What is the Plugins Array?

The plugins array is a read-only, array-like object in web browsers that lists the browser plugins installed on a user’s system. It provides details like the plugin’s name, description, and filename. This data is often used for browser fingerprinting, compatibility checks, and detecting automated traffic or bots.

The Definition of the Plugins Array

The plugins array, accessed in JavaScript via the `navigator.plugins` property, serves as a directory of browser plugins. These are small software components that extend the browser’s native capabilities, such as running Java applets or viewing PDF documents directly on a webpage.

Historically, this array was critical for web developers. They needed to check if a user had a specific plugin, like Adobe Flash Player, before attempting to load content that required it. This ensured a functional experience and provided a fallback for users without the necessary software.

Over time, the role of plugins has diminished significantly. Modern web standards like HTML5, CSS3, and JavaScript provide the functionality that once required external plugins. This shift has improved browser security, stability, and performance across all devices.

As a result, major browsers like Chrome, Firefox, and Edge have phased out support for most plugins, especially those using older technologies like NPAPI. Today, the plugins array on a typical user’s browser is often empty or contains only a few default entries, like a native PDF viewer.

Despite its reduced importance for web functionality, the plugins array has found a new purpose. It has become a valuable data point in the field of browser fingerprinting. Security systems and analytics platforms analyze its contents to help create a unique identifier for each user’s browser.

The specific combination, order, and details of the plugins listed can be highly unique. This information, when combined with other data points like screen resolution, fonts, and user agent, helps distinguish between real human users and automated bots. An unusual or completely empty array can be a signal of a non-standard browser environment, which is common with fraudulent traffic.

Technical Mechanics: How the Plugins Array Works

The plugins array is part of the Browser Object Model (BOM), which provides developers with access to the browser’s features outside the content of the webpage itself. It is a property of the `navigator` object, a standard interface containing information about the browser’s state and identity.

To access it, a developer uses a simple line of JavaScript: `let userPlugins = navigator.plugins;` This command returns a `PluginArray` object. While it is not a true JavaScript `Array`, it behaves similarly. It has a `length` property indicating the number of plugins and allows access to individual plugins by index.

You can iterate over this object to inspect each installed plugin. A standard `for` loop is commonly used to cycle through the collection and extract information. Inside the loop, each item is a `Plugin` object with its own set of properties.

Each `Plugin` object contains specific details that describe the software. These details are the core data used for fingerprinting and compatibility checks. The information is read-only, meaning a script can view the data but cannot modify the user’s installed plugins.

The primary properties of a `Plugin` object are `name`, `description`, `filename`, and `length`. The `name` is the human-readable title of the plugin, such as “Chrome PDF Viewer”. The `description` provides a brief summary of what the plugin does.

The `filename` property points to the executable file for the plugin on the user’s local system. The `length` property indicates the number of MIME types the plugin can handle. MIME types define the types of media or content the plugin is registered to process.

Here is a basic code example showing how to list a user’s plugins in the browser console:

if (navigator.plugins.length > 0) {
  console.log(`Found ${navigator.plugins.length} plugins:`);
  for (let i = 0; i < navigator.plugins.length; i++) {
    let plugin = navigator.plugins[i];
    console.log(`- Name: ${plugin.name}`);
    console.log(`  Filename: ${plugin.filename}`);
    console.log(`  Description: ${plugin.description}`);
  }
} else {
  console.log("No plugins found.");
}

In security applications, this data is not just logged to the console. It is collected, often hashed into a string, and sent to a server. On the server, algorithms analyze this plugins array hash along with dozens of other browser and device attributes.

This analysis forms a comprehensive browser fingerprint. Systems then look for anomalies. For example, if thousands of website visitors all report the exact same rare plugin configuration, it is a strong indicator of a botnet using identical browser profiles to generate fake traffic.

Similarly, a browser that claims to be a standard version of Chrome on Windows but has an empty plugins array might be flagged. A real Chrome installation typically includes at least a default PDF viewer and potentially other standard components. The absence of these can suggest the use of a headless browser like Puppeteer or Selenium, which are frequently used for web scraping and ad fraud.

Three Distinct Case Studies

Scenario A: E-commerce Brand vs. Cart Scrapers

An online fashion retailer noticed a strange pattern in their analytics. Their paid search campaigns were driving thousands of “add to cart” events, but the final conversion rate was extremely low. This discrepancy was costing them a fortune in ad spend and skewing their performance metrics.

The initial investigation found no technical errors in the checkout process. The problem was more subtle. A deep dive into the session data revealed that a large portion of the traffic adding items to carts had identical, minimalist browser fingerprints. Critically, their plugins arrays were completely empty.

This was a classic sign of a botnet. Competing businesses or data scrapers were using automated scripts in headless browsers to add products to the cart. Their goal was likely to scrape pricing data, check inventory levels in real-time, or disrupt the retailer’s ad campaign performance by triggering costly conversion events without any intent to buy.

The fix involved implementing a more advanced bot detection system. This system analyzed incoming traffic for a variety of signals, with the plugins array being a key factor. Sessions originating from browsers with an empty plugins array, combined with other suspicious markers like a data center IP address, were flagged. These sessions were then challenged with a CAPTCHA before they could add an item to the cart, effectively stopping the bots while allowing legitimate users to proceed.

Scenario B: B2B Lead Generation Under Attack

A B2B software company relied on a “Request a Demo” form to fill its sales pipeline. One month, the sales team reported that over 40% of their new leads were junk. The forms were filled with fake names, disposable email addresses, and nonsensical company information, wasting dozens of hours of follow-up time.

The technical team examined the server logs for the fraudulent submissions. They discovered a clear pattern: all the spam leads came from browsers that reported the exact same configuration. They all claimed to be a specific version of Firefox on Linux but reported a plugins array that was identical and highly unusual, containing only a single, outdated plugin.

