2026-02-04 16:11:55
Let’s start with data warehouse. A data warehouse is a subject-oriented data management system that aggregates data from different business systems and is intended for data query and analysis. As data expands and the number of business systems increases, data warehousing becomes necessary. In order to meet the business requirements, the raw data needs to be cleansed, transformed and deeply prepared before being loaded into the warehouse. Answering the existing business questions is the data warehouse’s core task. Those questions must be already defined.
But what if a business question is not defined (which is potential data value)? According to the data warehouse’s rule, a business question is asked first and then a model is built for it. The chain of identifying, raising and answering questions thus becomes very long. On the other hand, the data warehouse, as it stores highly prepared data, has to obtain desired data by processing the raw data when the new question requires fine data granularity. This is extremely cost-ineffective. If there are many such new questions, a query process will be overburdened.
So, in the context of this background, the data lake was born. It is a technology (or strategy) intended to store and analyze massive amounts of raw data. It enables to load as much raw data as possible into the data lake while keeping the highest fidelity as possible in storing it, and, in theory, extracting any potential data value based on full data. Speaking of this, the data lake’s two roles are absolutely obvious. One is data storage because the data lake needs to keep all raw data. The other is data analysis, which, from the technical point of view, is data computing, or the value extraction process.
Let’s look at the data lake’s performance in the two aspects.
The data lake stores full raw data, including structure data, semi-structured data and unstructured data, in its original state. The capacity of storing massive and diverse data is thus the data lake’s essential feature, which is different from the data warehouse that often uses databases to store structured data. Besides, loading data into the lake as early as possible helps fully extract value from association of differently themed data and ensure data security and integrity.
The good news is that the massive raw data storage needs can be fully met thanks to the great advance of storage and cloud technologies. Enterprises can choose self-built storage cluster or the storage service provided by a cloud vendor to deal with their business demands.
But, the toughest nut to crack is data processing! The data lake stores various types of data and each needs to be processed differently. The central and the most complicated part is structured data processing. With both historical data and newly generated business data, data processing mainly focuses on structured data. On many occasions, computations of semi-structured data and unstructured data will eventually be transformed to structured data computations.
At present, SQL-based databases and related technologies, which are also the abilities data warehouses have, dominate structured data processing field. In other words, the data lake depends on data warehouses (databases) to compute structured data. That is nearly all data lake products do. Building the data lake to store all raw data and then the data warehouse to add data processing capability catering to business needs of enterprises. As a result, data in the lake needs to be loaded to the data warehouse again through ETL. An advanced approach automates the process to some degree. The approach identifies data in the lake that needs to be loaded to the warehouse and performs the loading while the system is idle. This is the main functionality of the currently hot concept of Lakehouse. But, no matter how data is loaded to the warehouse (including the extremely inefficient method that lets the warehouse access data lake through the external table), today’s data lake is made up of three components – massive data storage, data warehouse and a specialized engine (for, like, unstructured data processing).
There are problems about this type of data lake framework.
Data lakes are expected to meet three key requirements – storing data in its original state (loading high fidelity data into the lake), sufficient computing capacity (extracting the maximum possible data value) and cost-effective development (which is obvious). The current technology stack, however, cannot achieve all the three demands at the same time.
Storing data as it is was the initial purpose of building the data lake because keeping the original data unaltered helps to extract the maximum value from it. The simplest way to achieve the purpose is that the data lake uses a completely same storage medium to store data loaded from the source. There will be, for instance, a MySQL to hold data originally stored in MySQL, a MongoDB to receive data initially stored in MongoDB, and so on. This helps load data into the lake in as hi-fi format as possible and make use of the source’s computing ability. Though achieving computations across data sources is still hard, it is enough to handle computations only involving the current source’s data, meeting the basic requirement of sufficient computing power (as part i in the above figure shows).
But the disadvantage is noticeable – the development is too expensive. Users need to put same storage mediums in place and copy all data sources accumulated over years to them. The workload is ridiculously heavy. If a data source is stored with commercial software, purchasing the software further pushes up the development cost. A relief strategy is to use a storage medium of same type, like storing Oracle data in MySQL, but it brings a side effect while the costs still stay high – some computations that could have been handled could become impossible or hard to achieve.
