What Is A Digital Injection Attack & How Do You Stop It?

 Fraud schemes evolve quickly, and many businesses still struggle to understand first-party fraud in its many forms. One of these forms is digital injection, a method bad actors use to bypass application security and exploit your software for their gain. This article will cover how digital injection works, how it relates to fraud, and how to prevent it to keep your business and customers safe.

Anonybit’s fraud prevention solution provides organizations the tools to stop digital injection attacks and protect their systems and data.

What Is A Digital Injection Attack?

A digital injection attack is a sophisticated form of cyberattack where criminals inject manipulated or falsified data into a system to bypass security measures. Unlike traditional attacks that might physically alter or fake information, digital injection attacks target the integrity of the data being processed by a system. This attack is hazardous because it can exploit vulnerabilities in systems that rely heavily on data streams, such as biometric verification and authentication systems. 

Digital Injection Attacks Explained 

In a digital injection attack, the primary objective is to trick the system into accepting fraudulent information as genuine. Attackers might use replayed or synthetic imagery, including advanced deepfakes, to bypass biometric security measures like facial recognition or fingerprint scanners. 

By injecting these manipulated data streams, criminals can impersonate legitimate users and gain unauthorized access to accounts or sensitive systems.  

Common Methods of Digital Injection Attacks 

One common method used in digital injection attacks involves faking camera feeds. In this scenario, attackers create a simulated live video stream with altered content, which appears authentic to the system. 

This deception can fool the system into processing and accepting the fake data as if it were legitimate, allowing attackers to bypass security measures without physical interaction. 

Combating Fraudulent Data Injection Attacks

Another technique is injecting fraudulent imagery, or in the case of biometrics, the biometric template, directly into the data stream between a device and the organization’s server. This method can deceive the system into accepting the tampered information as valid, compromising the security of the data exchange. 

Such attacks highlight the need for advanced detection and prevention mechanisms to safeguard against these highly scalable and replicable threats.

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Common Categories Of Digital Injection Attacks in Biometric Authentication Systems

Replay Attacks

In a replay attack, an attacker captures legitimate biometric data and reuses it to gain unauthorized access to a system. They might intercept a video recording of a user’s facial features or fingerprints and then replay it during authentication. 

If the attack involves presenting the recording through a physical device, it is often classified as a presentation attack. If the recording is injected directly into the application’s data stream, it falls under digital injection attacks. The core idea is to deceive the system into accepting previously captured, valid data as if it were being presented in real-time.

Injecting Imagery or Biometric Templates

This technique involves inserting manipulated or fraudulent images or potentially stolen biometric templates directly into the data stream between a user’s device and the server. Attackers may use advanced software to generate fake biometric images that closely mimic a natural person’s features or create entirely new, fabricated identities. They may also look to steal biometric templates that are stored in a centralized repotrick the system into processing and accepting altered information as authentic. This approach is particularly dangerous because it bypasses traditional front end biometric checks by directly corrupting the data stream, making it challenging to detect and prevent.

Deepfakes

Deepfakes leverage artificial intelligence to produce hyper-realistic images or videos depicting individuals in scenarios they have never experienced. In biometric authentication, deep fakes can deceive facial recognition or voice recognition systems. 

A deepfake video might impersonate a person during a facial recognition check, tricking the system into believing the fake video is a real-time, genuine interaction. Attacks on communication and capture channels often target the camera capturing the facial image, deceiving the system through a virtual camera that injects images or videos or even the server-side API by exploiting vulnerabilities to inject false data. 

Understanding the Impact of Man-in-the-Middle Attacks on Biometric Security

Another fraud technique used in other digital domains that can inject false biometric data is the famous man-in-the-middle attack. In this scenario, the attacker acts as a proxy between the user attempting to verify their identity and the biometric verification system, injecting false biometric data. 

The impact of these attacks on the security of a biometric system is significant. A successful attack compromises the system’s security and can lead to identity theft, financial fraud, and a loss of trust in biometric technologies.

How Does A Digital Injection Attack Happen?

A digital injection attack happens when a cybercriminal uses fake or stolen biometric data to bypass biometric security systems. Instead of launching physical attacks like masks or photos to fool a biometric system, the attacker creates a fake digital identity using advanced technology to generate a lifelike target profile. The end goal? To inject this data into the system to gain unauthorized access. 

How It Works

Here’s how a digital injection attack happens. Imagine a secure bank account with a biometric security system that uses facial recognition to verify users. Instead of physically tricking this system with a photo of a target, an attacker uses generative AI to create a fake digital profile of the target. 

This injected data is fed into the security system, fooling it into thinking it’s interacting with a genuine person. In another scenario, an attacker might create a fake digital identity and use it to authenticate themselves in a system. The result? They gain unauthorized access using a fabricated persona, completely bypassing the system’s defenses.

