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Apple M chip vulnerability: A Breach in Data Security

Illustration of an Apple MacBook with a highlighted M-series chip vulnerability, surrounded by symbols of data security breach and a global impact background.

Apple M-Chip Vulnerability: Critical Risk

Learn about the critical Apple M-chip flaw, a micro-architectural vulnerability that threatens data security. This article reveals the attack process exploiting data prefetching and encryption key extraction, highlighting the major security impact. Essential reading to understand and anticipate the risks linked to this alarming discovery.

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Apple M chip vulnerability: uncover the critical security breach highlighted by MIT (CSAIL). Stay updated with our latest insights.

Apple M chip vulnerability and how to Safeguard Against Threats, by Jacques Gascuel, the innovator behind advanced sensitive data security and safety systems, provides invaluable knowledge on how data encryption and decryption can prevent email compromise and other threats.

Apple M chip vulnerability: uncovering a breach in data security

Researchers at the Massachusetts Institute of Technology’s (MIT) Computer Science and Artificial Intelligence Laboratory (CSAIL) have unveiled a critical hardware flaw within Apple’s M-series chips, dubbed the “Apple M chip vulnerability,” marking a significant breach in data security. This vulnerability, referred to as ‘GoFetch,’ highlights a concerning issue in the chips’ microarchitecture, potentially compromising the integrity of sensitive information stored on millions of devices. Unlike previous security flaws, this unpatchable vulnerability allows for the unauthorized extraction of cryptographic keys through a secondary channel during the execution of cryptographic protocols, posing a serious threat to data security across a broad spectrum of devices. The discovery underscores the vulnerability’s profound implications, as it affects not only the security of Apple devices but also the broader ecosystem relying on these cryptographic protocols.

Exploiting the Apple M Chip Vulnerability Without Elevated Privileges

A notable aspect of this vulnerability is its exploitation without the need for elevated privileges. Academic researchers have devised an application capable of retrieving cryptographic keys from other applications running the affected algorithms. This exploitation leverages the Data Memory-Dependent Prefetcher (DMP) within the chips, which can mistakenly interpret data as memory addresses, thereby enabling attackers to reconstruct secret keys.

The Risk to Users’ Sensitive Data

The implications of this vulnerability are far-reaching, affecting all common cryptographic algorithms, including those designed to be quantum-resistant. Researchers have demonstrated the successful extraction of RSA, DHKE, Kyber, and Dilithium keys, with extraction times varying from 49 minutes to 15 hours, depending on the algorithm. This vulnerability endangers the integrity of encrypted data, including sensitive personal and financial information.

The Mechanics Behind the Attack

The vulnerability arises from the architectural design of Apple’s M1, M2, and M3 chips, which, similar to Intel’s latest Raptor Lake processors, utilize caches to enhance performance. These caches can inadvertently mix up data with memory addresses, leading to potential data leakage. A well-designed cryptographic code should operate uniformly in time to prevent such vulnerabilities.

La Vulnérabilité des Puces M d’Apple: A Risk to Cryptocurrency Wallets

The discovery of this vulnerability also casts a shadow over the security of cryptocurrency wallets. Given the flaw’s capacity for cryptographic key extraction through side-channel attacks, users of cold wallets or hardware wallets connected to computers with vulnerable chips for transactions may face heightened risks. These vulnerabilities underscore the importance of assessing the security measures of cold wallets and hardware wallets against such exploits.

Impact on Cold Wallets and Hardware Wallets

Private key extraction poses a serious threat, especially when devices are connected to vulnerable computers for transactions. This vulnerability could compromise the very foundation of cryptocurrency security, affecting both local and remote attack scenarios.

Security Recommendations

Manufacturers of cold and hardware wallets must promptly assess and address their vulnerability to ensure user security. Users are advised to adhere to best security practices, such as regular updates and minimizing the connection of cold wallets to computers. An effective alternative is the utilization of Cold Wallet NFC HSM technology, such as Freemindtronic’s EviVault NFC HSM or EviSeed NFC HSM, embedded in Keepser and SeedNFC HSM products, offering robust protection against such vulnerabilities.

