#Log4j

[jpmellojr]

Think Log4j is a wrap? Think again

Three years after its discovery, Log4Shell remains one of the software flaws that are most used by threat actors, a new report released by Cato Networks has found. https://jpmellojr.blogspot.com/2024/08/think-log4j-is-wrap-think-again.html

[edwardos]

I think the verdict is now very clear about when the Minecraft Silver Age ended.

[english-mace]

linux is not. magically free of the Dependencies curse, tho.

like I respect the moxie & mod-ability of an open OS as much as the next bigtech girlie but it very much is still Computer

it's honestly nuts to me that critical infrastructure literally everywhere went down because everyone is dependent on windows and instead of questioning whether we should be letting one single company handle literally the vast majority of global technological infrastructure, we're pointing and laughing at a subcontracted company for pushing a bad update and potentially ruining themselves

like yall linux has been here for decades. it's stable. the bank I used to work for is having zero outage on their critical systems because they had the foresight to migrate away from windows-only infrastructure years ago whereas some other institutions literally cannot process debit card transactions right now.

global windows dependence is a massive risk and this WILL happen again if something isn't done to address it. one company should not be able to brick our global infrastructure.

[cyber-sec]

Threat Advisory: Critical Apache Log4j vulnerability being exploited in the wild

Source: https://blog.talosintelligence.com/apache-log4j-rce-vulnerability/

[jadeghosthill]

2b2t players will post videos about "Minecraft's most DANGEROUS bug" and it's actually just a really niche glitch that lets you find someone's base and the only reason its dangerous is because 2b2t players are mentally ill

good for them but log4j existed there are more dangerous bugs i think

[aktionfsa-blog-blog]

Gesundheitsdaten nur bedingt sicher

KIM = Kaos in der Medizin

Eigentlich sollte KIM ein sicherer E-Mail Service für die Medizin, also die Kommunikation zwischen Krankenkassen und Ärzten sein. Etwas ähnliches gibt es auch seit Jahren im Bereich der Justiz für Gerichte und Anwälte. Insofern handelt es sich nicht um die grandiosiste Innovation. 

Trotzem ging es schief. Wie auf dem 37. CCC Kongress in Hamburg von dem Münsteraner Sicherheitsforscher Christoph Saatjohann vom Fraunhofer-Institut für Sichere Informationstechnologie (SIT) in Münster und Sebastian Schinzel berichtet wurde, haben insgesamt acht Krankenkassen durch die Gematik den gleichen S/MIME-Key erhalten. Sichere E-Mail beruht auf dem seit den 80-iger Jahren von Phil Zimmermann entwickelten Public-Private-Key Verfahren. In öffentlichen Einrichtungen geschieht das nach dem Standard X.509, während im privaten Umfeld Jede/r seine Schlüsselpaare selbst generieren kann.

Wenn jedoch die Zertifizierungsstellen (CAs) für verschiedene Akteure die gleichen Schlüssel verteilen, dann war es das mit der Sicherheit sensibler medizinischer Daten. Das ist der GAU in der PKI - der Public Key Infrastructure.

Laut den Sicherheitsforschern hatten, wie Heise.de schreibt, einmal drei Krankenkassen denselben im September 2021 ausgestellten Schlüssel, bei einem zweiten Schlüssel fünf. 28% der Bürgerinnen und Bürger seien über diese acht Krankenkassen versichert gewesen. Dieser Vorfall war nicht der erste mit KIM. 2022 wurde eine Log4J-Schwachstelle im KIM-Clientmodul von T-Systems gefunden.

Künftig werden die Schlüssel nun monatlich auf Dopplungen geprüft.

