How to stop Al-fueled injection attacks
Bess Ferrell
Senior Product Manager
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Injection attacks, particularly deepfake injection and presentation attacks, are increasingly used to bypass biometric verification systems. With the rapid advancement of generative AI and fraud automation, attackers can now inject synthetic or manipulated ID document directly into identity systems, undermining trust in digital interactions.
For organisations that rely on remote identity verification, this evolving threat demands immediate attention.
What are injection attacks and why AI is empowering them?
An injection attack involves feeding falsified biometric data or identity documents directly into a verification system. These attacks often sidestep traditional liveness detection by manipulating the data stream itself. For instance, using a virtual camera to display a screen-recorded image to impersonate an individual.
“Injection attacks have become a high-risk reality. As generative AI evolves, so must our defense against this threat.”
The use of generative AI (GenAI) has significantly accelerated this threat. Attackers can now generate synthetic identities, impersonate someone's identity, create deepfake videos and produce forged documents that appear convincingly real. Furthermore, AI enables fraudsters to identify and exploit specific vulnerabilities in identity systems, allowing for targeted and high-success-rate attacks. As these tools become more accessible, the frequency and sophistication of injection attacks continue to rise.
The growing risks of biometrics-based fraud
Biometric verification relies on verifying that a real person is genuinely present during a transaction or onboarding process. Injection attacks circumvent this by feeding pre-constructed content directly into the system, effectively bypassing real-time interaction. This can result in account takeover and application fraud, synthetic identity creation and large-scale fraud.
Instances have already emerged across sectors where attackers used replayed videos or tampered documents to successfully impersonate individuals. The resulting damage can include reputational harm, financial loss and diminished trust in digital channels. As fraudsters continue to exploit tools like GenAI, the risks associated with advanced threats like injection attacks grow proportionally.
“The rise of deepfake injection and presentation attacks marks a turning point in digital identity security. Organizations must act now or risk falling behind.”
Why controlling the capture process is critical
Injection attack detection is only as effective as the environment in which identity data is captured. To detect sophisticated injection techniques, organisations must implement a secure, real-time capture process.
This is where dedicated mobile, web or native Software Development Kit (SDKs) – pre-built components and resources tailored for a specific platform - become essential. These SDKs embed directly into the identity verification flow, enabling organisations to:
- Control the capture: Ensuring that images and videos are taken live using the device’s native camera, not uploaded from unknown sources.
- Detect spoofing attempts: Identifying when a virtual camera or other manipulation method is used to bypass the capture process.
- Preserve the chain of custody: Maintaining confidence in the authenticity of the captured media.
Without SDK-based capture, organisations lose visibility into how and where the content was created, leaving them vulnerable to injection attacks that can bypass even advanced biometric and liveness checks.
“Injection detection begins with capture. If you don’t control how content enters your system, you can’t trust what it represents.”
Embedding secure capture into the verification process, organisations establish a first line of defense. Firms are empowered to stop fraudulent content before it can exploit downstream systems.
How layered ID verification protects against injection attacks
Mitigating injection attacks requires a comprehensive, layered identity verification approach. Layering document verification, biometric authentication, passive liveness detection, explainable AI and human oversight on top of having a dedicated mobile, web or native SDK ensures you can effectively stop fraud in its tracks.
Our document verification tool authenticates IDs by assessing over 8,500 document types across 195+ countries. Using advanced optical character recognition (OCR), forensic analysis and tamper detection techniques, these tools can identify manipulated or fraudulent documents with high accuracy.
Biometric verification complements this by comparing facial features from the ID with a live selfie. Relying on our complete document and biometric solutions leverages up to 68 distinct landmarks to confirm identity with precision and guard against impersonation.
“Passive liveness detection and forensic document analysis are essential tools in the fight against AI-driven identity fraud.”
Passive liveness detection further enhances security by confirming that a real, live person is present, without requiring extensive movement or user action beyond taking a selfie. This approach is particularly effective when including SDKs that can specifically detect deepfakes, virtual camera inputs and replayed videos. Solutions using ISO/IEC-certified liveness testing have demonstrated success in detecting up to 100% of known injection attacks using certain face swap tools.
Layering solutions into a single platform ensures you have a simplified way to prevent fraud while showing good identities the fast lane.
Document tampering detection also plays a vital role. Forensic image analysis can identify signs of digital and physical manipulation, such as inconsistencies in fonts, overlays or image compression. Our solutions help businesses achieve accuracy rates on fraud vectors such as photo tampering above 99% for blatant attacks with a false reject rate of 5%, enabling high trust without disrupting the user experience.
Ready to connect safely with every genuine identity? Get a demo today.
Frequently Asked Questions
How is generative AI making injection attacks more dangerous?
Generative AI enables attackers to create hyper-realistic deepfakes, synthetic identities, and forged documents with alarming ease. It also allows fraudsters to identify and exploit specific weaknesses in identity systems, making attacks more targeted, scalable, and difficult to detect.
What makes injection attacks so concerning?
Injection attacks involve feeding falsified biometric data or identity documents directly into a verification system. These attacks bypass traditional liveness detection to impersonate real users, which can have significant consequences.
How effective are current solutions at detecting injection attacks?
Advanced identity verification solutions demonstrated a high degree of success in detecting known injection attacks. By layering in ID verification solutions with a mobile SDK, businesses can deter trending threats. Document authentication and biometric verification with human supervised AI are all powerful solutions that play a key role in a layered approach to fraud prevention.