Apple M Chip Vulnerability: Unveiling the Unpatchable Flaw

This flaw, inherent to the microarchitecture of the chips, allows the extraction of cryptographic keys via a secondary channel during the execution of the cryptographic protocol.
This discovery of an “irreparable flaw” in Apple’s M-series chips could seriously compromise data security by allowing unauthorized extraction of encryption keys. This vulnerability constitutes a significant security flaw, posing a substantial risk to user data across various devices.

The Micro Architectural Rift and its Implications: Unveiling the Apple M Chip Vulnerability

Critical Flaw Discovered in Apple’s M-Chips

Moreover, the recent discovery of the ‘Apple M chip vulnerability’ in Apple’s M-series chips has raised major IT security concerns. This vulnerability, inherent in the silicon design, enables extraction of cryptographic keys through a side channel during the execution of standard cryptographic protocols. Furthermore, manufacturers cannot rectify this flaw with a simple software or firmware update, as it is embedded in the physical structure of processors.

Implications for Previous Generations

Additionally, the implications of the ‘Apple M chip vulnerability’ are particularly severe for earlier generations of the M-series, such as M1 and M2. Furthermore, addressing this flaw would necessitate integrating defenses into third-party cryptographic software, potentially resulting in noticeable performance degradation when performing cryptographic operations.

Hardware optimizations: a double-edged sword

Moreover, modern processors, including Apple’s M-series and Intel’s 13th Gen Raptor Lake microarchitecture, utilize hardware optimizations such as memory-dependent prefetching (DMP). Additionally, these optimizations, while enhancing performance, introduce security risks.

New DMP Research

Moreover, recent research breakthroughs have unveiled unexpected behavior of DMPs in Apple silicon. Additionally, DMPs sometimes confuse memory contents, such as cryptographic keys, with pointer values, resulting in data “dereference” and thus violating the principle of constant-time programming.

Additionally, we can conclude that the micro-architectural flaw and the unforeseen behaviors of hardware optimizations emphasize the need for increased vigilance in designing cryptographic chips and protocols. Therefore, addressing these vulnerabilities necessitates ongoing collaboration between security researchers and hardware designers to ensure the protection of sensitive data.

Everything you need to know about Apple’s M chip “GoFetch” flaw

Origin of the fault

The flaw, dubbed “GoFetch,” was discovered by researchers at the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology (MIT). It affects Apple’s M1, M2 and M3 chips and allows for the extraction of encryption keys, compromising data security1.

Level of hazardousness

The vulnerability is considered severe because it cannot be fixed by a simple software patch. Furthermore, it is due to a specific hardware optimization in the architecture of the chips, making it difficult to correct without significantly impacting the performance of the devices.

Apple’s response and actions taken

Moreover, to date, Apple has not yet officially communicated about this flaw. Security experts recommend the use of software solutions to mitigate risk, although this may reduce the performance of affected devices.

Source of the vulnerability report

The detailed report on this vulnerability has been published by CSAIL. For an in-depth understanding of the flaw and its implications, it is advisable to consult the full research paper provided by the researchers.

Understanding the ‘Apple M chip vulnerability’ and its ‘GoFetch’ flaw

Vulnerability Description

  • Data Memory-Dependent Prefetcher (DMP): Moreover, this function in Apple’s M chips is designed to improve performance by predicting and loading data that the CPU might need next. However, it has a vulnerability that can be exploited through a side-channel attack.
  • Side-Channel Attack: Additionally, the flaw allows attackers to observe the effects of the DMP’s operation, such as timing information, to infer sensitive data.
  • Encryption Key Extraction: Furthermore, by exploiting the DMP’s behavior, attackers can extract encryption keys that are used to secure data on the device. This includes keys from widely-used cryptographic protocols like OpenSSL Diffie-Hellman, Go RSA, CRYSTALS Kyber, and Dilithium.

Level of Hazardousness

Additionally, the “GoFetch” flaw is considered very dangerous because it is a hardware-level vulnerability. It cannot be fixed with a software update without potentially reducing chip performance.

