Bot mitigation is an adversarial game: attackers adapt, defenders respond, and the cycle continues. At Cloudflare, we stay ahead by combining visibility across our global network with signals from the client-side environment. At the network level, we analyze over 1 trillion requests per day to understand reputation, patterns, and anomalies across more than 20% of the web.
On the client side, we’ve pushed detection deeper with Cloudflare Turnstile , which has evolved from a CAPTCHA replacement to a risk-based managed challenge that adapts the amount of friction needed to verify the user is authentic. Today, Turnstile runs nearly 3 billion times per day on some of the most sensitive endpoints on the Internet, helping verify users at key moments like login, signup, and checkout.
This improves protection on the most important areas of customer applications, but still leaves limited visibility into the rest of the application — how humans and bots actually interact across the full user journey. This is the visibility gap we’re closing today with our launch of Precursor . Introducing Precursor Precursor is a client-side, session-based verification system, built with privacy in mind, that uses dynamically injected JavaScript to continuously collect behavioral signals as visitors interact with your application.
These signals are processed and incorporated into Cloudflare’s bot protection in real time, allowing us to continuously distinguish human traffic from automated or agentic traffic. This extends the client-side detections offered by a Challenge to your entire web application. Precursor is an optional complement to Turnstile — both are features of our Enterprise Bot Management.
This user-journey-based detection is powerful because modern automation is increasingly capable of appearing legitimate in short bursts. Bots can execute JavaScript, use real browser environments, and pass individual CAPTCHAs without raising suspicion. What remains difficult to replicate is consistent human behavior over time.
Precursor is built to capture that layer of interaction, turning behavior itself into a reliable signal for detecting fraud and abuse. By evaluating behavior across an entire session, Precursor adds significantly more signal to each decision. This improves detection precision, making it easier to distinguish real users from automation without relying on aggressive Challenges.
For legitimate users, Precursor means fewer unnecessary interruptions. For bot developers, it raises the cost of operating automation by requiring them to simulate a full session. This is significantly harder to build, more expensive to maintain, and far less reliable to operate at scale.
To err is human When a bot developer tries to make a mouse movement look human, they usually add Gaussian noise or uniform random delays. But human movement isn't just "noisy," it is also constrained by physics: Wrist pivot: A human mouse movement is often an arc, limited by the range of the wrist and the rotation of the forearm. Cognitive load: There is a measurable delay between a human seeing a checkbox and clicking it.
Hand tremor: Even the steadiest human hand oscillates at a physiological tremor frequency. Bots, by contrast, often behave in ways that give them away. They move in linear interpolations or mathematically ideal Bézier curves.
They click with a precision that humans could never replicate. And even when they do manage to simulate human error, there is a rhythm to human movements that can only be seen by examining an entire session. Mouse movement is just one example of the signals Precursor evaluates, but it illustrates the difference clearly.
Below is an example of a mouse automation library interacting with a site. You can see how the mouse moves in perfectly straight lines, always returns to an origin, and reacts with the same velocity. Now, contrast that with a human navigating the same site: you see irregular paths, small corrections and overshoots, and variations in speed, timing, and direction.
Individually, these interactions might look plausible. But over the course of a session, these patterns diverge in ways that are difficult to fake. Precursor is designed to capture and evaluate these behavioral signatures as they develop over a visitor’s interaction with an application.
How Precursor works To evaluate behavior over time, Precursor continuously collects interaction data on the client and builds a session-level view of activity for that site. 1. Injection and collection layer When Precursor is enabled on your application, Cloudflare automatically injects a lightweight script into HTML responses from your site as they pass through our network, with no additional configuration, network connections, or third-party embedding required.
The injected Precursor bundle is compact, obfuscated, and assembled dynamically for each response. The bundle is designed to not interfere with any additional page logic of the hosted web application. The script attaches lightweight event listeners to capture interaction signals such as pointer movement, keyboard activity, focus changes, and visibility.
These events are serialized into a compact format and buffered in memory. At regular intervals, the buffered data is sent back to the evaluation layer for analysis. 2.
Evaluation layer On the edge server, incoming Precursor payloads are deserialized into behavioral inputs. A dispatcher runs a roster of evaluators on the input data. Each evaluator reads the Precursor streams it cares about and can raise signals into the shared detection registry.
Evaluators are designed to cross-reference data. For example, they confirm that pointer activity correlates with page visibility duration, or that keyboard events only fire when a text field is focused. This stream of information is then consolidated into individual signals that are used for weighting detections.
Originally published at blog.cloudflare.com
