/What are injection attacks, and how to prevent them

In the realm of identity verification and biometric security, safeguarding against various types of fraud is paramount. Among these, injection attacks and deepfake threats stand out due to their potential to compromise the integrity of identity verification systems. This article delves into the nature of injection attacks, how they are detected, and the measures Veridas employs to ensure robust security against these threats.

Types of Identity Fraud Attacks

Identity fraud can generally be categorized into two primary types:

1. Presentation Attacks: These involve an attacker presenting falsified evidence directly to the capture device’s camera. Examples include using photocopies, screenshots, or other forms of impersonation to deceive the system.
2. Injection Attacks: These are more sophisticated, where the attacker introduces false evidence directly into the system without using the camera. This often involves manipulating the data capture or communication channels.

Types of injection attacks

Injection attacks related to identity verification can exploit the way applications handle multimedia inputs like images, videos, and audio. Here are some specific types of injection attacks in this context:

1. Document Injection Attacks involve using stolen, falsified, or manipulated identity documents. Attackers may introduce forged passports, driver’s licenses, or other identification documents into the verification system by uploading altered documents or using software to create counterfeit versions of legitimate IDs. 
2. Selfie Injection Attacks occur when attackers submit altered or fake selfies to the identity verification system. This can involve using pre-recorded images, photoshopped pictures or synthetically generated faces (deepfakes) that match the stolen identity documents. Attackers might also employ image manipulation techniques to adjust lighting, angles, or other attributes to bypass liveness detection. 
3. Video Injection Attacks happen when attackers introduce pre-recorded or manipulated videos instead of live video feeds. These can include deepfakes or videos edited to mimic real-time interaction. By injecting these videos, attackers aim to deceive the verification system’s facial recognition and liveness detection features.
4. Voice Injection Attacks involve submitting falsified or manipulated voice recordings. Attackers might use stolen voice samples, synthetic voices generated through deep learning algorithms, or altered recordings to impersonate the legitimate user. 
5. Data Channel Manipulation occurs when attackers manipulate the data capture or communication channels to inject false information directly into the system. This includes intercepting and altering data packets during transmission or using software tools to insert fraudulent data. Such attacks can be particularly sophisticated, as they often exploit vulnerabilities in the identity verification system’s data handling and transmission protocols.

To defend against these attacks, applications should implement robust input validation, sanitize all multimedia inputs, employ secure libraries for processing multimedia files, and use comprehensive security measures. Veridas Solutions has successfully identified various types of injection attack attempts, including:

How can injection attacks be prevented

Veridas employs a comprehensive suite of security measures designed to detect and prevent injection attacks. Here are the key methods used:

• API Security: Veridas utilizes robust security mechanisms to prevent unauthorized API invocations, which could be used to inject malicious content. These include api-key usage and IP filtering, among other advanced security protocols.
• Virtual Camera Detection: The system can detect the use of fake cameras (e.g., virtual cameras). This capability ensures that images or videos injected into the system through a virtual camera, as if they were captured by a real camera, are identified and blocked.
• Man-in-the-middle Attack Detection: Veridas systems are adept at detecting instances where images have been intercepted and altered before being processed by biometric engines. This includes detecting changes due to digital manipulation, compression, formatting alterations, and cropping.
• Business Intelligence Techniques: Dozens of parameters are monitored to ensure the integrity of the identity verification process. These checks confirm that the process is executed end-to-end on the same device, preventing any evidence from being directly injected into the system.
• Deepfake Detection: Veridas employs AI algorithms trained to analyze and detect images, videos, or voices that have been artificially generated. These algorithms differentiate genuine content from deepfakes, ensuring that only authentic images, voices, and videos are processed.

Injection Attack Example

In early 2023, a globally recognized financial services company in the United States fell victim to a sophisticated injection attack targeting their identity verification process. The attackers exploited a vulnerability in the company’s data handling protocols, allowing them to inject falsified identity documents and manipulated selfies directly into the verification system.

The attack involved intercepting data transmissions and using high-quality deepfakes and edited photos to create realistic-looking fraudulent identities. Despite the company’s use of liveness detection measures, the attackers bypassed these defenses with pre-recorded videos that mimicked real-time interactions. Over 5,000 fraudulent accounts were created, leading to direct financial losses exceeding $10 million.

This breach resulted in significant financial losses and reputational damage for the company. Customer trust plummeted, and the company saw a 20% decline in new account sign-ups. The incident highlighted the need for enhanced encryption, advanced liveness detection technologies, and continuous monitoring to prevent similar attacks in the future. The company has since implemented these measures to restore trust and ensure the security of its identity verification process.

Mitigating Deepfake Threats

Deepfakes present a significant challenge due to their ability to create highly realistic but fake images or videos. Veridas employs specialized algorithms to detect deepfakes in both identity documents and selfies, complementing the anti-injection measures. These algorithms work in conjunction with other security techniques to ensure the system can accurately identify and reject deepfake attempts.

Regulatory Compliance and Certifications

Veridas solutions are not only robust in their technological capabilities but also compliant with stringent regulatory standards. In Spain, for example, the National Cryptological Center (CCN) established guidelines for evaluating video identification tools, requiring the detection of various identity theft attempts, including deepfakes. Veridas has achieved a 0% error rate in these evaluations, demonstrating the effectiveness of its security measures.

Conclusion

Injection attacks and deepfake threats pose significant risks to biometric identity verification systems. However, with advanced detection techniques and robust security measures, Veridas ensures that its solutions remain secure and reliable. By employing comprehensive strategies such as API security, virtual camera detection, and specialized deepfake detection algorithms, Veridas stands at the forefront of protecting against sophisticated identity fraud attempts.