Now, let’s lower the bar. We don’t demand that data be duplicated at loading but just store data in the database. By doing this, we obtain the database’s computing ability and meet the requirement of cheap development (as part ii in the above figure shows) at the same time. But this is infeasible since it heavily depends on one relational database into which all data needs to be loaded.
Information may be easily lost during the loading process, which will fall short of the first requirement of building the data lake (loading high-fidelity data into the lake). Storing MongoDB data in MySQL or Hive is hard, for instance. Many MongoDB data types and relationships between sets do not exist in MySQL, such as the set data type like nested data structure, array and hash, and the instances of many-to-many relationship. They cannot be simply duplicated in the course of data migration. But rather, certain data structure needs to be restructured before the migration. That requires a series of sophisticated data reorganization steps, which is not cost-effective but needs a lot of people and time to sort out the business target and design appropriate form of target data organization. Without doing this, information will be lost, and errors, in turn, appear during the subsequent analysis. Sometimes errors are too deeply hidden to be easily visible.
A general approach is to load data unalterably into large files (or as large fields in the database). This way the information loss is within an acceptable range and data basically remains intact. File storage has many advantages. It is more flexible, more open, and has higher I/O efficiency. Particularly, storing data in files (or in a file system) is cheaper.
Yet, the problem of file storage is that files/large fields do not have computing capacity, making it impossible to meet the requirement of convenient/sufficient computing power. It seems that the impossible triangle is too strong to break.
No approach can resolve the conflict between the demand for storing data in its initial state and the convenient use of it. Under the requirement for cost-saving lake building (loading data to the lake fast), high fidelity data loading and convenient/sufficient computing power are mutually exclusive. This goes against the data lake’s goal of openness.
The underlying cause of the conflict is the contradiction between the closed database and its strict constraints. The database requires that data be loaded into it for computations and data needs to meet certain database constraints before being able to be loaded. In order to conform to the rules, data needs to be cleansed and transformed. And information loss happens during the process. Abandoning databases and switching to other routes (like files) cannot satisfy the demand of sufficient computing power, except that you turn to hardcoding. But hardcoding is too complicated and not nearly as convenient as databases.
Actually, an open computing engine can become the breaker of the impossible triangle. Such an engine possessing sufficient and convenient computing power can compute the raw data, including data stored in diverse data sources, in real time.
The open-source SPL is a structured data computing engine that provides open computing power for data lakes. It has diverse-source mixed computing capability that enables to compute raw data stored in different sources directly and based on its original status. No matter which storage mediums the data lake uses – same types as data sources or files, SPL can compute data directly and perform the data transformation step by step, making the lake building easier.
Diverse-source mixed computing ability
SPL supports various data sources, including RDB, NoSQL, JSON/XML, CSV, Webservice, etc., and mixed computations between different sources. This enables direct use of any type of raw data stored in the data lake and extraction of its value without the “loading” step and preparation. And this flexible and efficient use of data is just one of the goals of data lakes.
Being agile like this, the data lake will be able to provide data services to applications as soon as it is established rather than after the prolonged cycle of data preparation, loading and modeling. The more flexible data lake service enables real time response to business needs.
Particularly, SPL’s good support for files gives powerful computing ability to them. Storing lake data in a file system can also obtain computing power nearly as good as, even greater than, the database capability. This introduces computing capacity on the basis of part iii and makes the originally impossible triangle feasible.
Besides text files, SPL can also handle data of hierarchical format like JSON naturally. Data stored in NoSQL and RESTful can thus be used directly without transformation. It’s really convenient.
All-around computing capacity
SPL has all-around computational capability. The discrete data set model (instead of relational algebra) it is based arms it with a complete set of computing abilities as SQL has. Moreover, with agile syntax and procedural programming ability, data processing in SPL is simpler and more convenient than in SQL.