This uniformity was the giveaway. A bot was cycling through IP addresses using a proxy network but was failing to randomize its browser profile. The static plugins array became its Achilles’ heel. The bot was designed to bypass simple spam filters but not a more detailed fingerprint analysis.

The solution was to integrate a fingerprinting script on the lead generation form. Before the form could be submitted, the script collected browser data, including the plugins array. A server-side check was implemented to score each submission based on the uniqueness and legitimacy of its fingerprint. Submissions with the known bot fingerprint, or other low-quality signals, were silently discarded without ever reaching the sales team’s CRM.

Scenario C: Publisher Battling Ad Fraud

A popular news blog that monetized its content with display advertising saw its revenue decline. Their ad network partners warned them about a rising percentage of invalid traffic (IVT). If the issue was not resolved, they risked having their account suspended and facing financial clawbacks for ad revenue generated from fraudulent impressions.

The publisher’s traffic seemed legitimate on the surface. Page views were high, and session durations looked normal. However, an ad fraud audit painted a different picture. The fraudulent traffic consisted of sophisticated bots that could mimic human behavior, like scrolling and mouse movements.

The key differentiator was found in the browser environment data. The bots were running in virtualized environments that were not perfectly configured. While they spoofed their user agent strings to appear as common browsers, their plugins arrays were inconsistent with their claimed identities. For instance, a bot claiming to be Safari on a Mac would report a plugins array characteristic of a Windows machine.

To solve this, the publisher worked with an ad fraud detection provider. The provider’s technology analyzed traffic in real time, looking for such inconsistencies. When a visitor’s plugins array did not match the expected configuration for their reported browser and operating system, their session was flagged as invalid. The system then prevented ads from being served to this visitor, preserving the publisher’s reputation with advertisers and protecting their revenue stream.

The Financial Impact of the Plugins Array

Understanding the plugins array is not just a technical exercise; it has direct financial consequences. When used correctly as a signal for fraud detection, it can save businesses significant amounts of money and protect revenue. The financial impact is clear across different business models.

For the e-commerce brand in our case study, let’s assume a monthly ad spend of $100,000. If 15% of the traffic driving high-intent events like “add to cart” is from bots, that represents $15,000 in wasted ad spend each month. This money is spent on clicks that will never convert. By identifying and blocking this traffic using signals like the plugins array, the company can reinvest that $15,000 into reaching real customers, directly improving their Return on Ad Spend (ROAS).

In the B2B lead generation scenario, the cost is measured in lost productivity. Suppose five sales development representatives each spend 10% of their time (4 hours per week) chasing fake leads. If the fully-loaded cost of each representative is $80,000 per year, the company is losing over $15,000 annually in salaries paid for unproductive work. Filtering these leads out at the source frees the sales team to focus on genuine prospects, increasing sales velocity and morale.

For the content publisher, the financial risk is multifaceted. First, there is the direct loss from ad impressions served to bots, which advertisers will not pay for. If 20% of their traffic is invalid on a site earning $50,000 per month, that is $10,000 in revenue at risk. Second, high IVT rates can lead to clawbacks, where ad networks demand repayment for previously paid earnings. Finally, persistent ad fraud can damage the publisher’s reputation, leading to lower CPMs or outright blacklisting from premium ad exchanges, causing long-term financial harm.

In all these cases, leveraging the plugins array as part of a security stack provides a clear return on investment. It helps ensure that marketing budgets are spent effectively, sales teams operate efficiently, and revenue streams remain secure and stable.

Strategic Nuance: Advanced Tactics

While the plugins array is a useful signal, relying on it too heavily can be a mistake. A sophisticated understanding requires moving beyond simple checks and debunking common myths. This is where strategic advantage is gained.

Myths vs. Reality

A common myth is that an empty plugins array is a definite sign of a bot. In reality, many privacy-conscious humans use browsers configured to have minimal plugins. Modern browsers like Chrome and Firefox have very few by default. An empty array is suspicious, but it requires other signals to confirm it is a bot.

Another misconception is that the plugins array is a foolproof identifier. This is false. Sophisticated bots can easily spoof this data. They can report a perfectly normal-looking set of plugins to appear legitimate. Therefore, it should never be the only data point used for identification or blocking decisions.

The reality is that the plugins array is one piece of a much larger puzzle. Its true power comes from context and correlation. It is a signal of probability, not a binary indicator of human or bot.

Advanced Tips Competitors Miss

One advanced tactic is to analyze the order of plugins in the array. While the specification does not mandate an order, many browsers list them in a consistent way, such as the order they were installed or alphabetically. Some automation frameworks, however, list them in a static, predictable order that differs from a real browser. Detecting this non-standard ordering can unmask bots that otherwise have a plausible list of plugins.

Another powerful strategy is to look for inconsistencies between the plugins and other browser properties. A browser might claim to be Chrome on macOS via its user agent string, but its plugins array might list `np-mswmp.dll` (the Windows Media Player plugin). This logical contradiction is a very strong signal of a poorly configured bot or a fraudulent user attempting to hide their true identity.

Finally, consider historical consistency. A real user’s plugin configuration rarely changes. If a returning user’s fingerprint shows a different plugins array on every visit, it could indicate a fraudster cycling through different virtual profiles. Tracking this data over time provides a behavioral dimension to fingerprinting that is much harder to spoof than a single static property.

Abisola

Meet Abisola! As the content manager at ClickPatrol, she’s the go-to expert on all things fake traffic. From bot clicks to ad fraud, Abisola knows how to spot, stop, and educate others about the sneaky tactics that inflate numbers but don’t bring real results.

View all posts by Abisola (Abisola)