8 Common Types of Account Abuse

Leading platforms like Canva, Atlassian, Figma, Notion, and Dropbox have completely transformed the modern workspace. They have brought productivity to new heights and made online collaboration effortless. However, the widespread shift towards digital work has unintentionally given rise to new security vulnerabilities.

Industries such as e-commerce and financial services have long been at the forefront of combating digital fraud. Their transaction-based models have allowed them to refine security measures and protect against a narrower range of attacks. In contrast, collaboration platforms, with their diverse and ever-evolving use cases, have become hotbeds for innovative forms of abuse.

As these platforms become cornerstones of our daily tasks, their security isn't just a perk—it's imperative. And it’s not just about fending off yesterday's threats; it's about anticipating tomorrow's. With technology, especially AI, advancing at lightning speed, our security approach needs to be both agile and comprehensive. It's about identifying new risks while ensuring that the user experience remains smooth and uncompromised. Essentially, we're aiming for a balance—equipping ourselves for new challenges while preserving the ease and efficiency that drew us to these platforms in the first place.

In the midst of all this, let's unpack eight common types of account abuse that are shaking up these modern online platforms.

1. Fake Accounts

Modern fake accounts often mimic genuine user behavior and pass initial sign-up checks with ease, making them increasingly challenging to identify and block. Unlike more streamlined transaction-based platforms, the multi-faceted interaction landscape makes it harder to anticipate and counter such abuse.

To tackle this evolved threat, we need to rethink our approach and go beyond just hardening the sign-up process. Drawing inspiration from the zero-trust security model, we propose the concept of 'trust progression'. Rather than relying on a one-time verification at sign-up, this method employs continuous, context-aware verification throughout the user's journey.

Trust progression begins by applying stricter rules to new accounts, reflecting their unproven status. As users interact with the platform over time, their behavior is continuously scrutinized, gradually loosening restrictions as their actions confirm their legitimacy. This continuous validation ensures that even if a fake account slips through initial checks, their activities will raise flags during ongoing monitoring.

This adaptive approach to security effectively shifts the paradigm from a static, one-time verification to a dynamic, behavior-driven model. It harnesses the strength of the zero-trust model in continuously verifying actions and offers a robust solution to the challenge of sophisticated fake accounts in today's online collaboration platforms.

2. Account Takeover

Account Takeover (ATO) is another form of abuse that online platforms grapple with. This occurs when a malicious entity gains unauthorized access to a legitimate user's account.

ATO is not just an intrusion into privacy; it disrupts workflow, hampers productivity, and can lead to substantial data loss. Given the intricate interactions and high-value transactions taking place on these platforms, detecting and preventing ATOs can be more challenging than in more streamlined environments.

Adopting a more nuanced approach inspired by the zero-trust model could significantly bolster defenses against ATO. The essence of this model is the principle of 'never trust, always verify'. This means that instead of placing trust in an account after initial sign-in, the platform continually checks the legitimacy of the user's actions.

This persistent vigilance can be particularly effective in mitigating ATOs. By applying trust progression, the platform continuously evaluates each action a user takes, considering factors such as the user's usual behavior patterns, IP address changes, unusual transaction activities, and more. This ongoing monitoring can help identify suspicious behavior indicative of an account takeover, even if the intruder manages to bypass initial authentication measures.

3. Multi Accounting

This form of abuse occurs when users create multiple accounts to exploit features, bypass restrictions, or engage in prohibited activities while remaining anonymous.

To combat this issue, a rules engine can leverage user interactions and data points to develop a comprehensive understanding of user behavior. The engine enables monitoring and assessment of metrics, such as the number of users per device, account activity frequency, or utilization of multiple credit cards per IP address within a specific timeframe. These insights help identify any anomalies.

For instance, if a single device is linked to multiple accounts, or if a particular IP address is associated with an unusually high number of credit cards within an hour, the rules engine can flag these activities as potential instances of multi-accounting. Unlike one-time checks, this system continuously monitors user activities, providing a real-time feed to swiftly identify and mitigate risks.

4. Content Abuse

In the realm of collaboration platforms, the nature of interactions fundamentally centers around content creation and sharing. This open-ended interaction model is what enables the free flow of ideas, insights, and collaborative work products. However, it's precisely this openness that also leaves room for an abuse vector not typically encountered in traditional services such as e-commerce and financial platforms - content abuse.

Content abuse occurs when users generate and disseminate inappropriate, harmful, or deceptive content. Forms of such abuse may range from posting spam or distributing disinformation to sharing harmful or explicit material. Beyond the immediate disruption of the user experience, content abuse can inflict long-term damage on a platform's reputation and potentially cause harm to its community of users.

Dealing with this relies on a mix of never trusting anything blindly and making good use of data. In geek terms, it's combining a zero-trust model with data aggregation. In this setup, no content gets a free pass. Everything shared is examined from a behavioral standpoint before it's posted.

Take, for example, a feature that observes how users behave on Dropbox. If someone is sharing too many links too fast, or a brand-new account is suddenly sending tons of files, that's a red flag.