SPL boasts a wealth of class libraries for computations.
Accessing source data directly
SPL’s open computing power extends beyond data lake. Generally, if the target data isn’t synchronized from the source to the lake but is needed right now, we have no choice but to wait for the completion of synchronization. Now with SPL, we can access the data source directly to perform computations, or perform mixed computations between the data source and the existing data in the lake. Logically, the data source can be treated as part of the data lake to engage in the computation so that higher flexibility can be achieved.
SPL’s joining makes data warehouse optional. SPL has all-around, remarkable computing power and offers high-performance file storage strategies. ETLing raw data and storing it in SPL storage formats can achieve higher performance. What’s more, the file system has a series of advantages like flexible to use and easy to parallelly process.
SPL provides two high-performance storage formats – bin file and composite table. A bin file is compressed (to occupy less space and allow fast retrieval), stores data types (to enable faster retrieval without parsing), and supports the double increment segmentation technique to divide an append-able file, which facilitates parallel processing in an effort to further increase computing performance. The composite table uses column-wise storage to have great advantage in handling scenarios where only a very small number of columns (fields) is involved. A composite table is also equipped with the minmax index and supports double increment segmentation technique, letting computations both enjoy the advantages of column-wise storage and be more easily parallelly processed to have better performance.
It is easy to implement parallel processing in SPL and fully bring into play the advantage of multiple CPUs. Many SPL functions, like file retrieval, filtering and sorting, support parallel processing. It is simple and convenient for them to automatically implement the multithreaded processing only by adding the @m option. They support writing parallel program explicitly to enhance computing performance.
In addition, SPL supports a variety of high-performance algorithms SQL cannot achieve, the commonly seen TopN operation, for example. It treats calculating TopN as a kind of aggregate operation, which successfully transforms the highly complex sorting to the low-complexity aggregate operation while extending the field of application.
The SPL statements do not involve any sort-related keywords and will not trigger a full sorting. The statement for getting top N from a whole set and that from grouped subsets are basically the same and both have high performance. SPL boasts many more such high-performance algorithms.
Assisted by all these mechanisms, SPL can achieve performance orders of magnitude higher than that of the traditional data warehouses. The storage and computation issues after data are transformed are solved. Data warehouses won’t be a data lake necessity any longer.
Furthermore, SPL can perform mixed computations directly on/between transformed data and raw data by making good use of values of different types of data sources rather than by preparing data in advance. This creates highly agile data lakes.
SPL enables performing lake building phases side by side while, conventionally, they can only be performed one by one (loading, transformation and computation). Data preparation and computation can be carried out concurrently and any type of raw, irregular data can be computed directly. Dealing with the computation and the transformation at the same time rather than in serial order is the key to building an ideal data lake.
2026-02-04 16:08:29
Data modeling in Power BI involves creating a semantic model (also called a dataset or data model) that connects tables through relationships, enabling analysis, calculations, and visualizations. A semantic model combines tables from Power Query, column metadata, and defined relationships. These relationships allow filters and aggregations to propagate correctly across tables, turning raw data into actionable insights.
Data modeling in power BI emphasises thinking in terms of fact tables and dimension tables, drawing from dimensional modeling principles.
Fact tables contain numeric data about business processes (e.g., sales, inventory, or hours worked). Each row represents an event, with columns for measures like quantities, amounts, or rates. Example: A "Hours" table with columns for TotalHoursBilled, HourlyRate, and HourlyCost.
Dimension tables store descriptive attributes about entities (people, places, things). They provide context for facts, with rows for each unique entity and columns like names, dates, locations, or categories. Examples: A "People" table (employee details) or "Calendar" table (dates, months, years).
It is recommended that we identify facts and dimensions early in design, using a bus matrix (a Kimball technique) to map which dimensions connect to which facts. This creates an organized overview of grain (detail level), key measures, and relationships.
Good modeling starts by identifying facts (what to measure) and dimensions (how to analyze them).
Relationships link tables so calculations work across them. In Power BI's Model view, create them by dragging columns or using Manage relationships.
