Version 3 with 517M hashes and counts of password usage ordered by most to least prevalent Pwned Passwords are 517,238,891 real world passwords previously exposed in data breaches. This exposure makes them unsuitable for ongoing use as they re at much greater risk of being used to take over other accounts. They re searchable online below as well as being downloadable for use in other online system. The entire set of passwords is downloadable for free below with each password being represented as a SHA-1 hash to protect the original value (some passwords contain personally identifiable information) followed by a count of how many times that password had been seen in the source data breaches. The list may be integrated into other systems and used to verify whether a password has previously appeared in a data breach after which a system may warn the user or even block the password outright.

As of June 2025, the most significant data breach incident in the United States was the Yahoo data breach that dates back to 2013-2016. Impacting over three billion online users, this incident still remains one of the most significant data breaches worldwide. The second-biggest case was the January 2021 data breach at Microsoft, involving about 30 thousand companies in the United States and around 60 thousand companies around the world.

The list contains every wordlist, dictionary, and password database leak that I could find on the internet (and I spent a LOT of time looking). It also contains every word in the Wikipedia databases (pages-articles, retrieved 2010, all languages) as well as lots of books from Project Gutenberg. It also includes the passwords from some low-profile database breaches that were being sold in the underground years ago. The format of the list is a standard text file sorted in non-case-sensitive alphabetical order. Lines are separated with a newline " " character. You can test the list without downloading it by giving SHA256 hashes to the free hash cracker or to @PlzCrack on twitter. Here s a tool for computing hashes easily. Here are the results of cracking LinkedIn s and eHarmony s password hash leaks with the list. The list is responsible for cracking about 30% of all hashes given to CrackStation s free hash cracker, but that figure should be taken with a grain of salt because s

Breaches

## Overview

Breaches is a dataset for object detection tasks - it contains Objects annotations for 265 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Between May 2018 and September 2024, France saw eight incidents of significant data breaches. The first recorded case was the May 2018 data breach at Apollo.io. The marketing company based in San Francisco lost approximately 10.93 million data records from French users. The latest reported data breach incident occurred at the France-based security company Thales. As a result of this incident, 9.5 GB of archive files were leaked.

