Libraries tagged by exploit
marcocesarato/amwscan
12426 Downloads
AMWSCAN (Antimalware Scanner) is a php antimalware/antivirus scanner console script written in php for scan your project. This can work on php projects and a lot of others platform.
phpmussel/phpmussel
193056 Downloads
PHP-based anti-virus anti-trojan anti-malware solution.
phpmussel/core
96220 Downloads
phpMussel core
lightswitch05/php-version-audit
39174 Downloads
A convenience tool to easily check a given PHP version against a regularly updated list of CVE exploits, new releases, and end of life dates
phpmussel/web
52335 Downloads
phpMussel upload handler
phpmussel/frontend
45053 Downloads
phpMussel front-end
phpmussel/cli
45425 Downloads
phpMussel CLI-mode
maikuolan/phpmussel
188 Downloads
PHP-based anti-virus anti-trojan anti-malware solution.
accentinteractive/laravel-blocker
2407 Downloads
Block bad bots and users that visit certain (exploit) urls for a set amount of time.
exploitfate/sdk-core-php
2108 Downloads
PayPal Core SDK for PHP
exploitfate/rest-api-sdk-php
2082 Downloads
PayPal's PHP SDK for REST APIs
exploitfate/merchant-sdk-php
2081 Downloads
PayPal Merchant SDK for PHP
inda-hr/php_sdk
491 Downloads
# Introduction **INDA (INtelligent Data Analysis)** is an [Intervieweb](https://www.intervieweb.it/hrm/) AI solution provided as a RESTful API. The INDA pricing model is *credits-based*, which means that a certain number of credits is associated to each API request. Hence, users have to purchase a certain amount of credits (established according to their needs) which will be reduced at each API call. INDA accepts and processes a user's request only if their credits quota is grater than - or, at least, equal to - the number of credits required by that request. To obtain further details on the pricing, please visit our [site](https://inda.ai) or contact us. INDA HR embraces a wide range of functionalities to manage the main elements of a recruitment process: + [**candidate**](https://api.inda.ai/hr/docs/v2/#tag/Resume-Management) (hereafter also referred to as **resume** or **applicant**), or rather a person looking for a job; + [**job advertisement**](https://api.inda.ai/hr/docs/v2/#tag/JobAd-Management) (hereafter also referred to as **job ad**), which is a document that collects all the main information and details about a job vacancy; + [**application**](https://api.inda.ai/hr/docs/v2/#tag/Application-Management), that binds candidates to job ads; it is generated whenever a candidate applies for a job. Each of them has a specific set of methods that grants users the ability to create, read, update and delete the relative documents, plus some special features based on AI approaches (such as *document parsing* or *semantic search*). They can be explored in their respective sections. Data about the listed document types can be enriched by connecting them to other INDA supported entities, such as [**companies**](https://api.inda.ai/hr/docs/v2/#tag/Company-Management) and [**universities**](https://api.inda.ai/hr/docs/v2/#tag/Universities), so that recruiters may get a better and more detailed idea on the candidates' experiences and acquired skills. All the functionalities mentioned above are meant to help recruiters during the talent acquisition process, by exploiting the power of AI systems. Among the advantages a recruiter has by using this kind of systems, tackling the bias problem is surely one of the most relevant. Bias in recruitment is a serious issue that affect both recruiters and candidates, since it may cause wrong hiring decisions. As we care a lot about this problem, we are constantly working on reduce the bias in original data so that INDA results may be as fair as possible. As of now, in order to tackle the bias issue, INDA automatically ignores specific fields (such as name, gender, age and nationality) during the initial processing of each candidate data. Furthermore, we decided to let users collect data of various types, including personal or sensitive details, but we do not allow their usage if it is different from statistical purposes; our aim is to discourage recruiters from focusing on candidates' personal information, and to put their attention on the candidate's skills and abilities. We want to help recruiters to prevent any kind of bias while searching for the most valuable candidates they really need. The following documentation is addressed both to developers, in order to provide all technical details for INDA integration, and to managers, to guide them in the exploration of the implementation possibilities. The host of the API is [https://api.inda.ai/hr/v2/](https://api.inda.ai/hr/v2/). We recommend to check the API version and build (displayed near the documentation title). You can contact us at [email protected] in case of problems, suggestions, or particular needs. The search panel on the left can be used to navigate through the documentation and provides an overview of the API structure. On the right, you can find (*i*) the url of the method, (*ii*) an example of request body (if present), and (*iii*) an example of response for each response code. Finally, in the central section of each API method, you can find (*i*) a general description of the purpose of the method, (*ii*) details on parameters and request body schema (if present), and (*iii*) details on response schema, error models, and error codes.
edgar/ez-uiaudit-bundle
2618 Downloads
eZ Platform audit configuration and exploitation
artisanbarista/laravel-shield
97 Downloads
Block bad bots and users that visit certain (exploit) urls for a set amount of time.