Mehr dazu bei https://www.heise.de/news/37C3-Schluessel-fuer-E-Mail-Dienst-KIM-fuer-das-Medizinwesen-mehrfach-vergeben-9583275.html

Kategorie[21]: Unsere Themen in der Presse Short-Link dieser Seite: a-fsa.de/d/3y7 Link zu dieser Seite: https://www.aktion-freiheitstattangst.org/de/articles/8633-20231229-gesundheitsdaten-nur-bedingt-sicher.html

[queerbogbitch]

botnets like mirai are pretty prolific, people just don't notice because nobody cares about securing routers and IoT

the modern internet needs a new mega virus. weve gone too long without having a named virus that takes out a major % of computers

[vijaysethupati]

 Debugging Full Stack Apps: Common Pitfalls and Fixes

If you’ve ever stared at your code wondering why nothing works—while everything looks fine—you’re not alone. Debugging Full Stack Apps: Common Pitfalls and Fixes is something every full stack developer becomes intimately familiar with, usually the hard way. Debugging can feel like detective work: sifting through clues, spotting red herrings, and slowly putting the pieces together.

Whether you’re knee-deep in React components or wrangling with PostgreSQL queries, bugs don’t discriminate. They can lurk in the front end, back end, or anywhere in between.

Here’s a look at common pitfalls when debugging full stack apps—and practical ways to fix them.

1. Miscommunication Between Front End and Back End

One of the most common issues arises from how the front end communicates with the back end. Sometimes, they seem to speak different languages.

Common Symptoms:

API calls returning unexpected results (or nothing at all)

Mismatched data formats (e.g., sending a string where the server expects a number)

CORS errors that mysteriously appear during deployment

Fixes:

Always double-check your request headers and response formats.

Use tools like Postman or Insomnia to simulate API requests separately from your front-end code.

Implement consistent API response structures across endpoints.

As a full stack developer, ensuring clean contracts between layers is essential. Don’t assume—it’s better to over-communicate between parts of your app than to be left scratching your head at 2 AM.

2. Version Mismatches and Package Conflicts

Let’s face it: dependency hell is real.

Common Symptoms:

Front-end not rendering after an npm install

Server crashing due to deprecated methods

Mysterious breaking changes after updating a package

Fixes:

Lock dependencies using a package-lock.json or yarn.lock file.

Regularly audit your packages with tools like npm audit or yarn audit.

Avoid updating all dependencies at once—do it incrementally and test thoroughly.

Even the most seasoned full stack developer gets tripped up here. Being methodical with updates and isolating changes can save you hours of frustration.

3. State Management Gone Wrong

If your app behaves inconsistently, the problem might be state management.

Common Symptoms:

UI doesn’t reflect expected changes

Data seems to "disappear" or update out of sync

Components re-render unnecessarily

Fixes:

Use debugging tools like Redux DevTools or Vuex Inspector to trace changes.

Store only essential data in global state—leave UI state local whenever possible.

Be cautious with asynchronous operations that update state (e.g., API calls).

Mastering state is part art, part science. As a full stack developer, understanding both front-end and back-end data flow is key to smooth state management.

4. Overlooking Server Logs and Console Errors

It’s easy to jump straight into the code—but logs often contain the breadcrumbs you need.

Common Symptoms:

500 errors with no clear origin

"Something went wrong" messages with no context

App crashing without traceable bugs

Fixes:

Always monitor the back-end logs (use console.log, but also tools like Winston or Log4js for structured logging).

Use browser developer tools to inspect network requests and console outputs.

Integrate error-tracking tools like Sentry or LogRocket.

A skilled full stack developer knows that logs are like black box recorders for your app—ignore them at your own peril.

5. Deployment-Specific Bugs

Your app runs perfectly locally—but breaks in production. Sound familiar?

Common Symptoms:

Missing environment variables

Static assets not loading

Database connection failures post-deployment

Fixes:

Use .env files carefully and securely manage environment-specific configs.

Ensure your build process includes all required assets.

Test your deployment process using staging environments before going live.

Every full stack developer eventually realizes: what works in dev doesn’t always work in prod. Always test in conditions that mimic your live environment.

Final Thoughts

Debugging Full Stack Apps: Common Pitfalls and Fixes isn’t just about technical skills—it’s about mindset. It’s easy to get overwhelmed when something breaks, but remember: every bug you squash teaches you something new.