Key concepts of relationships
1.Cardinality:
One-to-many (or many-to-one): Preferred; unique values on the "one" side (often dimensions) link to multiple rows on the "many" side (facts).
One-to-one: Rare, for unique matches.
Many-to-many: Allowed but avoided due to complexity and resource use.
Single: Filtering flows one way (typically dimension → fact).
Both: Bidirectional; use sparingly to avoid ambiguity.
Power BI often autodetects relationships based on column names and data.
Star Schemas are the output of a practice known as Dimensional Modelling. The objective is to design data models that are optimised for reporting and analysis.
It's not as scary as it sounds! Dimensional modelling seeks to model the business domain and capture semantics to structure the data in way that reflects the real world.
A Star Schema (like any schema for tabular data) is composed of tables, which are composed of columns. Relationships exist between the tables.
In a Star Schema there are two types of table:
Dimension tables capture key attributes of a record in the fact table - allowing you to answer questions such as:
When? exploring points in time, or grouping data over time periods such as days, months, years.
Who? by representing organisations, departments or even individual people.
What? identifying specific products or services.
Where? understanding relationships to physical and virtual locations.
Fact tables store the numerical data / transactions that you want to aggregate, group, filter, slice and dice (using the dimensions above) to deliver a specific actionable insight.
The simplest Star Schema has one fact table and a small number of dimension tables. Each of the dimension tables has a "one to many" relationship with the fact table. When arranged on an entity relationship diagram, this leads to a star-like arrangement, hence the name Star Schema! This is illustrated below:
Power BI is optimised to work with Star Schemas. What it boils down to is:
Data models that are simple. They have a smaller number of tables and relationships. Therefore, they are easier to understand and evolve.
Memory requirements are minimised - the data structures in a star schema are well suited to the column-store approach used by the VertiPaq engine at the heart of Power BI. This means that is able to compress the data minimising the memory footprint.
Compute requirements are minimised - a star schema is optimised for analytical workloads, queries over the data involve very few joins across tables, therefore the amount of compute required to return analytics is reduced.
Better user experience - better performance when querying and interacting with the data.
Maximising value from data - by unlocking all of the features in Power BI such as DAX measures or interactive features in visuals.
The data model that underpins every Power BI report implements the Star Schema as tables and relationships. DAX based measures are then layered on top of this data model to generate the analytics that are surfaced in the report.
This concept is illustrated below:
Good data modelling is critical to the success of any Power BI solution.
In Power BI, the data model is the foundation of all reporting and analysis. Separating data into fact and dimension tables, using a star schema, defining clear relationships, and avoiding unnecessary complexity are essential best practices. Investing time in good data modelling results in faster reports, accurate insights, and more reliable decision-making across the organization.
2026-02-04 16:08:28
The WCAG (Web Content Accessibility Guidelines) is a set of recommendations for making web content more accessible. It is developed by W3C's WAI, primarily for people with disabilities - but also for all user agents, including some highly limited devices or services, such as digital assistants.
WCAG is not detailed for PDF
WCAG offers high-level principles such as making content perceivable, operable, understandable, and **robust **which apply broadly across digital formats. These principles help guide the creation of accessible content, but do not cover the technical specifics required to make a PDF truly accessible.
For example, WCAG will state that content should be navigable and readable with assistive technologies, but it won't explain how to properly tag a PDF, define reading order, or add alternative text within a PDF file. These are technical requirements unique to PDF, which WCAG doesn't address in depth.
That's why, when it comes to PDFs, WCAG compliance is often interpreted through the lens of PDF/UA (PDF Universal Accessibility) - the ISO standard specifically designed for accessible PDF documents. While WCAG sets the accessibility goals, PDF/UA provides the technical blueprint for achieving them within the PDF format.
