At Peakhour, we are seeing more automation tools used to simplify interaction with web platforms. These tools have legitimate uses, including automating repetitive tasks and testing applications, but they can also be misused. OpenBullet is one example: a flexible web testing suite that has become a common tool for web attacks such as credential stuffing. This article explains how OpenBullet works, why it creates risk, which libraries it relies on, and how defenders can detect and block it.
Overview of OpenBullet
OpenBullet is an automation suite for scraping, parsing data, and automated penetration testing. It is commonly used by bot developers for automated attacks, including credential stuffing. Released under the MIT open-source licence on GitHub, it is now in its second version, OpenBullet2, which, as of March 2023, had over 1.1K stars and was forked roughly 370 times.
It is particularly favoured by people with limited programming knowledge because it is easy to use and supports third-party plugins. The tool uses configurations that define the actions to perform on a website, and those configurations are easy to find online.
Types of Actions with OpenBullet
The actions OpenBullet can perform are categorised by the framework and library used. There are three broad types:
- Browser Actions: Open or close tabs, maximise or minimise the browser window, and more.
- Page Actions: Visit a page, fetch page attributes, set or clear cookies, click on page elements, take screenshots, and so forth.
- Element Actions: Set or get element attributes, click on elements, check their status, fill in text forms, and more.
OpenBullet's versatility has made it attractive to users who share configurations freely. Advanced configurations for tasks such as scraping and credential stuffing can be found on forums and even sold.
OpenBullet Versus Other Testing Suites
One of OpenBullet's main advantages over other testing suites or automation frameworks is ease of use. It offers a visual mode, with a simple UI instead of lines of code. It also includes a high-level programming language for fine-tuning operations. It does not offer the same level of control as direct interaction with its underlying frameworks, but it can still cause significant issues for websites.
Why OpenBullet is Dangerous
OpenBullet is a threat because its simple UI lets people without programming skills create automated sequences for web attacks. Its integration with CAPTCHA farms also makes it effective against websites that rely on traditional CAPTCHAs for bot protection.
After installing OpenBullet, an attacker needs to create or import a configuration and manage bot behaviour. They can also configure proxies to distribute attacks, hide their real IP addresses, and sidestep traditional rate limiting.
OpenBullet also supports attacks like credential stuffing through a range of integrations. Attackers can add new credentials, store valid credentials, and set the configuration to run for any duration they choose.
We can, however, counter OpenBullet by detecting and blocking the frameworks on which it relies.
OpenBullet and Its Underlying Libraries
OpenBullet relies on several well-known bot automation libraries and frameworks:
- Requests: A Python module for sending HTTP requests with forged attributes. It's highly scalable and can bypass traditional CAPTCHAs using external CAPTCHA farm services. However, it struggles against highly protected sites and mobile applications.
- Selenium: This is a browser automation framework initially developed for testing web applications. It can interact with a web service as a human user would, helping attackers mask their bots with human-like behaviours.
- Puppeteer: This Node.js library controls Chromium-based browsers. It's faster and lighter than Selenium, making it capable of running more parallel requests.
OpenBullet does not inherently simulate human behaviour; the bot developer has to implement that. Based on an analysis of online configurations, most do not include fake human behaviour features. OpenBullet does, however, support ad hoc JavaScript execution to enable them.
Detecting and Blocking OpenBullet
To detect and block OpenBullet, defenders need to understand where a request is coming from, especially when proxies are used to distribute attacks. OpenBullet can be effective in the wrong hands, but it is not invisible. Several signals can help identify and block its activity.
Identifying Unusual Patterns
Most automated tools, including OpenBullet, generate request patterns that differ from typical human behaviour. The frequency, timing, and sequence of requests can help identify potential OpenBullet attacks. For instance, a high volume of requests from a single IP address, or repeated requests with different login credentials, could indicate automation.
Analysing User Agents
User agents can also provide useful clues. OpenBullet can mimic different user agents to look like a range of browsers, but it may not simulate the broader spread of user agents an actual user base would generate. If an unusual number of requests come from a small set of user agents, it may indicate an automated attack.