The diagram illustrating the level of hazardousness of the micro-architectural flaw in the Apple M-Chip, specifically the “GoFetch” flaw, has been successfully created. Moreover, this visual representation captures the flaw’s inception at the Data Prefetching (DMP) function, its exploitation through the attack process, the subsequent extraction of encryption keys, and the final security impact, including compromised data privacy and security breaches.

Diagram showcasing the GoFetch vulnerability in Apple M-Chip, from data prefetching to security impact.
This diagram delineates the exploitation process of the GoFetch flaw in the Apple M-Chip, highlighting its hazardous impact on data security.
  1. Data Prefetching (DMP): Furthermore, a diagram component shows the DMP function, which is the initial target for the attack.
  2. Attack Process: Additionally, a flow demonstrates how the attacker exploits the DMP to initiate a side-channel attack.
  3. Encryption Key Extraction: Moreover, a depiction of the attacker successfully retrieving the encryption keys through the side-channel.
  4. Security Impact: Additionally, the final part of the diagram should show the potential risks, such as compromised data privacy and security breaches.

Impact and Timeline of Apple M1, M2, and M3 Chips: Assessing the ‘Apple M chip vulnerability’ Impact and Progression

The ‘Apple M chip vulnerability’ affects all Macs running Apple silicon, including M1, M2, and recent M3 chips. This includes a wide range of Mac and MacBook computers, which are now susceptible to side-channel attacks exploiting this vulnerability.

Apple computer affected by this flaw

The ‘Apple M chip vulnerability’ impacts a wide range of Apple hardware, starting with the launch of the first Mac system-on-chip, the M1, in November 2020. This hardware includes the M1, M1 Pro, M1 Max, M1 Ultra, M2, M2 Pro, M2 Max, M2 Ultra, M3, M3 Pro, and M3 Max chips.

Date Model Description
Nov 2020 M1 Introducing the M1 to MacBook Air, MacBook Pro, and Mac mini 13″
Apr 2021 M1 Launch of the iMac with M1 chip
Oct 2021 M1 Pro and M1 Max M1 Pro and M1 Max arrive in 14-inch and 16-inch MacBook Pros
March 2022 M1 Ultra M1 Ultra launches with Mac Studio
June 2022 M2 Next generation with the M2 chip
Jan. 2023 M2 Pro and M2 Max M2 Pro and M2 Max launch in 14-inch and 16-inch MacBook Pros, and Mac mini
June 2023 M2 Ultra M2 Ultra launches on Mac Studio and Mac Pro
Oct 2023 M3 M3 series with the M3, M3 Pro and M3 Max

To establish the extent of the problem of Apple’s M chip vulnerability and its consequences on a global scale, we sought to establish the most accurate statistics published on the internet to try to assess as accurately as possible the number of devices affected and the geographical scope of the impact.

The Magnitude of the ‘Apple M chip vulnerability’: Global Consequences and Statistics

The “GoFetch” vulnerability in Apple’s M chips has a potential impact on millions of devices around the world. Since the introduction of the M1 chip in November 2020, Apple has sold tens of millions of Mac computers with the M1, M2, and M3 chips, with a presence in more than 100 countries. This security flaw therefore represents a significant threat to data privacy and security on a global scale.

Potential Consequences:

  • Privacy breach: Because encryption keys can be extracted, sensitive user data is at risk.
  • Business impact: Organizations that rely on Apple devices for their operations could face costly data breaches.
  • Economic repercussions: Confidence in the safety of Apple products could be shaken, potentially affecting future sales.

It is crucial that users are aware of this vulnerability and take steps to secure their devices, pending an official response from Apple and potential solutions to mitigate the risks associated with this critical security breach.


In terms of sales, Apple’s A and M chips have seen impressive growth, with a 54% increase in revenue, reaching $2 billion in the first quarter. This positive trend reflects the widespread geographic impact and growing adoption of Apple Silicon technologies.