PDF/UA is an ISO standard that comes in two parts:
ISO 14289-1: Electronic document file format enhancement for accessibility – Part 1: Use of ISO 32000-1 (PDF/UA-1)
ISO 14289-2: Document management applications — Electronic document file format enhancement for accessibility Part 2: Use of ISO 32000-2 (PDF/UA-2)
WCAG and PDF/UA are not the same
WCAG outlines what is required for the accessibility for PDFs, but not the how this is technically achieved. While WCAG outlines basic accessibility principles, it leaves many of the specific technical details for documents like PDFs to other standards, such as PDF/UA.
WCAG compliance for PDFs is often understood to be equivalent to PDF/UA compliance, with some minor modifications. To cut it short,
_WCAG for PDF = PDF/UA + contrast requirements of WCAG - XMP metadata identification of PDF/UA + extra minor sanity checks.
_
These extra sanity checks are formally defined https://pdf4wcag.com/validate/wcag-2-2-machine. We provide more details below.
Tagged PDF
To ensure accessibility, the PDF document must be tagged. Tagging adds structure to the content, allowing assistive technologies to interpret and navigate the document correctly. PDF/UA (PDF Universal Accessibility) covers all relevant accessibility requirements for tagged PDFs, ensuring they meet the necessary standards for structure, reading order, and usability for people with disabilities.
PDF4WCAG visualizes structure tree in the right pane of the error preview. We do recommend our other tool ngPDF for inspecting the structure elements of the document with all their properties and attributes.
See also Questions and Answers about Tagged PDF from PDF Association.
Contrast checks
A notable difference between WCAG and PDF/UA is the contrast requirements found in WCAG, which are typically not covered in PDF/UA but are still essential for PDF accessibility.
WCAG Success Criterion 1.4.3 Contrast (Minimum) provides all the details. In short, it requires all text to have contrast ratio at least 4.5:1, with exception of large text (18pt or higher) which is required to have a contrast ratio at least 3:1.
PDF4WCAG includes these contrast checks into both its WCAG 2.2 Machine and Human profiles and implements full support for all PDF color models with computing the contrast.
Less Metadata requirements for WCAG
PDF/UA includes requirements for so-called identification Metadata, which identify PDF documents as PDF/UA compliant. While they provide very useful technical information and must be present in all PDF/UA-compliant documents, they are not explicitly covered by WCAG Success Criterions. So, when validating PDF document against WCAG profiles, PDF4WCAG relaxes these PDF/UA requirements and does not report missing identification metadata as a WCAG error.
It should be noted that there are some other Metadata that are still required by both PDF/UA and WCAG, such as presence of dc:title (Dublin Core) property in the PDF document metadata. It is equivalent to the WCAG Success Criterion 2.4.2 Page Titled.
Basic sanity checks defined in PDF4WCAG
The WCAG 2.2 Machine Validation for PDFs offers a set of basic sanity checks to ensure that PDF documents meet essential accessibility criteria.
PDF4WCAG tool performs the following additional checks based on the WCAG 2.2 guidelines:
1. Document Structure and Tagging
Sanity checks ensure that the document is properly tagged. For PDF 1.7 (or earlier) documents this includes checking the provisions of ISO 32000-1 (PDF 1.7) specification for the structure tree. In case of PDF 2.0 documents PDF4WCAG implements full validation of the structure tree against the schema defined in the additional ISO Technical Specification 32005 - PDF 1.7 and 2.0 structure namespace inclusion in ISO 32000-2.
2. Tagged Links and Annotations
Following WCAG Success Criterion 2.4.9 Link Purpose (https://www.w3.org/TR/WCAG22/#link-purpose-link-only) PDF4WCAG this check ensures that links are tagged with meaningful, descriptive text rather than generic terms like.
3. Page orientation
Following WCAG Success Criterion 1.3.4 Orientation all pages of the PDF document are required to have the same orientation.
4. Non-empty structures
Empty paragraphs, section headings, table of content items may cause unexpected behaviour of screen readers and are detected and reported as potential WCAG issues.
By using PDF4WCAG, you can automate this validation process, saving time and ensuring your PDFs comply with the latest accessibility standards effortlessly.