Introduction This dataset records all curtailment events experienced by curtailable-connection customers. About Curtailment When a generation customer requests a firm connection under a congested part of our network, there may be a requirement to reinforce the network to accommodate the connection. The reinforcement works take time to complete which increases the lead time to connect for the customer. Furthermore, the customer may need to contribute to the cost of the reinforcement works.UK Power Networks offers curtailable-connections as an alternative solution for our customers. It allows customers to connect to the distribution network as soon as possible rather than waiting, and potentially paying, for network reinforcement. This is possible because under a curtailable connection, the customer agrees that their access to the network can be controlled when congestion is high. These fast-tracked curtailable-connections can transition to firm connections once the reinforcement activity has taken place. Curtailable connections have enabled faster and cheaper connection of renewable energy generation to the distribution network owned and operated by UK Power Networks.The Distribution System Operator (DSO) team has developed the Distributed Energy Resource Management System (DERMS) that monitors curtailable-connection generators as well as associated constraints on the network. When a constraint reaches a critical threshold, an export access reduction signal may be sent to generators associated with that constraint so that the network can be kept safe, secure, and reliable.This dataset contains a record of curtailment actions we have taken and the resultant access reduction experienced by our curtailment-connections customers. Access reduction is calculated as the MW access reduction from maximum × duration of access reduction in hours (MW×h). The dataset categorises curtailment actions into 2 categories: Constraint-driven curtailment: when a constraint is breached, we aggregate the access reduction of all customers associated with that constraint. A constraint breach occurs when the network load exceeds the safe limit. Non-constraint driven curtailment: this covers all curtailment which is not directly related to a constraint breach on the network. It includes customer comms failures, non-compliance trips (where the customer has not complied with a curtailment instruction), planned outages and unplanned outages Each row in the dataset details the start and end times, durations and customer access reduction associated with a curtailment actions. We also provide the associated grid supply point (GSP) and nominal voltage to provide greater aggregation capabilities. By virtue of being able to track curtailment across our network in granular detail, we have managed to significantly reduce curtailment of our curtailable-connections customers. Methodological Approach A Remote Terminal Unit (RTU) is installed at each curtailable-connection site providing live telemetry data into the DERMS. It measures communications status, generator output and mode of operation. RTUs are also installed at constraint locations (physical parts of the network, e.g., transformers, cables which may become overloaded under certain conditions). These are identified through planning power load studies. These RTUs monitor current at the constraint and communications status. The DERMS design integrates network topology information. This maps constraints to associated curtailable connections under different network running conditions, including the sensitivity of the constraints to each curtailable connection. In general, a 1MW reduction in generation of a customer will cause <1MW reduction at the constraint. Each constraint is registered to a GSP.DERMS monitors constraints against the associated breach limit. When a constraint limit is breached, DERMS calculates the amount of access reduction required from curtailable connections linked to the constraint to alleviate the breach. This calculation factors in the real-time level of generation of each customer and the sensitivity of the constraint to each generator. Access reduction is issued to each curtailable-connection via the RTU until the constraint limit breach is mitigated. Multiple constraints can apply to a curtailable-connection and constraint breaches can occur simultaneously. Where multiple constraint breaches act upon a single curtailable-connection, we apportion the access reduction of that connection to the constraint breaches depending on the relative magnitude of the breaches. Where customer curtailment occurs without any associated constraint breach, we categorise the curtailment as non-constraint driven. Future developments will include the reason for non-constraint driven curtailment. Quality Control Statement The dataset is derived from data recorded by RTUs located at customer sites and constraint locations across our network. UKPN’s Ops Telecoms team monitors and maintains these RTUs to ensure they are providing accurate customer/network data. An alarms system notifies the team of communications failures which are attended to by our engineers as quickly as possible. RTUs can store telemetry data for prolonged periods during communications outages and then transmit data once communications are reinstated. These measures ensure we have a continuous stream of accurate data with minimal gaps. On the rare instances where there are issues with the raw data received from DERMS, we employ simple data cleaning algorithms such as forward filling. RTU measurements of access reduction update on change or every 30-mins in absence of change. We also minimise postprocessing of RTU data (e.g. we do not time average data). Using the raw data allows us to ascertain event start and end times of curtailment actions exactly and accurately determine access reductions experienced by our customers. Assurance Statement The dataset is generated and updated by a script which is scheduled to run daily. The script was developed by the DSO Data Science team in conjunction with the DSO Network Access team, the DSO Operations team and the UKPN Ops Telecoms team to ensure correct interpretation of the RTU data streams. The underlying script logic has been cross-referenced with the developers and maintainers of the DERMS scheme to ensure that the data reflects how DERMS operates. The outputs of the script were independently checked by the DSO Network Access team for accuracy of the curtailment event timings and access reduction prior to first publication on the Open Data Portal (ODP). The DSO Operations team conduct an ongoing review of the data as it is updated daily to verify that the operational expectations are reflected in the data. The Data Science team have implemented automated logging which notifies the team of any issues when the script runs. This allows the Data Science to investigate and debug any errors/warnings as soon as they happen.

Other

Download dataset information: Metadata (JSON)

Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary: https://ukpowernetworks.opendatasoft.com/pages/glossary/ To view this data please register and login.

Over ****** data breaches were recorded in Greece between October and December 2023. In the first quarter of 2021, the data breach count exceeded *** million, which was the highest figure over the observed period.

Between November 2022 and October 2023, the education saw 860 data breach cases caused by system intrusion. Basic web application attacks resulted in 161 data breaches in the finance sector. Social engineering attacks caused 158 data breaches in the construction sector.

Between November 2022 and October 2023, organizations in the education sector worldwide saw around 872 instances of data breaches caused by hacking. The professional industry ranked second, with 603 data breach cases in the measured period. Furthermore, hacking caused 598 data breach incidents in the finance sector.