Based on available data, here is an estimate of the number of Apple computers with the M1, M2, and M3 chips sold, broken down by geographic region:

Statistics Table Detailed Statistics

Based on available data, here is an estimate of the number of Apple computers with the M1, M2, and M3 chips sold, broken down by geographic region:

Region Estimated sales
Americas 2 millions
Europe 1.5 million
Greater China 1 million
Japan 500 000
Middle East 300 000
Africa 200 000
Asia-Pacific 300 000
Latin America 100 000
Eastern Europe 100 000

Estimated total: 6 million units sold.

These estimates underscore the importance of the “GoFetch” vulnerability and the need for Apple to effectively respond to this security flaw on a global scale.

These estimates are based on market shares and sales trends in these regions. They give an idea of the distribution of sales of Macs with the M1, M2, and M3 chips outside of major markets.

These figures are based on overall sales and may vary depending on the sources and methods of calculation. Still, they give an idea of the scale of Apple’s M-chip distribution around the world and highlight the importance of the “GoFetch” vulnerability on a global scale. It’s important to note that these numbers are estimates, and exact sales data by country isn’t always published by Apple or third-party sources.

What are the Safeguards?

The IT security expert community emphasizes the importance of developing software solutions to mitigate risk, even if it could lead to a significant decrease in the performance of affected devices. Solutions like DataShielder Defense NFC HSM, developed by Freemindtronic, offer hardware or hybrid countermeasures to secure encryption keys

DataShielder NFC HSM

DataShielder Defense NFC HSM, developed by Freemindtronic, offers advanced security measures to protect encryption keys against vulnerabilities such as “GoFetch.” Utilizing AES-256 and RSA-4096 encryption through an NFC HSM and/or hybrid hardware and software HSM PGP for data encryption as well as wifi, Lan, Bluetooth, and NFC communication protocols, DataShielder enables externalized encryption for Apple computers, ensuring the confidentiality and integrity of sensitive data. This solution is particularly beneficial for businesses and organizations handling highly sensitive information, providing them with robust cybersecurity and security against potential cyber threats.

DataShielder HSM PGP

DataShielder HSM PGP provides a secure hybrid HSM PGP platform solution for generating, storing, and managing PGP keys, offering end-to-end encryption for email communications via a web browser. By integrating mechanisms for creating secure containers on multiple hardware supports that can be physically externalized from the computer, DataShielder HSM PGP enhances the confidentiality and authenticity of email exchanges by encrypting emails, thus mitigating the risk of interception or tampering by malicious actors. This solution is ideal for all types of businesses, financial institutions, and companies requiring stringent data protection measures without the risk of relying on their computers’ security vulnerabilities.

DataShielder Defense

DataShielder Defense provides comprehensive protection against hardware vulnerabilities and cyber threats by combining hardware and software hybrid encryption compatible with all types of storage media, including NFC HSM. It incorporates the management of various standard symmetric and asymmetric encryption keys, including freely selectable Open PGP encryption algorithms by the user. By protecting sensitive data at the hardware level, without servers, without databases, and in total anonymity, DataShielder Defense ensures a very high level of security considered post-quantum, offering a wide range of applications, including data storage, communication, and processing. This solution is particularly advantageous for governmental entities and organizations dealing with classified information. It serves as a counter-espionage tool suitable for organizations looking to strengthen their cybersecurity posture and mitigate risks associated with very complex emerging threats.

In summary, DataShielder solutions provide effective countermeasures against hardware vulnerabilities like “GoFetch,” offering organizations reliable protection for their sensitive data and critical assets. Through continuous innovation and collaboration with industry partners, DataShielder remains at the forefront of data security, empowering organizations to defend against evolving cyber threats and protect their digital infrastructure.

Let’s summarize

The recent discovery of a vulnerability in Apple M chips, dubbed “GoFetch,” by MIT researchers raises major concerns about data security on devices equipped with these chips. This flaw potentially exposes millions of Mac computers worldwide to side-channel attacks, compromising the privacy of stored information.