Why They’re Dangerous

What makes digital injection attacks so tricky is that the data being injected can be highly realistic, thanks to advances in AI and machine learning. This makes it harder for traditional security measures to catch these sophisticated fakes. 

As a result, it is critical to use advanced tools that are designed to handle these clever digital deceptions. 

Preventing Data Breaches and Account Takeover Fraud with Anonybit

At Anonybit, we help companies prevent data breaches and account takeover fraud with our decentralized biometrics features and first-party fraud prevention software. With a decentralized biometrics solution, companies can enable passwordless login, wire verification, step-up authentication, and help desk authentication. We aim to protect companies from data breaches, account takeover, and synthetic identity fraud.

To achieve this goal, we offer security solutions that cover the user lifecycle such as:

• 1:N deduplication, synthetic and blocklist checks upon account origination
• Passwordless login
• Step up authentication
• Account recovery
• Secure storage of biometrics and other PII data

Anonybit eliminates the tradeoffs between privacy and security. Prevent data breaches, enable strong authentication to eliminate account takeovers, and enhance the user experience across the enterprise using Anonybit. 

Book a free demo today to learn more about our integrated identity management platform.

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Injection Attacks vs. Presentation Attacks

Understanding the difference between presentation attacks and injection attacks is crucial for businesses and organizations when it comes to protecting identity verification systems.

Exploiting Presentation Attacks in Biometric Verification

Presentation Attacks occur when an attacker takes advantage of the verification process but manipulates it by presenting falsified information. These attacks typically involve physical or visual deceit. 

Someone might use a fake document, like a doctored ID or a printed selfie photo, to fool the system. In more sophisticated cases, attackers may employ face swaps, using software to blend facial features from different images to bypass facial recognition checks. Another method involves hyper-realistic masks, which, though rare, have been used to deceive facial recognition systems by creating a highly convincing false appearance.

Mitigating Injection Attacks and Digital Manipulation in Security Systems

Injection Attacks involve tricking the verification system by directly altering or injecting falsified data into the process.In addition to manipulating physical documents that can bypass document verification systems, attackers might use digitally manipulated selfie images or other digital content to fool the system. Attackers could employ deepfake technology or other advanced techniques to create convincing but fake data that bypasses the usual verification checks.

Challenges and Limitations of Presentation Attack Detection (PAD) in Modern Verification Systems

While Presentation Attack Detection (PAD) is a common defense against presentation attacks, not all PAD technologies are created equal. KYC and identity verification platforms use Presentation Attack Detection (PAD) to defend against presentation attacks. PAD approaches use proprietary techniques to perform liveness checks on documents and selfies presented to the system. 

This can detect some presentation attacks, including screens and printed-out photos. Some PAD interventions are vulnerable to injection attacks that leverage deepfakes and generative AI. Journalists could fool the KYC measures of a cryptocurrency exchange using ID documents created using generative AI.   

Rising Threats from Face Swaps and Deepfakes in Biometric Security

2023 also saw a 704% increase in face swap attacks wherein an attacker uses software tools to combine traits of one face with another to spoof facial biometrics or liveness checks. Note that face swaps can be used in both presentation and injection attacks.

Some recent reports highlighted how deepfake technology was used to deceive identity verification systems. This demonstrates that while PAD can detect many presentation attacks, it may struggle against sophisticated digital manipulations.  

Why Are Digital Injection Attacks Dangerous?

Digital injection attacks exploit vulnerabilities in identity verification systems. Unlike traditional presentation attacks that involve showing a fake ID or a mask, digital injection attacks use advanced technology to bypass security measures without raising suspicion. 

Inherent Vulnerabilities in Identity Verification Systems and Digital Injection Attacks

These attacks are dangerous because many identity systems were not designed with this type of threat in mind. They might not be designed to handle advanced techniques in digital injection attacks, making them susceptible to these sophisticated methods. 

IDR&D’s unique model counteracts this by preventing injection attacks from being effective in the first place. This forces attackers to rely on more traditional—and often easier to defend against—presentation attacks. 

Rapid Growth of Digital Injection Attacks

The threat is growing rapidly. Research by iProov shows that digital injection attacks surged by 255% in 2023. These attacks are now five times more common than presentation attacks. This increase in frequency is alarming, given that presentation attacks are still a serious threat often underestimated. 

Digital Injection Attacks Are Stealthy

What makes injection attacks particularly challenging is their stealthiness. They can be incredibly difficult to detect, especially as they become more sophisticated. 

A striking example is a recent incident where a Hong Kong company lost $25 million due to an attack that involved injecting deepfake video feeds into a live video call.

