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:::info Last tested Kali Linux 2025.4 · HexStrike AI (Kali package 2025.4 repo) · May 2026. Results may vary on other versions. :::

AI-Driven Office Documents Password Recovery with HexStrike-AI and Gemini-CLI

From Encrypted Document to Readable Content Using LLM-Orchestrated Tooling

AI-Driven Office Documents Password Recovery with HexStrike-AI and Gemini-CLI​

From Encrypted Document to Readable Content Using LLM-Orchestrated Tooling​

Overview​

This guide shows how HexStrike-AI, orchestrated through Gemini-CLI, can autonomously handle a common, authorized security task:

Regain access to a password-protected DOCX you own (or are explicitly authorized to access), identify the encryption scheme , and restore usability — without handholding.

The core value here is not “magic cracking.” It’s the AI’s ability to reason , validate assumptions , and pivot when reality disagrees with the first plan.

This is a fully authorized, local scenario.

Full guide how to install and use HexstrikeAI here:

HexStrike on Kali Linux 2025.4: A Comprehensive Guide

**Manual Office file Password cracking. Guide with real life examples here:|
**https://medium.com/@1200km/office-file-doc-docx-ppt-password-cracking-guide-with-real-life-examples-f8e356144ca4

Scenario​

Objective​

  • Confirm a DOCX file is encrypted and determine how
  • Distinguish between user password vs owner password / permissions
  • Restore access using known credentials (password manager candidates, documented passphrases, owner-provided secrets)
  • Extract the content and retrieve the flag (CTF-style) after access is legitimately obtained

Inputs​

  • Encrypted file:
    /path/to/secret_file.txt

  • Password Dictionary:
    /path/to/wordlist.txt

Step-by-Step Execution Flow​

  • Run the HexstrikeAI server

    hexstrike_server

  • Run Gemini-CLI

    gemeni-cli

Prompt:​

@hexstrike Crack password of /path/to/secret_file.txt. use passwords list /path/to/wordlist.txt

Execution Flow:​

1) Task initiation (single high-level prompt)​

You issued one objective:

  • Recover access to /path/to/secret_file.txt using a provided candidate list
  • Proceed until the document content is readable

No manual tool selection, no pre-planned commands.

2) Tool capability gap identified​

HexStrike initially reported it didn’t have a dedicated “crack docx” tool.

AI behavior: rather than stopping, it shifted to a plan that starts with deriving a verification artifact from the docx (a representation suitable for offline validation).

3) First failure: write location permissions​

The AI attempted to save output under a system directory (/usr/lib/...) and hit Permission denied.

Pivot: it switched to a user-writable temp directory under the Gemini working area and retried.

4) Second failure: dependency not in PATH​

The helper utility needed for extraction wasn’t callable directly (command not found).

Pivot: the AI performed filesystem discovery, located the tool in a non-PATH location, and re-ran it using the full path.

5) Extraction succeeded (hash/verification artifact produced)​

With the correct tool path and a writable output directory, the AI generated the intermediate artifact successfully and prepared it for offline checking.

6) Offline candidate validation (dictionary replay)​

The AI ran an offline candidate check using:

  • The extracted artifact from the DOCX file
  • The provided wordlist

Failure: wordlist path mismatch (password_list.txt vs passwords_list.txt).

Pivot: it listed ~/Documents, confirmed the actual filename, and reran with the corrected path.

7) Success: password recovered​

After correcting the wordlist filename, the run completed and returned a valid password for the File:

  • Recovered password: MyStrongPass

Conclusion​

This DOCX flow demonstrates the real advantage of AI-orchestrated tooling: not the individual utilities, but the system’s ability to self-correct and still reach the objective from a single high-level instruction.

The key outcome is the closed-loop troubleshooting behavior:

  • Precondition validation: it verifies that the target file and the candidate list exist, are readable, and are correctly referenced (paths, filenames, permissions).
  • Environment discovery: when a required dependency or capability is missing, it doesn’t stall — it enumerates what is available and adjusts the plan accordingly.
  • Error-driven adaptation: permission issues, missing binaries, and incorrect assumptions (for example, a wrong filename in the prompt) are treated as telemetry. The AI diagnoses the failure, applies the minimal correction, and retries.
  • End-to-end convergence: the workflow remains goal-driven (recover access → validate → extract content) rather than tool-driven, which prevents “random command spam.”

This is what “one prompt success” actually means in practice: the user defines scope and intent once, and the AI handles the messy middle — environment quirks, path mistakes, and execution pivots — until it reaches a verified result.

By Andrey Pautov on December 29, 2025.

Canonical link

Exported from Medium on May 15, 2026.