Here are some golden rules to live by:

Reproduce the bug consistently before trying to fix it.

Break down the problem layer by layer.

Ask for a second pair of eyes—sometimes, fresh perspective is all it takes.

Being a full stack developer is like being a bridge-builder—you connect front end and back end, logic and interface, user and server. And in between, debugging is your glue.

So next time you hit a wall, take a breath, grab a coffee, and dig in. You’ve got this.

[jpmellojr]

Cloudflare report: Log4j remains top target for attacks in 2023

Log4j remained a top attack vector for threat actors in 2023, while a new vulnerability, HTTP/2 Rapid Reset is emerging as a significant threat to organizations, according to Cloudflare’s annual “Year in Review” report. https://jpmellojr.blogspot.com/2023/12/cloudflare-report-log4j-remains-top.html

[fromdevcom]

Splunk is a popular choice for log analytics. I am a java developer and really love to use splunk for production analytics. I have used splunk for more than 5 years and like its simplicity. This article is a list of best practices that I have learned from good splunk books and over my splunk usage in everyday software projects. Most of the learnings are common for any software architect however it becomes important to document them for new developers. This makes our life easier in maintaining the software after it goes live in production. Almost any software becomes difficult change after its live in production. There are some many things you may need to worry about. Using these best practices while implementing splunk in your software will help you in long run. First Thing First : Keep Splunk Logs Separate Keep splunk log separate from debug / error logs. Debug logs can be verbose. Define a separate splunk logging file in your application. This will also save you on licensing cost since you will not index unwanted logs. Use Standard Logging Framework Use existing logging framework to log to splunk log files. Do not invent your own logging framework. Just ensure to keep the log file separate for splunk. I recommend using Asynchronous logger to avoid any performance issues related to too much logging. Some popular choice of logging frameworks in Java are listed below Log4j  SLF4J Apache commons logging Logback Log In KEY=VALUE Format Follow Key=Value format in splunk logging - Splunk understands Key=Value format, so your fields are automatically extracted by splunk. This format is also easier to read without splunk too. You may want to follow this for all other logs too. Use Shorter KEY Names Keep the key name short - preferable size should be less than 10 characters. Though you may have plenty of disc space. Its better to keep a tap on how much you log since it may create performance problems in long run. At the same time keep them understandable. Use Enums For Keys Define a Java Enum for SplunkKeys that has Description of each key and uses name field as the splunk key.  public enum SplunkKey TXID("Transaction id"); /** * Describes the purpose of field to be splunked - not logged */ private String description; SplunkKey(String description) this.description = description; public String getDescription() return description; Create A Util Class To Log In Splunk Define a SplunkAudit class in project that can do all splunk logging using easy to call methods. public class SplunkAudit private Map values = new HashMap(); private static ThreadLocal auditLocal = new ThreadLocal(); public static SplunkAudit getInstance() SplunkAudit instance = auditLocal.get(); if (instance == null) instance = new SplunkAudit(); auditLocal.set(instance); return instance; private SplunkAudit() public void add(SplunkKey key, String message) values.put(key.name(), message); public void flush() StringBuilder fullMessage = new StringBuilder(); for (Map.Entry val : values.entrySet()) fullMessage.append(val.getKey()); fullMessage.append("="); fullMessage.append(val.getValue()); fullMessage.append(" "); //log the full message now //log.info(fullMessage); Collect the Splunk Parameters (a collection of key,value pairs ) in transaction and log them at the end of transaction to avoid multiple writes. Use Async Log Writer  Its recommended to use async logger for splunk logs. Async logging will perform logging in a separate thread. Below are some options  Async Logger Appender for Log4j Logback Async Appender  Setup Alerts Setup Splunk queries as alerts - get automatic notifications. Index GC Logs in Splunk Index Java Garbage Collection Logs separately in splunk.