The Growing Number of Digital Injection Attacks

The scale of the problem is evident: In 2022, nearly 29% of businesses reported falling victim to deepfake video fraud. And the situation has only worsened since then, with Onfido reporting a staggering 3,000% increase in deepfake attacks in 2023. 

Despite other identity verification products’ efforts to detect and thwart these attacks, many fail to do so effectively. IDR&D stands out by addressing this issue head-on, eliminating the threat vector, and providing a robust defense against these sophisticated cyber threats.

Impact Of Digital Injection Attacks On Biometric Security

Biometric systems rely on unique data to verify identities. But if the quality of this data could be better, it becomes more susceptible to injection attacks. 

An attacker might use a low-quality deepfake image to fool a biometric system without defenses against such manipulations. A successful attack can enable unauthorized access, leading to identity theft and financial fraud. 

Data Transmission and Vulnerabilities to Digital Injection Attacks 

Biometric data is often transmitted between devices and servers. If this data isn’t encrypted properly, it can be intercepted by cybercriminals who can use it in replay attacks. They could capture and use this data to impersonate someone, gaining unauthorized access to systems. 

Data Storage and Vulnerabilities to Digital Injection Attacks 

Attackers can also target biometric data stored in databases. If these databases are compromised, attackers can access and misuse the biometric information in a digital injection attack. The way Anonybit stores biometric data makes it if not impossible for attackers to gain access to a biometric template in the first place. 

Consequences of Successful Injection Attacks on Biometric Systems 

The consequences of a successful injection attack can be quite severe. Unauthorized access to sensitive systems, such as bank accounts or government databases, is a primary risk.

This can lead to identity theft, where criminals use stolen data to conduct fraudulent transactions or open accounts in someone else’s name..

The Impact of Poorly Implemented Biometric Authentication on Financial Integrity and User Trust

Systems relying on poorly implemented biometric authentication are particularly vulnerable to digital injection attacks. By extension, successful attacks on these identity systems can erode confidence in these technologies. If people start doubting the reliability of identity and authentication systems, it could slow its adoption and push users back towards less secure verification methods, such as passwords.

How To Stop Digital Injection Attacks On Your Biometric Systems

Liveness detection helps ensure that biometric data comes from a live person rather than an attempt at impersonation using spoofed data. There are two primary types of liveness detection: 

Active

Active liveness detection requires users to perform specific actions like smiling or blinking. These actions are hard to replicate with static images or videos, making it difficult for attackers to bypass this security measure. This approach can sometimes be inconvenient for users, as they must follow precise instructions.

Passive 

Passive liveness detection does not require any user actions. It analyzes features like light reflections on the skin, skin texture, and subtle movements. This method offers a smoother user experience since it doesn’t require the user to perform specific tasks but still provides robust protection against spoofing. 

Behavioral Analysis: An Extra Layer of Security Against Digital Injection Attacks 

Behavioral analysis adds another layer of security by monitoring and analyzing how users interact with their devices and raise a fraud alert if an anomaly is found. The types of behavioral patterns to be analyzed can include typing speed, mouse movements, toggling choices and more:

Significant deviations from a user’s usual patterns can signal potential impersonation attempts. If a user who typically types quickly suddenly inputs data at a much slower rate, it may trigger an alert for further verification. 

Eliminating Biometric Templates: A Must for Stopping Digital Injection Attacks 

As biometrics become mainstream, fraudsters will seek ways to steal biometric information and use them in injection attacks. Secure data storage of biometrics therefore is a critical element of any responsible biometric deployment. Anonybit’s biometric storage works in a way that renders biometric data useless to an attacker, even if there is a breach. 

Multi-Biometric Solutions: Strength in Numbers Against Digital Injection Attacks 

Using multiple biometric traits or authentication methods can also significantly strengthen security. Combining methods like facial recognition and voice authentication or device verification makes it much harder for attackers to simultaneously spoof all required factors. 

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Book a Free Demo to Learn More About Our Account Takeover Fraud Prevention Software

At Anonybit, we help companies prevent data breaches and account takeover fraud with our decentralized biometrics technology. With our decentralized biometrics framework, companies can enable passwordless login, wire verification, step-up authentication, help desk authentication, and more. 

Comprehensive Security Solutions for Companies

We aim to protect companies from data breaches, account takeovers and synthetic identity on the rise, privacy regulations, and digital transformation. To achieve this goal, we offer security solutions that cover the user lifecycle such as:

• 1:N deduplication, synthetic and blocklist checks upon account origination
• Passwordless login
• Step up authentication
• Account recovery
• Secure storage of biometrics and other PII data

Balancing Privacy and Security with Anonybit’s Integrated Platform

Anonybit eliminates the tradeoffs between privacy and security. Prevent data breaches, reduce account takeover fraud, and enhance the user experience across the enterprise using Anonybit. Book a free demo today to learn more about our integrated identity management platform.