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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 PDF Password Recovery with HexStrike-AI and Gemini-CLI

From Encrypted Document to Readable Content Using LLM-Orchestrated Tooling

AI-Driven PDF 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 PDF 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 PDF file Password cracking. Guide with real life examples here:|
**https://medium.com/@1200km/pdf-file-password-cracking-guide-with-real-life-examples-901ee411a6f4

Scenario​

Objective​

  • Confirm a PDF 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 PDF:
    /path/to/secret.pdf

  • 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 ~/Documents/enc_secret.pdf. use passwords list ~/Documents/password_list.txt

Execution Flow:​

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

You issued one objective:

  • Recover access to ~/Documents/enc_secret.pdf 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 PDF” tool.

AI behavior: rather than stopping, it shifted to a plan that starts with deriving a verification artifact from the PDF (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 PDF
  • 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 PDF:

  • Recovered password: MyStrongPass

Conclusion​

This flow is a clear example of why AI-orchestrated security tooling is qualitatively different from “running commands.”

With a single high-level prompt, the system executed an end-to-end objective and — more importantly — self-troubleshot its own failures without human intervention:

  • It detected a permission boundary (writing into a protected directory), then automatically rerouted output to a user-writable workspace.
  • It hit a missing dependency in PATH , then performed environment discovery , located the tool by searching the filesystem, and continued using the correct absolute path.
  • It encountered a bad input assumption (wrong wordlist filename), then validated reality by enumerating ~/Documents, corrected the path, and retried.
  • It maintained a consistent strategy throughout: derive an offline verification artifact → validate candidates offline → confirm success.

The key takeaway is not the specific PDF outcome — it is the closed-loop execution model :

Plan → execute → observe error → diagnose → adapt → retry → validate , repeated at machine speed.

That is what “one prompt success” really means here: the user didn’t babysit the workflow. The AI treated errors as telemetry, not blockers, and completed the task by dynamically chaining tools, correcting assumptions, and converging on a verified result.

By Andrey Pautov on December 29, 2025.

Canonical link

Exported from Medium on May 15, 2026.