2026-02-04 16:08:01
Read the original article：Generate SM2 Key Pair Using Key Parameters for Encryption and Decryption
In SM2 encryption and decryption, HarmonyOS requires ASN.1 serialized key data (91-byte public key, 51-byte private key). However, most SM2 key data is provided as raw, unserialized data (64-byte public key, 32-byte private key), which cannot be used directly.
How can raw SM2 keys be converted into ASN.1 serialized SM2 key pairs that are usable on the HarmonyOS platform?
You need to reconstruct SM2 keys from the raw parameters by generating public and private keys using cryptoFramework with ASN.1 specification.
/**
* Generate SM2 public key based on public key parameters
* @param keyStr The general format of the public key parameter is 04 + x + y.
* @returns SM2 public key
*/
async function convertStrToPubKey(keyStr: string): Promise<cryptoFramework.PubKey> {
let pubKeyStr = keyStr.startsWith("04") ? keyStr.slice(2) : keyStr;
let pkPart1 = pubKeyStr.slice(0, pubKeyStr.length / 2);
let pkPart2 = pubKeyStr.slice(pubKeyStr.length / 2);
// Enter hexadecimal in the corresponding position
let pk: cryptoFramework.Point = {
x: BigInt("0x" + pkPart1),
y: BigInt("0x" + pkPart2),
}
// Public key object parameters
let pubKeySpec: cryptoFramework.ECCPubKeySpec = {
params: cryptoFramework.ECCKeyUtil.genECCCommonParamsSpec('NID_sm2'),
pk: pk,
algName: "SM2",
specType: cryptoFramework.AsyKeySpecType.PUBLIC_KEY_SPEC
}
let keypairGenerator = cryptoFramework.createAsyKeyGeneratorBySpec(pubKeySpec);
return await keypairGenerator.generatePubKey();
}
/**
* Generate SM2 private key based on private key parameters
* @param keyStr The private key parameter is generally a 128-bit string.
* @returns SM2 private key
*/
async function convertStrToPriKey(keyStr: string): Promise<cryptoFramework.PriKey> {
let sk = BigInt("0x" + keyStr);
// Private key object parameters
let priKeySpec: cryptoFramework.ECCPriKeySpec = {
params: cryptoFramework.ECCKeyUtil.genECCCommonParamsSpec('NID_sm2'),
sk: sk,
algName: "SM2",
specType: cryptoFramework.AsyKeySpecType.PRIVATE_KEY_SPEC
}
let keypairGenerator = cryptoFramework.createAsyKeyGeneratorBySpec(priKeySpec);
return await keypairGenerator.generatePriKey();
}
For details, refer to the document SM2 Encryption and Decryption.
SM2 Encryption and Decryption - HarmonyOS Documentation
How do I encrypt and decrypt public and private keys?
2026-02-04 16:05:33
A tale of curiosity, incompetence, and why you should never trust a software engineer who makes more than you.
It started, as most disasters do, with mild curiosity and a free afternoon.
I downloaded an application. Not because I'm a hacker. Not because I'm conducting corporate espionage. Not because I have any idea what I'm doing. I downloaded it because I wanted to use it.
Revolutionary concept, I know.
The installer was a .exe file. For the uninitiated, this is the software equivalent of a wrapped gift. And like any gift from a stranger on the internet, I decided to unwrap it.
"What's inside?" I wondered, the way a child wonders what's inside a clock before destroying it with a hammer.
Every modern desktop application, it turns out, is just a website pretending to be software.
It's like finding out your "homemade" meal came from a freezer bag—technically real, philosophically disappointing.
This particular application was built with Electron, which means somewhere inside was a file called app.asar. Think of it as a zip file that really, really wants you to think it's not a zip file.
I extracted it:
npx asar extract app.asar ./unpacked
Inside was JavaScript. Thousands of lines of minified, obfuscated JavaScript that looked like someone had sneezed on a keyboard and called it architecture.
And there, sitting in the open like a wallet on a park bench, was a .env file.
For those blissfully unaware, a .env file is where developers store secrets. API keys. Database credentials. The sort of things you absolutely, positively, under no circumstances should ship to production.
