Biometrics Hacking: Key Risks, Tactics & Protection

From unlocking smartphones to verifying high-value transactions, biometric authentication is fast becoming a mainstay of digital security and is increasingly relied upon to prove we are who we say we are. But as adoption scales, so do the risks.

In 2024, the average cost of a data breach involving biometric data rose to $5.22 million, making it one of the most expensive data types to compromise. While biometrics promise convenience and enhanced protection, the technology is not immune to attack, and the consequences of a breach are often irreversible.

What Is Biometrics Hacking?

Biometrics hacking involves the unauthorized capture, replication, or manipulation of physical traits like fingerprints, facial scans, iris patterns, and voiceprints. Unlike passwords, these can’t be changed if stolen, making them prime targets for cybercriminals.

With attackers now using AI-generated deepfakes alongside spoofing tactics, the pressure is on to rethink how biometric data is captured, stored, and protected. Whether you’re a tech leader, security pro, or everyday user, understanding these risks is essential in a world where your identity is your fingerprint, voice, and face.

Are Biometrics Safe?

Biometrics are widely viewed as a more secure alternative to traditional passwords. They rely on unique physical characteristics, like fingerprints, facial structure or voice patterns, that are harder to replicate than alphanumeric credentials. This makes them especially attractive for high-security use cases like mobile banking, travel authentication and device access.

Unlike passwords, they can’t be guessed, phished or reused across platforms. This makes biometric systems (biometric authentication and biometric verification) especially appealing for applications where security and user experience must go hand-in-hand, from mobile banking to border control. As of 2023, over 80% of smartphone users now rely on biometrics for device access, and biometric transactions are expected to exceed $3 trillion globally by 2025.

But “Safe” doesn’t mean invulnerable. Biometric systems can fail under pressure — facial recognition may struggle in low light, and fingerprints can be affected by cuts or moisture. Their security depends not just on uniqueness but on how data is collected, stored, and transmitted. Strong encryption and proper implementation are critical.

Unlike passwords, compromised biometrics can’t be changed. That permanence raises the stakes, making robust system design and layered security essential. As businesses adopt biometrics, they must consider not just convenience, but the lasting risks of using the human body as a password.

Can Biometrics Be Hacked?

Yes, biometrics can be hacked — and the methods are evolving fast. While biometric traits are unique, they can still be faked, stolen, or intercepted. Hackers typically target one of four weak points in the biometric pipeline: capture, storage, transmission, or matching. Spoofing attacks use fake biometric samples, like 3D-printed masks or synthetic iris images, to trick sensors. Skimming tools lift fingerprints from surfaces or scanners. Unencrypted fingerprint records exposed in database breaches can be reused to bypass systems. Even real-time replay or man-in-the-middle attacks can inject previously captured data during authentication. With AI and machine learning becoming more accessible, attackers are using them to create synthetic biometrics — like AI-generated faces that can fool facial recognition systems. What once required high-budget espionage is now possible with everyday tools, pushing security teams to adopt multi-layered defenses.

Common attack methods include:

• Spoofing: Fake biometric samples like 3D-printed masks or synthetic iris images
• Skimming: Extracting fingerprints from scanners or surfaces
• Database breaches: Exposing and reusing unencrypted biometric records
• Replay/MitM attacks: Injecting captured biometric data during authentication
• AI-generated forgeries: Synthetic faces or traits used to bypass facial recognition systems

Can Biometrics Be Stolen?

Yes — and when biometric data is compromised, it’s gone for good. Unlike passwords, you can’t change your fingerprints or retake your iris scans. As biometric authentication becomes more widespread, the incentive for attackers to steal and misuse this sensitive data has surged.

Between 2018 and 2023, nearly 6 billion biometric records were compromised globally, primarily through poorly secured databases and third-party breaches. The potential damage from such breaches extends beyond individuals to the organizations responsible, triggering legal liabilities, reputational fallout and regulatory scrutiny.

High-profile incidents  further illustrate the scale of the threat. In 2019, a major breach exposed 27.8 million biometric records, including unencrypted fingerprints and facial recognition data, from security systems used in airports, banks and law enforcement facilities. The theft of biometric data isn’t limited to breaches however. Attackers have demonstrated the ability to “lift” fingerprints from surfaces like glass or plastic and replicate them with high-resolution 3D printing. With the rise of AI-powered deepfakes and synthetic identities, biometric data is not only stealable, but increasingly reusable in ways that legacy fraud detection systems can’t detect.

Can Biometrics Be Spoofed?

Yes, biometrics can absolutely be spoofed, and the methods used to do so are becoming more accessible and sophisticated. Spoofing refers to the act of imitating or forging biometric traits to deceive authentication systems. While biometrics are harder to replicate than passwords, they are not foolproof. In fact, the growing body of academic and cybersecurity research has repeatedly shown that biometric systems, when not properly secured, can be tricked with fake fingerprints, 3D-printed masks, synthetic voices and AI-generated deepfakes.

These findings underline a critical truth: even the most advanced biometric systems can be fooled without sufficient liveness detection, behavioral analysis or device intelligence. As attackers get smarter, spoofing is no longer limited to nation-state actors or cybersecurity researchers. It’s a real-world, commercial threat, and organizations must evolve their fraud prevention strategies accordingly.