The format of GC log is different and it may get mixed with your regular application logs. Therefore its better to keep it separate. Here are some tips to do GC log analytics using splunk. Log These Fields Production logs are key to debug problems in your software. Having following fields may always be useful. This list is just the minimum fields, you may add more based on your application domain. ThreadName  Most important field for Java application to debug and identify multithreading related problems. Ensure every thread has a logical name in your application. This way you can differentiate threads. For example transaction threads and background threads may have different prefix in thread name. Ensure to give a unique id for each thread. Its super easy to set thread names in java. One line statement will do it. Thread.currentThread().setName(“NameOfThread-UniqueId”) Thread Count  Print count of threads at any point in time in JVM. Below one liner should provide you java active thread count at any point in JVM. java.lang.Thread.activeCount() Server IP Address Logging the server IP address become essential when we are running the application on multiple servers. Most enterprise application run cluster of servers. Its important to be able to differentiate errors specific to a special server.  Its easy to get IP address of current server. Below line of code should work for most places (unless the server has multiple ip addresses) InetAddress.getLocalHost().getHostAddress() Version Version of software source from version control is important field. The software keeps changing for various reasons. You need to be able to identify exact version that is currently live on production. You can include your version control details in manifest file of deployable war / ear file. This can be easily done by maven. Once the information is available in your war/ear file, it can be read in application at runtime and logged in splunk log file. API Name Every application performs some tasks. It may be called API or something else. These are the key identifier of actions. Log unique API names for each action in your application. For example API=CREATE_USER API=DELETE_USER API=RESET_PASS Transaction ID Transaction id is a unique identifier of the transaction. This need not be your database transaction id. However you need a unique identifier to be able to trace one full transaction. User ID - Unique Identifier User identification is important to debug many use cases. You may not want to log user emails or sensitive info, however you can alway log a unique identifier that represents a user in your database. Success / Failure of Transaction Ensure you log success or failure of a transaction in the splunk. This will provide you a easy trend of failures in your system. Sample field would look like TXS=S (Success transaction) TXS=F (Failed transaction) Error Code Log error codes whenever there is a failure. Error codes can uniquely identify exact scenario therefore spend time defining them in your application. Best way is to define enum of ErrorCodes like below public enum ErrorCodes INVALID_EMAIL(1); private int id; ErrorCodes(int id) this.id = id; public int getId() return id; Elapsed Time - Time Taken to Finish Transaction Log the total time take by a transaction. It will help you easily identify the transactions that are slow. Elapsed Time of Each Major Component in Transaction If you transaction is made of multiple steps, you must also include time take for each step. This can narrow down your problem to the component that is performing slow.  I hope you find these tip useful. Please share with us anything missed in this page.

[threetrenchcoatsinatrenchcoat]

on the one hand it's kinda annoying that our digital and physical lives are coated with ads set on making us feel incomplete, for companies that then have unchecked censorship rights on their surrounding content, and political campaigns are won by the most advertised candidate, and the surveillance state created by the amount of our data being sold is used for voter suppression and stalkers, and it's burning down the planet with direct online advertising alone producing the equivalent of up to 159 metric tons of carbon dioxide emissions a year,

but hey, the internet couldn't possibly ever be run by volunteers.

except it is. right now.

XZ Utils and OpenSSL and Log4j and many projects like them are volunteer-led--OpenSSL in particular is almost entirely managed by two men named Steve. the projects have some funding sometimes but the people who fix stuff when it breaks usually aren't paid and all have other full-time jobs. we know this because it's happened, i only heard about these specific services because they've all recently had vulnerabilities that had to wait for volunteers to get off work or for one of the Steves to pause his vacation. and some big companies were relying on them.

big companies like linux and facebook and google and microsoft and amazon web services and twitter and cloudflare and apple and intuit and paypal and tumblr. y'know, basically the internet. so much of their infrastructure is volunteer code right now. if they don't need all that ad money and user data we're netting them, what are we actually getting in return?

what if we just turned the ads off? what if we just turned the ads off? what if we just turned the ads off?

what if the next time google wants to collect data to sell for drone strikes they have to fill out a grant proposal and put the notion on a ballot?

