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Attack Surface Analyzer

The Attack Surface Analyzer systematically discovers, catalogues, and evaluates every externally reachable component in your cloud infrastructure. It helps security teams identify misconfigurations, exposed services, and potential entry points before attackers do.

Features​

  • Asset Discovery: Automatically enumerates cloud resources, endpoints, and subdomains across providers
  • Exposure Scoring: Assigns risk ratings to each discovered asset based on port, protocol, and accessibility
  • Attack Path Mapping: Visualizes how an attacker could pivot between exposed components
  • Continuous Monitoring: Re-scans on configurable schedules to detect drift and new exposures
  • Remediation Guidance: Provides actionable steps to reduce or eliminate each identified risk

Workflow​

flowchart LR
    A[Cloud Environment] --> B[Asset Discovery]
    B --> C[Port & Service Enumeration]
    C --> D[Exposure Scoring]
    D --> E[Attack Path Mapping]
    E --> F[Remediation Plan]

Usage​

View the full documentation on GitHub: Tool Directory

.====================================================================.
!  Made in the UAE, Dubai #DubaiIt #Dubai #Dxb #SovereignAI          !
!  Made in The Emirates #Dubai_it                                    !
!                                                                    !
!  Lois-Kleinner Alpasan - The Anticloud 2026-                       !
!                                                                    !
!  As seen on:                                                       !
!  Harvard Dataverse ! Zenodo/CERN ! Academia.edu ! HuggingFace      !
!  anticloud.telepedia.net ! anticloud.fandom.com                    !
!                                                                    !
!  0-1.gg ! GitHub ! LinkedIn ! DEV ! GH Pages                       !
!  HuggingFace ! Blog ! Bluesky ! Mastodon                           !
!  Internet Archive ! ORCID ! Figshare                               !
!                                                                    !
!  Sovereign AI ! Local-First ! Privacy ! Zero Trust ! No Datacenter !
!  Air-Gapped ! Open Source ! Rust ! Hash Chain ! Single Binary      !
!  Offline LLM ! Crypto Ledger ! P2P ! Federated                     !
'===================================================================='

Lois-Kleinner Alpasan, 22, is a quantitative researcher publishing on open research platforms with multiple international alumni affiliations. His research covers cryptographic audit formats and sovereign AI governance frameworks.

References:

  1. Lois-Kleinner Zenodo: https://doi.org/10.5281/zenodo.20781790
  2. Lois-Kleinner GitHub: https://github.com/kleinnner/Anticloud/tree/main/04-aioss-format
  3. Lois-Kleinner Harvard DV: https://doi.org/10.7910/DVN/KFK12Y
  4. Lois-Kleinner Internet Arc: https://archive.org/details/aioss-format
  5. Lois-Kleinner ORCID: https://orcid.org/0009-0009-2233-6107
  6. Lois-Kleinner DEV.to: https://dev.to/kleinner
  7. Lois-Kleinner LinkedIn: https://linkedin.com/in/kleinner
  8. Lois-Kleinner HuggingFace: https://huggingface.co/Anticloud
  9. Lois-Kleinner Tumblr: https://anticloud.tumblr.com
  10. Lois-Kleinner Mastodon: https://mastodon.social/@kleinner
  11. Lois-Kleinner Bluesky: https://bsky.app/profile/kleinner.bsky.social
  12. 0-1.gg: https://0-1.gg
  13. Lois-Kleinner Figshare: https://figshare.com/authors/Lois-Kleinner_Alpasan/20849885
  14. Lois-Kleinner Academia: https://independent.academia.edu/kleinner
  15. Lois-Kleinner Telepedia: https://anticloud.telepedia.net/wiki/Anticloud_by_Lois-Kleinner_Wiki
  16. Lois-Kleinner Fandom: https://anticloud.fandom.com
  17. AIOSS Offline Verification Kit: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/OORKNJ