According to Cognitive Market Research, the global Mobile Security Market size is USD 6.3 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 18.7% from 2024 to 2031.

North America held largest share of XX% in the year 2024 
Europe held share of XX% in the year 2024 
Asia-Pacific held significant share of XX% in the year 2024 
South America held significant share of XX% in the year 2024
Middle East and Africa held significant share of XX% in the year 2024 

Market Dynamics of Mobile Security Market

Key Drivers for Mobile Security Market

Increase in the usage of BYOD is fueling the market growth

Businesses and organizations are embracing the BYOD phenomenon, resulting in a much more productive workplace for workers. The workers utilize the company's network within working hours and when the device is outside the company's area. Thus, the necessity to protect the security and safety of corporate and financial information at remote ends grows. The security solution that companies adopt must distinguish between employees' personal data and the company's data. Individual users are also subjected to data loss when hackers attack payment information and audio and video files. Mobile security solutions are becoming more essential for all mobile users to safeguard their data. This aspect contributes to the market CAGR in the forecast period. Furthermore, artificial intelligence is inherent in various smartphones and their development. Smartphone developers, like Apple, Samsung, Huawei, and others, integrate machine learning and artificial intelligence into the creation of their smartphone models, thereby, offering promising opportunities for market expansion. In addition, based on a study, 200 mobile security app firms are likely to integrate AI artificial intelligence and machine learning into smartphones to enhance several features, including cameras and security apps. Additionally, firms are integrating artificial intelligence into smartphones and tablets to enable mobile applications and enhance the performance of several mobile devices. AI enables mobile apps to gain insights from different data produced by all users and enhances the decision-making power of the user. So, it turns out to be a profitable chance for Mobile security market revenue growth. For instance, a 2023 study found that 59% of companies have implemented BYOD policies, leading to a surge in mobile security concerns. With more employees using personal devices for work, the risk of data breaches and cyberattacks escalates, prompting companies to invest in mobile security solutions to safeguard sensitive information. Therefore, as businesses continue to prioritize securing mobile endpoints amidst the growing BYOD trend.

Key Restraints for Mobile Security Market

Lack of awareness is restraining the mobile security market

Billions of smartphone owners use their phones to access a number of applications. Most users require to be informed about the privacy policies of the applications and how they obtain them. Most individuals employ laptops or mobile phones to access untrusted websites and download unprotected software, raising the risk of cyberattack on these phones. The growth of the market is restrained by a demand for greater information regarding mobile device threats and the security products on hand to counteract them. Mobile device security threats may include malicious applications and websites, data leaks, spyware, social engineering attacks, and more. They are designed to infiltrate a network, steal data, compromise communications, and exploit vulnerabilities found in remote endpoints. Moreover, many of the people are visiting untrusted websites via laptops or mobiles while downloading insecure applications, which make these devices more vulnerable to cyberattacks. Lack of awareness about mobile device threats and their security solutions are restraining the growth of the market. For instance, a 2022 survey revealed that 43% of mobile device users are unaware of the potential risks associated with unsecured devices, making them more vulnerable to cyberattacks. This lack of understanding leads to poor security practices, such as using weak passwords or neglecting software updates. Furthermore, a report from Cybersecurity Ventures indicates that 60% of small to medium-sized businesses do not have a mobile security policy in place, which furthe...

Between November 2022 and October 2023, organizations in the professional sector worldwide saw around 429 instances of data breaches caused by malware attacks. Public administration ranked second, with 292 data breach cases in the measured period. Furthermore, malware caused 165 data breach incidents in the healthcare sector.

Security breaches, leaks, and certified erasure rates for refurbished assets.

Between November 2022 and October 2023, 67 percent of compromised information in the healthcare industry was personal data. Furthermore, 60 percent of data compromised in the manufacturing industry was personal information, while 38 percent were compromised credentials.

DATA_GEL_LEAK

## Overview

DATA_GEL_LEAK is a dataset for object detection tasks - it contains GEL LEAK annotations for 788 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [BY-NC-SA 4.0 license](https://creativecommons.org/licenses/BY-NC-SA 4.0).