Here is a **Hashtopia-style rewrite** of your Pipal guide: more analytical, slightly more academic, and explicitly tied to _probability, structure, and workflow integration_, while keeping it practical and tool-agnostic in spirit. --- ## What Pipal Is **Pipal** is a password analysis utility designed to extract **statistical insight** from a set of recovered plaintext passwords or candidate strings. Rather than attempting to crack passwords, Pipal focuses on understanding **how passwords are constructed**, highlighting patterns that influence **guessability**, **reuse**, and **probability concentration**. Given a line-delimited list of passwords, Pipal produces metrics describing: - Length distributions - Character set usage and ordering - Common basewords and suffixes - Repeated endings and digit patterns - Dominant **Hashcat-style mask structures** These outputs help translate raw password data into **actionable understanding** about user behavior and authentication risk. The project is maintained by DigiNinja and is available here: https://www.digininja.org/projects/pipal.php --- ## When to Use Pipal Pipal is most useful when your goal is **analysis**, not extraction. Use it to answer questions such as: - *What do users actually choose, as opposed to what policy intends?* - *Where is probability mass concentrated?* - *Which structural patterns dominate the dataset?* - *How much password structure appears policy-driven?* - *Which mask families should be prioritized in controlled cracking experiments?* In Hashtopia workflows, Pipal typically sits **between data acquisition and cracking**, informing which strategies are most defensible and efficient. --- ## Inputs and Assumptions ### Input Format Pipal expects a **line-delimited text file**, with one password per line. It estimates progress using line counts (`wc`) and processes the dataset sequentially. ### Data Quality Considerations Pipal’s output is only as meaningful as its input. It is most effective when: - Obvious junk or artifacts have been removed - The dataset reasonably represents the population under study - Duplicates are handled intentionally (depending on whether frequency matters) Poorly curated inputs will produce **misleading statistics**, especially in mask and baseword analysis. --- ## Installation Notes ### Requirements According to DigiNinja’s documentation, Pipal was written for **Ruby 1.9.x** and is largely self-contained, requiring no external gems. In modern environments, compatibility should be verified if using newer Ruby versions. ### Obtaining the Tool The official project page and downloads are available here: https://www.digininja.org/projects/pipal.php --- ## Quick Start ### View Help ```bash ./pipal.rb -h ```` This displays supported flags and usage information. ### Minimal Execution ```bash ./pipal.rb passwords.txt ``` This produces a console-based report and displays a progress indicator during processing. Pipal can be safely interrupted with `Ctrl-C`; it will print the statistics collected up to that point. --- ## Core Command-Line Options Refer to DigiNinja’s documentation for the authoritative list. Commonly used options include: |Option|Purpose|Example| |---|---|---| |`--help, -h`|Display help|`./pipal.rb -h`| |`--top, -t X`|Show top X results (default: 10)|`./pipal.rb -t 25 passwords.txt`| |`--output, -o <file>`|Write report to file|`./pipal.rb -o report.txt passwords.txt`| |`--external, -e <file>`|Compare against reference list|`./pipal.rb -e corp_terms.txt passwords.txt`| --- ## Practical Analysis Workflows ### 1. Baseline Dataset Characterization **Objective:** Understand the dominant characteristics of the dataset. ```bash ./pipal.rb -t 10 passwords.txt ``` This provides immediate visibility into: - Common lengths - Frequent basewords - Popular suffixes - Dominant mask structures Use this as a first-pass “shape of the data” assessment. --- ### 2. Generating a Report Artifact **Objective:** Produce a persistent record suitable for notes, reports, or review. ```bash ./pipal.rb -o pipal_report.txt -t 20 passwords.txt ``` This creates a reproducible artifact that can be referenced alongside cracking results or methodology documentation. --- ### 3. Comparing Against Organizational Vocabulary **Objective:** Measure the presence of internal language, product names, or cultural markers. ```bash ./pipal.rb -e org_terms.txt -t 30 passwords.txt ``` This helps quantify the extent to which users incorporate **organization-specific knowledge** into passwords. --- ## Interpreting Pipal Output Pipal’s value lies in how it exposes **probability concentration**. ### Length Distribution Shorter lengths often dominate real-world datasets. This directly informs where cracking effort yields the highest return. --- ### Ending Digit Patterns Pipal explicitly reports patterns for the last 1–5 characters. Clustering here often indicates: - Years - Dates - Incrementing counters - Meaningful numeric tokens These are strong indicators of structured, non-random behavior. --- ### Character Sets and Ordering Pipal summarizes both character classes and their order (e.g., `stringdigit`, `digitstringdigit`). This highlights: - Policy-shaped behavior - Predictable capitalization - Suffix-heavy constructions --- ### Hashcat Mask Distribution Pipal reports the most common **mask patterns** observed in the dataset. This is one of the most actionable outputs, serving as a direct bridge from **analysis to cracking**, allowing you to prioritize masks that reflect observed reality rather than theoretical possibility. --- ## Common Pitfalls - **Inaccurate progress reporting** If `wc` is unavailable, Pipal estimates line counts heuristically, which can skew progress indicators. - **Mixed or polluted inputs** Including reset tokens, non-password fields, or artifacts will distort statistics and mask distributions. - **Overgeneralization** Results from small or highly specific datasets should not be extrapolated to broader populations. --- ## Relationship to Hashtopia Methodology Within Hashtopia, Pipal supports: - **Password structure and composition analysis** - **Empirical entropy vs. guessability evaluation** - **Mask and rule prioritization** - **Defensible cracking experiment design** Pipal does not replace cracking tools,it informs **how and where** they should be used. Understanding structure is often more valuable than expanding brute-force scope. --- ## Intended Outcome After using Pipal, you should be able to: - Describe how passwords are structured in your dataset - Identify high-probability constructions - Justify cracking strategies empirically - Avoid wasting effort on low-yield keyspace [[Password Pattern Analysis|Analysis]] [[Home]] #tools #howto