Spotting IP Address Anomalies
OpenBullet, like many automated tools, uses proxies to mask its true location and appear to be many different users. Proxies have their own characteristics. Data centre proxies, for instance, do not behave like residential or mobile IP addresses, and they can be flagged as suspicious. Similarly, if many different user identities come from a single IP address, or if the geolocation of an IP address does not match the stated location of the user, it may signal proxy use.
OpenBullet in the Greater Cybersecurity Context
OpenBullet reflects a broader pattern in cybersecurity: tools built for testing can be repurposed for abuse. Its simple UI and automation capabilities show why online security cannot depend on basic controls alone. Although it was created as a web testing tool, its misuse reinforces the need to keep defences current as attack methods change.
The Need for Strong Password Practices
OpenBullet's popularity for credential stuffing attacks underscores the importance of strong password practices. Encouraging users to create unique passwords for each account and change them regularly can help reduce the risks posed by credential stuffing.
Implementing Advanced Bot Protection Measures
To secure online services against tools like OpenBullet, advanced bot protection matters. These measures can provide a multi-layered security strategy that runs in the background, collects detection signals, and challenges users only when necessary. They can help stop bot attacks from the first request, including those carried out through OpenBullet's automation framework.
As web platforms evolve, the tools used to exploit them evolve as well. Understanding threats like OpenBullet is the first step towards stronger online protection.
Peakhour's advanced Bot Management and rate limiting capabilities can help protect your site from automated tools like OpenBullet. Contact our team for a free security analysis.
Advanced Rate Limiting
One practical defensive measure against stuffing attacks, including those made using OpenBullet, is advanced rate limiting. Unlike basic rate limiting, which restricts the number of requests from a particular source within a specified time frame, advanced rate limiting provides a more nuanced and dynamic approach.
A critical feature of advanced rate limiting is its ability to group, or bucket, requests based on factors beyond the source IP address. These factors could include:
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Autonomous System Number (ASN): An ASN is a unique number assigned to each network on the Internet. By grouping requests by ASN, it's possible to detect an unusual number of requests from a specific network, even if those requests are spread across many different IP addresses.
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Country: Grouping requests by country allows the detection of a sudden surge of traffic from a specific geographic location, which might indicate a coordinated attack.
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Device Fingerprint: A device fingerprint can be constructed from a range of attributes, including the device's operating system, browser version, and more. This allows the detection of repeated requests coming from the same device, even if other factors like the IP address or user agent are being manipulated.
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Headers: By examining the headers in HTTP requests, it's possible to detect patterns or anomalies that might signify an automated attack. For instance, a high volume of requests with identical headers could indicate the use of an automation tool.
By grouping requests on these and other factors, advanced rate limiting can provide a nuanced and dynamic defence against stuffing attacks. It allows detection of complex attack patterns that might otherwise go unnoticed, adding a useful layer of security for online systems.
Fingerprinting and Behavioral Analysis
Alongside advanced rate limiting, technologies like fingerprinting and behavioural analysis add another layer of protection. By creating a unique identifier for each user based on a combination of factors, such as IP address, device information, and browser settings, and tracking that user's behaviour over time, these technologies can help distinguish genuine users from automated scripts.
These mechanisms can detect anomalies, such as repetitive non-human patterns, sudden changes in behaviour, or usage patterns that do not match the typical profile of a human user. This level of analysis can identify and block even sophisticated bots that are designed to mimic human behaviour, providing an effective defence against OpenBullet and similar tools.
OpenBullet's accessibility and versatility have made it a popular choice for web attacks. Its simple UI, support for CAPTCHA farm services, and ability to run human-like automation allow even users with limited programming skills to execute complex attacks. Despite these challenges, detecting and blocking OpenBullet is achievable by understanding the origin of requests and the nature of its operation. A combination of advanced rate limiting, fingerprinting, and behavioural analysis can provide an effective defence.
