AI
AI Agent for Microsoft Grap Red Team Framework
Hacker NewsAI Agent for Microsoft Grap Red Team Framework
a Python-based framework that uses multi-agent coordination (via CrewAI + GPT‑4) to automate the full attack lifecycle of the Microsoft Graph API (recon planning execution). Features include token-scope parsing, endpoint enumeration, autonomous multi-step attack chains (privilege escalation, persistence, lateral movement), retry logic, full logging (with curl equivalents), “crown jewel” target detection, etc.
Share cardActual performance
3points
Did not reach leaderboard
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Product HuntOn track for Day 1 leaderboard · Strong signals: agent · Missing: mac, agents, macos74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto21%21% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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