love when ppl defend the aggressive monetization of the internet with "what, do you just expect it to be free and them not make a profit???" like. yeah that would be really nice actually i would love that:)! thanks for asking

[infernovm]

Open-source Styrolite project aims to simplify container runtime security

Today Edera launched a new open-source project called Styrolite to bring tighter controls to the interactions between containers and Linux kernel namespaces, at a layer below where Open Container Initiative (OCI) runtimes like containerd operate. While software supply chain security incidents like Log4j and XZ Utils have dominated the container security headlines in recent years, the container…

[wingedmiraclesublime]

蜘蛛池需要哪些调试工具？

在SEO优化领域，蜘蛛池（Spider Pool）是一个重要的概念。它通过模拟搜索引擎的爬虫行为来提高网站的收录速度和排名。为了确保蜘蛛池能够高效运行，选择合适的调试工具至关重要。下面我们就来看看，构建和维护一个高效的蜘蛛池，需要哪些调试工具。

1. 日志分析工具

日志文件是了解蜘蛛池运行状态的重要途径。使用日志分析工具可以帮助我们快速定位问题，例如，Apache的Log4j或ELK Stack（Elasticsearch, Logstash, Kibana）都是不错的选择。这些工具可以实时监控系统日志，帮助我们发现并解决潜在的问题。

2. 性能监控工具

性能监控工具如Prometheus、Grafana等，可以让我们实时监控系统的各项指标，如响应时间、请求成功率等。

3. 网络抓包工具

网络抓包工具如Wireshark，可以帮助我们理解蜘蛛池与目标网站之间的交互过程。通过抓取网络数据包，我们可以检查HTTP请求和响应的具体内容，这对于调试和优化蜘蛛池的行为非常有帮助。

4. 自动化测试工具

自动化测试工具如Selenium，可以模拟真实用户的操作行为，���蜘蛛池进���功能性和性能测试。这有助于确保蜘蛛池能够正确地处理各种网页结构，并且在面对动态加载的内容时也能表现良好。

5. 数据库管理工具

数据库管理工具如MySQL Workbench或Navicat，对于管理和监控数据库的状态至关重要。它们可以帮助我们更好地管理存储的数据，以及优化查询效率，从而提升整体性能。

6. 编程IDE

一个好的编程集成开发环境（IDE），如PyCharm或VS Code，不仅能够提高代码编写效率，还能方便地进行单元测试和集成测试，确保每个模块都能按预期工作。

7. 版本控制工具

版本控制工具如Git，可以帮助团队协作开发，同时保证代码的版本控制和稳定性。然而，要让蜘蛛池高效运行，选择合适的调试工具是关键。本文介绍了几种常用的调试工具，包括日志分析工具、性能监控工具、网络抓包工具、自动化测试工具、数据库管理工具以及版本控制工具。每一种工具都有其独特的作用，合理利用这些工具，可以帮助我们更有效地管理和优化蜘蛛池，从而达到更好的效果。

讨论点：你平时使用哪些工具来调试你的蜘蛛池？

加飞机@yuantou2048

EPP Machine

负面删除

[ankitcodinghub]

CS6035 Log4Shell 2025 Spring Solved

Welcome! For this assignment you will exploit a real world vulnerability: Log4Shell. This will be a capture-the-flag style project where you will exploit a web application with a vulnerable version of log4j. A correct solution will output a ‘flag’ or ‘key’. There are 7 tasks to complete for 7 total flags. 6 required and 1 extra credit for a possible total of 102%. You will submit these flags in…

[thahxa]

between this and log4j is there anything minecraft kids can't do

mine craft seems like a good thing for youngsters actually. it’s creative and non violent and social to a degree. do they do a good job making sure it is safe

[educarecentre]

The Role of Log Frameworks in Academic Research and Data Management

In academic research, maintaining structured and well-documented data is essential for ensuring transparency, reproducibility, and efficient analysis. Just as log frameworks play a critical role in software development by tracking system behavior and debugging errors, they also serve as valuable tools for researcher’s handling large datasets, computational models, and digital experiments.

