Description
Bot-like behavior refers to interactions with a web application that lack the natural variability and unpredictability typically associated with human input. Human users exhibit irregular timing, pauses for reading or decision-making, imperfect mouse movements, and typing errors. Automated processes, by contrast, tend to follow deterministic and highly consistent execution patterns.
This detector identifies interaction characteristics that are statistically unlikely to be generated by a human user.
Key indicators include:
Robotic navigation
Movement between pages or interface elements at superhuman speeds or in perfectly linear, stepwise sequences.
Teleporting cursor behavior
Mouse pointers jumping instantly between screen coordinates rather than traversing the screen along natural arcs.
Fixed interaction cadence
Form submissions, clicks, or actions occurring at exact, repeated time intervals (for example, every 500 milliseconds).
Unnaturally perfect typing
Long strings of text, such as usernames or passwords, entered instantly or at a constant speed with no corrections, pauses, or backspacing.
Relevance to Social Engineering Attacks
In social engineering operations, automation is commonly used as a reconnaissance mechanism and a force multiplier. Rather than relying solely on manual interaction, attackers leverage bots to rapidly probe systems, identify weaknesses, and scale their activity.
Common attacker objectives include:
Automated credential or MFA guessing
Systematically attempting large volumes of credential combinations or one-time passcodes to identify valid access paths.
Speed and scale amplification
Running hundreds or thousands of parallel attempts, such as automated form submissions or chat interactions, to identify which targets or workflows are most susceptible.
Bypassing first-line defenses
Many traditional WAFs and IP-based controls fail to detect sophisticated automation that mimics real browser traffic. Behavioral analysis, such as cursor movement and interaction timing, exposes automation that network-level filtering alone cannot reliably detect.
Examples of Detected Tooling and Techniques
This detector is effective against a wide range of automation technologies, including:
Browser automation frameworks
Selenium, Puppeteer, Playwright.
Stealth and evasion plugins
Tools such as puppeteer-extra-stealth, designed to conceal browser automation fingerprints.
Scripted interaction frameworks
Python-based automation using libraries such as Requests, NoDriver, or similar headless execution tools.
Notable Incidents and Threat Actors
Intentionally omitted.