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 where defenders can look for evidence of its use.
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.
Those underlying frameworks can leave useful evidence, but detecting Selenium, Puppeteer, or a Python HTTP client is not the same as proving that a request came from OpenBullet. Legitimate tests use the same tools.
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. Encourage unique passwords, support password managers, and offer phishing-resistant multi-factor authentication. Do not force periodic password changes without evidence of compromise; predictable rotations often produce weaker passwords without removing the stolen credential from an attacker's list.
Layering Bot Protection Measures
Treat the tool name as context, not as the policy. Protect the login and recovery routes themselves: rate-limit failed attempts, watch how identities and network sources rotate, require stronger verification when several signals agree, and measure the effect on real customers. The useful question is not whether a request can be labelled “OpenBullet”. It is whether the request belongs to an abusive sequence and whether the response is proportionate to the evidence.
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 Behavioural Analysis
Alongside advanced rate limiting, fingerprints and behaviour add context that an IP address cannot provide. A fingerprint is a cohort signal, not a unique identifier for a person or device. It becomes more useful when it is combined with route, account, request sequence, failure rate, network, and session evidence.
That combination can expose repeated login attempts hidden behind rotating proxies or changing user agents. It still needs a measured response. A weak match may justify logging or a tighter limit; several independent signals may justify a challenge; a well-established attack sequence may justify a block. This is more durable than trying to maintain a single signature for OpenBullet, because configurations and underlying clients can change while the abuse objective remains the same.
