loader and unpacker, enabling dynamic class loading and evasion of static detection.
• Banking module: The banking component monitors installed browsers (Chrome, Firefox, sBrowser, Brave, Opera, DuckDuckGo, Dolphin Browser, Edge). It extracts visited domains using the regex ^(?:https?://)?(?:[^:/\\]+\\.)?([^:/\\]+\\.[^:/\\]+) and can manage and open links in the device's default browser.
• Crypto mining: Some samples include or drop a miner component (reported as BTMOB), indicating dual-purpose monetization.
• Persistence & telemetry: Includes mechanisms for persistence, telemetry exfiltration, and dynamic code loading from C2.

C2 Capabilities (selection)

The C2 implements a wide command set allowing full device control and data collection. Examples include dynamic DEX class loading, simulated updates that lock the screen, Google Authenticator monitoring (goauth), toggles for protection bypass, audio recording (srec), clipboard pasting via Accessibility Services (pst), SMS sending (ssms), and full device wipes via Device Administrator (adm<>wip<>).

Additional capabilities include keylogger and virtual keyboard management, overlay-based full-screen locks, screen capture/streaming, macroed taps/swipes, saved-link management, and VPN/firewall control.

Ecosystem and Delivery

Recent detections indicate modular deployment and possible Malware-as-a-Service distribution. The combination of banking-focus functionality and miner payloads suggests flexible monetization strategies. New samples reportedly drop BTMOB, reinforcing the dual-burden design.

Limitations and Open Details

Technical reporting focuses on observed code paths and C2 commands; specific IoCs and attribution are not provided here. The loader-based architecture and heavy reliance on Accessibility and overlay privileges are notable constraints and enablers for the malware's capabilities.

beatbanker #android #malware #btmob #mobilesecurity

Source: https://securelist.com/beatbanker-miner-and-banker/119121/

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AI Pentesting Roadmap — LLM Security and Offensive Testing
===================

Overview

This roadmap provides a structured learning path for practitioners aiming to assess and attack AI/ML systems, with a focus on LLMs and related pipelines. It organizes topics into progressive phases: foundations in ML and APIs, core AI security concepts, prompt injection and LLM-specific attacks, hands-on labs, advanced exploitation techniques, and real-world research/bug bounty work.

Phased Structure

Phase 1 (Foundations) covers machine learning fundamentals and LLM internals, including model architectures and tokenization concepts. Phase 2 (AI/ML Security Concepts) anchors the curriculum on standards and frameworks such as OWASP LLM Top 10, MITRE ATLAS, and NIST AI risk guidance. Phase 3 focuses on prompt injection and LLM adversarial vectors, describing attack surfaces like context manipulation, instruction-following bypasses, and RAG pipeline poisoning. Phase 4 emphasizes hands-on practice through CTFs, sandboxed labs, and safe testing methodologies. Phase 5 explores advanced exploitation: model poisoning, data poisoning, backdoor techniques, and chaining vulnerabilities across API/authentication layers. Phase 6 targets real-world research, disclosure workflows, and bug bounty engagement.

Technical Coverage

The roadmap lists practical tooling and repositories for experiment design and testing concepts without prescribing deployment steps. It calls out necessary foundations—Python programming, HTTP/API mechanics, and web security basics (XSS, SSRF, SQLi) to support end-to-end attack scenarios against AI systems. Notable conceptual risks include RAG poisoning, adversarial ML perturbations, prompt injection, and leakage through augmented memory or external tool integrations.

Limitations & Considerations

The guide is educational and emphasizes conceptual descriptions of capabilities and use cases rather than operational recipes. It highlights standards and references rather than prescriptive mitigations. Practical exploration should respect ethical boundaries and responsible disclosure norms.

OWASP #MITRE_ATLAS #RAG #prompt_injection #adversarialML

Source: https://github.com/anmolksachan/AI-ML-Free-Resources-for-Security-and-Prompt-Injection

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Tool: openclaw-kapso-whatsapp

Overview

openclaw-kapso-whatsapp provides a production‑oriented bridge that assigns a WhatsApp number to an OpenClaw AI agent by proxying requests through Kapso and the official WhatsApp Cloud API. The project emphasizes a stateless design implemented as two Go binaries: a bridge component that handles incoming events and a CLI/utility component for preflight and control. The bridge relays messages via a session JSONL mechanism: it reads session entries and emits replies, keeping runtime resource usage minimal.

