# Introduction Decentralized finance has dramatically lowered the barrier to token creation, liquidity provisioning, and permissionless trading. \ However, this openness has also introduced increasingly sophisticated forms of financial risk, including:
* Rugpulls * Liquidity extraction * Whale-driven exits * Protocol exploits * Stablecoin depegging * Governance attacks * Coordinated manipulation * Hidden structural vulnerabilities Most catastrophic DeFi failures begin as isolated abnormal events before propagating into broader market instability. \ Traditional monitoring systems often react too late because they focus primarily on secondary metrics rather than root-cause signals.\ Most existing tools fail because they:
* Focus only on static contract analysis * Display raw data without interpretation * Ignore changing market conditions * Lack continuous monitoring capabilities * Overwhelm non-technical users Traditional metrics alone often fail to distinguish between normal market behavior and systemic risk events.
> Metrics are often consequences, not causes. Critical risk events may propagate across interconnected DeFi infrastructure long before secondary market metrics fully reflect the underlying danger. \ For example:
* Utilization spikes may occur after panic borrowing already begins * TVL collapse often follows exploit-related events * Liquidity deterioration may lag behind whale exits * Volatility spikes frequently occur after structural danger has already emerged A token may appear structurally safe while still becoming extremely dangerous to enter due to abnormal protocol behavior or rapidly changing market conditions. \ Many modern DeFi risks no longer originate from token contracts alone, but from cascading failures across interconnected protocol infrastructure, bridge systems, liquidity dependencies, and ecosystem integrations. \ Rugtector addresses this gap by combining structural analysis, event-driven monitoring, and contextual risk interpretation into a unified DeFi risk visibility system. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://rugtector.gitbook.io/rugtector/introduction.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.