5. SMS Pumping

The growing use of SMS for multi-factor authentication, notifications, and other forms of communication within online platforms has given birth to a new form of abuse – SMS Pumping. SMS Pumping typically involves the sending of large volumes of messages to premium numbers in an attempt to generate illicit revenue. This practice can inflate operational costs, potentially disrupt services for genuine users, and damage the platform's reputation.

Addressing SMS Pumping requires a combination of the zero-trust approach and advanced data aggregation capabilities. The zero-trust model ensures that actions related to SMS services, like requesting a high volume of SMS within a short time frame, are continuously monitored and verified.

Further, a real-time rules engine that aggregates data from multiple sources and perspectives can be key in identifying suspicious patterns. For instance, it might track the number of SMS requests per IP address, the frequency of SMS requests per account, or the ratio of SMS requests to typical user activity levels. By detecting and flagging unusual behavior patterns like these, the platform can quickly intervene to prevent potential SMS Pumping.

6. Account Sharing

Account sharing is a growing issue for online platforms, causing significant revenue loss, especially for subscription-based businesses. When users share their account credentials, it breaches terms of service and undermines potential earnings.

Netflix, a leading global streaming platform, recently experienced revenue losses due to widespread account sharing, despite gaining new subscribers. To address this, businesses must focus on detecting and managing shared accounts, which could uncover additional revenue opportunities.

However, accurately identifying shared accounts without inconveniencing legitimate users presents challenges. Traditional methods like tracking IP addresses or device fingerprints can be bypassed by tech-savvy users using VPNs or shared Wi-Fi networks.

A more sophisticated approach is needed, involving continuous monitoring of user interactions. This includes tracking page views, button clicks, and streaming activity to more precisely detect overlapping usage that may indicate account sharing.

For example, streaming platforms like Netflix track events such as "Stream started," "Stream playing," and "Stream stopped" to understand user behavior. Aggregating usage data using sliding window functions provides a better estimate of overlapping usage and potential account sharing.

7. Transaction Abuse

Transactions are critical touch points for user interaction in a platform. However, it's important to recognize that transaction abuse in collaboration platforms differs from traditional payment fraud in e-commerce and financial services. Unlike payment fraud, which aims for unauthorized transactions and monetary gain, transaction abuse on collaboration platforms serves different purposes. For example, attackers exploit these platforms for card testing or as a crucial point for detecting subscription abuse.

Card testing involves using the platform's transaction system to test the validity of stolen or randomly generated card details. If the transaction is successful, fraudsters verify the validity of the card details, which they can then use for fraudulent activities or sell on the dark web. This unintentional exploitation of platforms exposes them to potential reputational damage and financial losses due to chargeback fees.

Additionally, transactions play a vital role in detecting subscription abuse, a growing problem in SaaS platforms, where users share or sell their login credentials. Analyzing transaction patterns can help identify anomalous behavior, such as a surge in new users subscribing and quickly downgrading or canceling their subscription, or inconsistent payment behaviors across different accounts.

8. API Abuse

APIs enable structured communication between applications, but this ease of interaction also opens the door to new forms of abuse such as data scraping, account takeovers, and potential system destabilization. Traditional safeguards like rate limiting can help, but when implemented on the WAF (web application firewall) level, they often lack the ability to maintain vital user context, making nuanced detection a challenge.

This is where an advanced rules engine can make a substantial difference. By moving beyond simply monitoring API traffic, and instead capturing the entire user journey, an advanced rules engine can identify early signals of potential abuse.

Such an engine allows for finely-tuned rule definitions based on a multitude of factors including IP reputation, geolocation, and request headers. But its power lies in not merely focusing on API traffic in isolation. It analyzes the user behavior throughout the session, thus building a complete, contextual picture.

Consider a scenario where a user logs in from a new location and accesses a page with API keys. Even if the API access itself only provides limited data like IP and headers, an advanced rules engine can recall context from earlier interactions. This prior context provides valuable insights for detecting potential abuse at the API level.

A Holistic Approach To Any Account Abuse

The advent of collaboration platforms has revolutionized the dynamics of the modern workspace, placing a premium on efficiency and fostering creativity. But amidst this digital progress, a new breed of sophisticated threats has emerged, ranging from fake accounts to API abuse and beyond. It's a multifaceted challenge that defies traditional defense strategies ill-equipped to keep pace.

Enter the need for a proactive, data-centric, and context-aware approach. Advanced rules engines step into the spotlight, diligently monitoring user interactions in real-time. Their remarkable ability to comprehend context makes them indispensable for safeguarding collaboration platforms. However, effective defense requires more than just guarding the entry point.

Here's where Castle comes in. Unlike its counterparts, Castle doesn't merely focus on securing the gateway. It goes a step further, comprehensively monitoring every facet of a user's journey within your platform. From credential updates to seamless navigation, every action is meticulously tracked and scrutinized.

Integration is a breeze, with options like Segment or a JavaScript snippet at your disposal. Plus, you can put Castle to the test without any upfront costs. Give it a try and let us know what you think!