It's Security 101. Literally. It's the first thing they teach you:
🚨 Rule #1 of Software Development: Don't commit your .env file.
This is not advanced knowledge. This is not arcane wisdom passed down through generations of security researchers.
This is the "wash your hands after using the bathroom" of software development.
And yet.
There it was. Gleaming. Unencrypted. Full of credentials.
I won't name names. I won't point fingers. I'll simply describe what I found, in the same way a nature documentary describes a lion eating a gazelle: with clinical detachment and mild horror.
| Discovery | Severity | My Reaction |
|---|---|---|
| API Keys | 🔴 Critical | Multiple. Active. Expensive. |
| Infrastructure URLs | 🔴 Critical | Internal endpoints. Very not public. |
| Service Credentials | 🟠 High | Analytics logging everything. |
| ML Inference Endpoints | 🟠 High | Cloud GPUs go brrrr on their dime. |
The total potential exposure?
Let's just say it was significant enough that I briefly considered a career change.
Now, here's where it gets personal.
The engineer who shipped this? Based on industry averages, location, and the general state of the tech job market, they're probably making around $300,000 a year.
Three. Hundred. Thousand. Dollars.
To do the software equivalent of leaving your house keys under the doormat, except the doormat is see-through and you've put up a sign that says "KEYS UNDER HERE."
I'm not bitter. I'm not bitter at all.
I am simply noting, for the record, that I—a person of humble curiosity—managed to find this in approximately forty-five minutes of casual investigation while eating leftover pizza.
Meanwhile, somewhere, a senior software engineer is collecting stock options.
📍 Plot twist: The pizza was cold. The credentials were not.
Having found the obvious vulnerabilities, I did what any responsible researcher would do: I kept looking.
The JavaScript bundle was minified, but minification is obfuscation in the same way a trench coat is a disguise. It technically conceals things, but anyone who looks for more than five seconds can see what's underneath.
I found:
Each discovery was like opening a nesting doll, except instead of smaller dolls, it was smaller failures.
If you've made it this far, you might be expecting a dramatic conclusion. A confrontation with the company. A bug bounty payout. A heartfelt apology from a CEO.
Instead, I'll offer you something more valuable: a lesson.
Your build pipeline is not a security feature. Electron apps are zip files with extra steps. Minification is not encryption.
And for the love of all that is holy, check what you're shipping before you ship it.
# Before you ship, maybe run:
npx asar extract your-app.asar ./check-this
grep -r "API_KEY\|SECRET\|PASSWORD" ./check-this
That engineer you're paying $300,000?
Maybe budget $50 for a security audit. I'll do it. I'm available. I have pizza.
Every application you download is a mystery box.
The mystery is usually "how badly is my data being handled?"
The answer is usually "badly."
I want to be clear: I didn't exploit anything.
I didn't access systems I wasn't supposed to. I looked at what was shipped to me, as a user, in an application I downloaded from their official website.
Everything I found was sitting in a package that anyone with fifteen minutes and a search engine could have extracted. The only sophisticated tool I used was npm and a vague sense of disbelief.
This article names no names. Points no fingers that haven't already been pointed by the act of shipping credentials in a desktop application.
I still use the application. It's actually quite good.
I just use it with the quiet knowledge that somewhere, in a data center, there's a server running endpoints I wasn't supposed to know about, processing requests through an API I could technically call, protected by credentials that are sitting in my Downloads folder.
The download button that started all this sits innocently on their website, cheerfully inviting users to install their app.
Beneath it, there should probably be a disclaimer:
"By downloading this software, you agree to receive a free education in application security."
If you ship Electron apps, please check:
.env files in your build.asar fileIf you found credentials in your own app while reading this, you're welcome.
If you're the $300k engineer who shipped this... we should talk.
The author is a security researcher in the same way that someone who finds a wallet on the ground is a "detective." DMs are open. Pizza recommendations welcome.
2026-02-04 16:01:39
In a world where AI can fake anything...
How do you prove what's real?
We're building the answer.
Coming soon.