This article explores the significance of log frameworks in research, their key features, and how scholars can leverage structured logging for efficient data management and compliance.

What Is a Log Framework?

A log framework is a structured system that allows users to generate, format, store, and manage log messages. In the context of academic research, logging frameworks assist in tracking data processing workflows, computational errors, and analytical operations, ensuring that research findings remain traceable and reproducible.

Researchers working on quantitative studies, data analytics, and machine learning can benefit from logging frameworks by maintaining structured logs of their methodologies, similar to how software developers debug applications.

For further insights into structuring academic research and improving data management, scholars can explore academic writing resources that provide guidance on research documentation.

Key Features of Log Frameworks in Research

🔹 Log Level Categorization – Helps classify research data into different levels of significance (e.g., raw data logs, processing logs, and result logs). 🔹 Multiple Storage Options – Logs can be stored in databases, spreadsheets, or cloud-based repositories. 🔹 Automated Logging – Reduces manual errors by tracking computational steps in the background. 🔹 Structured Formatting – Ensures research documentation remains clear and reproducible. 🔹 Data Integrity & Compliance – Supports adherence to research integrity standards and institutional requirements.

For a more in-depth discussion on structured academic documentation, scholars can engage in free academic Q&A discussions to refine their research methodologies.

Why Are Log Frameworks Important in Academic Research?

1️⃣ Enhanced Research Reproducibility

Logging helps ensure that all data transformations, computational steps, and methodological adjustments are well-documented, allowing other researchers to replicate findings.

2️⃣ Efficient Data Monitoring & Debugging

Researchers working with complex datasets or computational tools can use log frameworks to track anomalies and discrepancies, much like software developers debug errors in applications.

3️⃣ Compliance with Ethical & Institutional Guidelines

Academic institutions and publishers require transparency in data collection and analysis. Proper logging ensures compliance with ethical standards, grant requirements, and institutional policies.

4️⃣ Long-Term Data Preservation

Structured logs help retain critical research details over time, making it easier to revisit methodologies for future studies.

To explore additional academic research tools and methodologies, scholars may access comprehensive digital libraries that provide authoritative research materials.

Popular Log Frameworks for Research & Data Analysis

Log4j (Java) 📌 Use Case: Computational modeling, simulation research 📌 Pros: Highly configurable, supports integration with data analysis platforms 📌 Cons: Requires security updates to prevent vulnerabilities

Serilog (.NET) 📌 Use Case: Quantitative research using .NET-based statistical tools 📌 Pros: Supports structured logging and integration with visualization tools 📌 Cons: Requires familiarity with .NET framework

Winston (Node.js) 📌 Use Case: Web-based academic data analysis platforms 📌 Pros: Supports real-time research data logging and cloud integration 📌 Cons: May require additional configuration for large-scale data processing

ELK Stack (Elasticsearch, Logstash, Kibana) 📌 Use Case: Large-scale academic data aggregation and visualization 📌 Pros: Allows powerful search capabilities and real-time monitoring 📌 Cons: Requires technical expertise for setup and configuration

How to Choose the Right Log Framework for Academic Research

When selecting a log framework for research purposes, consider:

✅ Compatibility with Research Tools – Ensure it integrates with statistical or data management software. ✅ Scalability – Can it handle large datasets over time? ✅ User Accessibility – Does it require advanced programming knowledge? ✅ Data Security & Ethics Compliance – Does it meet institutional and publication standards?

Conclusion

Log frameworks are invaluable for researchers handling data-intensive studies, ensuring transparency, reproducibility, and compliance. Whether used for debugging computational errors, tracking methodological changes, or preserving data integrity, structured logging is a critical component of academic research.

For further guidance on structuring research documents, scholars can explore academic writing resources and engage in peer discussions to enhance their methodologies. Additionally, accessing digital academic libraries can provide further insights into data-driven research.

By incorporating effective log frameworks, researchers can elevate the quality and reliability of their academic contributions, ensuring their work remains impactful and reproducible.