Real-Life Methods of Biometrics Hacking

Fraudsters and cybercriminals have devised increasingly creative ways to bypass biometric authentication, exploiting everything from system design flaws to AI-driven impersonation. At its core, biometrics hacking is an attempt to subvert identity verification systems — whether through technical exploits, synthetic reproductions or social engineering, the methods can vary widely, but they all aim to trick systems into accepting a fraudulent user as legitimate.

• Database Breaches: Biometric authentication compares a live sample to a stored reference, usually in centralized databases. Poorly secured or segmented systems are prime targets. Breaches can expose biometric templates like fingerprints or facial maps, creating long-term risks.
• Fake and Synthetic Biometrics: Biometric traits can be faked using molds, replicas, or digital renderings to fool sensors. The rise of accessible fabrication tools has made it easier for attackers to replicate physical features without specialized equipment.
• AI-Generated Deepfakes: AI tools like GANs can produce realistic audio, video, or visuals that mimic human behavior. These deepfakes are used to impersonate users in voice or video verifications, making fraud harder to detect.
• Stolen Biometrics: Biometric data can be gathered from public sources like photos or discarded items. Once collected, it can be used—especially when paired with synthetic or replay techniques—to deceive biometric systems.
• Exploiting Bypass Mechanisms: Many systems offer fallback methods like document checks or static data verification. These alternatives often have weaker security, making them easier targets for fraud.

How Does Biometric Spoofing Work?

The process of biometric spoofing depends on both the biometric modality being targeted and how the fraudster acquires or fabricates the necessary data. The objective is to trick an authentication system into granting access by presenting falsified biometric input that mimics or simulates legitimate data.

In broad terms, biometric spoofing typically follows this structured approach:

1. Target Identification: The attacker determines the system they want to breach and the biometric modalities in use, whether it’s a fingerprint scanner, facial recognition camera or behavioral analysis tool.
   
2. Bypass Assessment: Before attempting spoofing, the fraudster checks whether the system allows alternative access methods, such as fallback to password or document verification. If a non-biometric route is available, it often presents an easier path and is prioritized.
   
3. Subject Selection: Depending on the system’s context, the fraudster either identifies a specific individual to impersonate or prepares a generic spoof capable of bypassing authentication without targeting a particular user.
   
4. Spoof Creation: The attacker then develops or steals the required biometric markers. This may involve:
   
   • Social engineering tactics, such as spear-phishing or pretexting, to manipulate the victim into granting access.
   • Reconstructing biometric traits using images or video, paired with tools like 3D printing or synthetic overlays.
   • Using AI-generated deepfakes to simulate the target’s voice or facial expressions.
   • Fabricating entirely synthetic identities, designed to appear legitimate to automated systems.

If the spoof succeeds, the fraudster gains unauthorized access. If it fails, repeated attempts may trigger anomaly detection mechanisms designed to flag unusual authentication behavior.

It’s also important to distinguish between targeted and untargeted spoofing. In high-security scenarios, only a few individuals may have access, requiring precise impersonation. In broader contexts, like corporate building access, any accepted biometric input might suffice, making generalized spoofing more feasible. The sophistication of the spoofing method typically scales with the value of the target system and the exclusivity of access.

Examples: 3 Real-Life Biometrics Hacks 

Biometric hacks have often played out in the real world, uncovered by ethical hackers seeking to expose vulnerabilities before malicious actors exploit them. In other cases, the attacks are discovered after the fact, either by victims or researchers analyzing fraud patterns. Here are three compelling real-life examples that highlight the evolving tactics used to exploit biometric systems.

How to Protect from Biometrics Hacking as a Business

Biometrics may offer a powerful layer of authentication, but no system is invulnerable, especially when sophisticated attackers use synthetic identities, deepfakes or stolen biometric data. For businesses relying on biometric systems, especially in high-risk sectors like fintech, eCommerce or digital banking, the key is not to abandon biometrics, but to strengthen them with layered, real-time intelligence.

SEON helps businesses go beyond static checks with real-time fraud detection based on dynamic data. It analyzes digital signals—email history, phone numbers, IP risk, device fingerprinting, and behavior—to build a contextual user profile. This makes it much harder for fraudsters to bypass onboarding or access flows undetected.

If biometric authentication succeeds but the device is new, the IP is from a high-risk region, or typing behavior looks unusual, SEON can flag or block the session in real time. Behavioral biometrics and velocity rules help catch spoofing, synthetic session replays, or AI-driven logins—common in deepfake attacks.

Custom rules let businesses adapt to evolving threats like biometric spoofing. This includes stricter checks for high-value actions, added friction for unknown devices, or scoring users based on combined risk signals. Biometric protection requires contextual checks, ongoing risk assessment, and smart friction based on threat level.

How to Protect from Biometrics Hacking as an Individual

As biometric authentication becomes more common, users must remember it’s not foolproof. Protect where and how your biometric data is used. Avoid enabling face or fingerprint login for apps that lack encryption or MFA. Stick to trusted platforms, review app privacy settings, and be cautious of unnecessary biometric requests—ask if the service really needs your data.

Public content like high-res photos or voice recordings can be used for spoofing. Don’t share them on unsecured platforms, especially if you use voice authentication. Use screen locks, app vaults, and encrypted backups to reduce exposure if your device is stolen or hacked.

If your biometric data is compromised, alert the platform, switch to non-biometric login methods, and monitor your accounts. As identity theft shifts from passwords to faces and fingerprints, strong digital hygiene is your best defense.

Frequently Asked Questions