Architecture and Components
• kapso API integration: Uses Kapso as a unified adapter for WhatsApp Cloud endpoints rather than reverse‑engineered WebSocket/Web clients.
• Stateless bridge: No persistent session objects are held in memory; API calls are performed per event which yields near‑zero idle CPU consumption.
• Two Go binaries: separates runtime bridge logic from CLI/management utilities.
• Session JSONL relay: message exchange is handled through JSONL session records; the relay reads these and issues outgoing replies.

Capabilities
• Official API path: avoids detection and ban risks associated with libraries that emulate WhatsApp Web (e.g., Baileys, whatsapp‑web.js).
• Low resource footprint: stateless calls and small Go binaries reduce idle CPU and simplify scaling models where ephemeral processes are acceptable.
• Delivery modes: supports polling by default and describes options to cut latency (for example via network tunneling) to reach sub‑second response times.
• Ancillary features: mentions voice transcription support and a NixOS/home‑manager module for system integration.

Operational considerations and limitations
• Statelessness trades in‑memory conversational state for simplified scaling; preserving multi‑turn context requires external state handling (session JSONL or agent backend).
• Reliance on Kapso and the WhatsApp Cloud API implies dependence on third‑party API quotas, rate limits, and billing models controlled by those services.
• Latency: default polling works with minimal config; lower latency relies on additional delivery modes or tunnel mechanisms.

Use cases
• Embedding a conversational AI agent with a dedicated WhatsApp number for task automation, notifications, or interactive workflows.
• Resource‑constrained deployments that require low idle CPU and simple runtime footprints.

openclaw #kapso #whatsapp_cloud_api #go #voice_transcription

Source: https://github.com/Enriquefft/openclaw-kapso-whatsapp

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️ Tool
===================

Executive summary:
The Zero Trust Assessment is a Microsoft PowerShell module designed to evaluate tenant configuration against Zero Trust principles and produce a local HTML report. The module performs read-only checks via Microsoft Graph and, optionally, Azure sign-in/audit log verification, and requests administrator consent on the initial connection.

Technical details:
• The module operates by authenticating to Microsoft Graph to enumerate tenant configuration and security-related settings. When available, it also connects to Microsoft Azure to verify export of audit and sign-in logs.
• The assessment is explicitly read-only and stores results locally in an output folder that contains an ZeroTrustAssessmentReport.html file and associated artifacts.
• Initial authentication requires Global Administrator consent to grant a set of Graph permissions. Subsequent assessments can run under Global Reader where applicable.

Permissions observed:

AuditLog.Read.All
CrossTenantInformation.ReadBasic.All
DeviceManagementApps.Read.All
DeviceManagementConfiguration.Read.All
Directory.Read.All
DirectoryRecommendations.Read.All
Policy.Read.All
Policy.Read.ConditionalAccess
Reports.Read.All
RoleManagement.Read.All
UserAuthenticationMethod.Read.All
PrivilegedAccess.Read.AzureAD

How it works (conceptual):
• The module queries tenant objects, policy configuration, device management settings, role and entitlement data, and authentication methods via Graph endpoints.
• If Azure sign-in is provided, additional checks validate whether audit/sign-in logs are being exported and accessible for monitoring and retention checks.

Use cases:
• Internal security reviews to benchmark tenant configuration against Zero Trust recommendations.
• Regular health checks before audits or compliance assessments.
• Pre-engagement diagnostic for third‑party security assessments (with caution about sharing results).

Limitations and considerations:
• The tool requires elevated consent on the first run; organizations must review requested Graph permissions before consenting.
• The assessment may skip Azure‑dependent checks when Azure access is not provided, producing partial results.
• Large tenants can experience runs exceeding 24 hours; the report and export folder contain sensitive tenant metadata and should be handled securely.

References & notes:
• The module name and approach indicate an endpoint‑driven audit using Graph APIs with local result storage. Additions such as custom report paths are supported conceptually.

tool #ZeroTrust #MicrosoftGraph #AzureAD #tenant_security

Source: https://learn.microsoft.com/en-us/security/zero-trust/assessment/get-started