In conclusion on the vulnerability of Apple M series chips: Addressing the critical Apple M chip vulnerability

The vulnerability discovered in Apple’s M-series chips, known as “GoFetch,” by researchers at MIT underscores the significant challenges facing hardware manufacturers in terms of security. Effective safeguards, both in software and hardware, are crucial to mitigate risks and uphold the security of sensitive user data. Collaboration among manufacturers, security researchers, and government entities is essential to develop robust solutions and ensure protection against emerging threats.

In conclusion, the prompt identification and resolution of hardware vulnerabilities like “GoFetch” are imperative for maintaining user confidence and safeguarding the integrity of IT systems. Continuous evaluation and implementation of technological advancements and security best practices are necessary to provide adequate protection against potential threats.

Are fingerprint systems really secure? How to protect your data and identity against BrutePrint

Fingerprint Systems Really Secure - How to Protect Your Data and Identity
Fingerprint Systems Really Secure by Jacques Gascuel: This article will be updated with any new information on the topic.

Fingerprint Security

You will surely be amazed by our discoveries! These systems verify your identity on smartphones and other devices by using the unique patterns of your finger. But is their security level? In this study, we explore the weaknesses of these systems and how various actors, from cybercriminals to sovereign entities, can exploit them. We looked at 25 techniques for corrupting fingerprint authentication systems. We will also introduce an effective dual-use defense solution: DataShielder HSM solutions to protect your secrets and sensitive data even if this biometric authentication system becomes compromised.

Fingerprint Biometrics: An In-Depth Exploration of Security Mechanisms and Vulnerabilities

It is a widely recognized biometric authentication system for identity verification. In this overview of fingerprint authentication systems, we will explore comprehensively to understand the complex world of fingerprint biometrics. Our goal is to provide a detailed exploration of these systems, their inner workings, vulnerabilities, and countermeasures.

Demystifying Fingerprint Systems: A Thorough Examination

Two fundamental components make up these systems: the fingerprint sensor and the comparison algorithm.:

The Fingerprint Sensor: Where Biometric Data Begins

These systems rely on an essential component: the fingerprint sensor. It captures the finger image and converts it into a digital format. Different types of sensors exist, each with their advantages and disadvantages:

  1. Optical sensors: They use light and a camera to create a high-resolution image.
  2. Capacitive sensors: They use an array of small capacitors to measure the differences in electrical charge between the ridges and valleys.
  3. Ultrasonic sensors: They use sound waves to create a three-dimensional image.
  4. Thermal sensors: They detect the heat emitted by the finger to generate an image.

The Comparison Algorithm: The Gatekeeper of Access

The comparison algorithm is a critical software component that analyzes the captured fingerprint image. Its role is vital:

  • Image Analysis: The algorithm scrutinizes the fingerprint image, extracting its unique features.
  • Template Comparison: It then compares these features to one or more stored templates, serving as reference fingerprints for authorized users.
  • Threshold Criteria: Access is granted if the algorithm determines a significant similarity between the captured image and a stored template, surpassing a predefined threshold. If not, the system considers the fingerprint invalid and denies access.

Fingerprint System Vulnerabilities and Attack Techniques

First, before evaluating attack techniques against fingerprinting systems, let’s explore different attack types, techniques, motivations, and strategies. In our thorough analysis of fingerprint system vulnerabilities, we must acknowledge numerous attack techniques employed by various actors. These techniques, driven by diverse motivations ranging from personal gain to malicious intent, illuminate the complexities of fingerprint system security. We’ve identified a total of twenty-five (25) distinct attack types, categorized into five groups in this study: “Electronic Devices for Biometric Attacks,” “Additional Fingerprint Attacks,” “Advanced Attacks,” “Attacks on Lock Patterns,” and “Attacks on Fingerprint Sensors.”

Attacks on Fingerprint Sensors

Fingerprint sensors, a common biometric authentication method, are vulnerable to several attack types and techniques update 23 february 2024:

Residual Fingerprint Attack Recovers the smartphone owner’s fingerprint left on surfaces, reproducing it. Identity theft, unauthorized access, or malicious purposes. Exploits traces of fingerprints on surfaces using materials like gelatin, silicone.
Code Injection Attack Injects malicious code to bypass fingerprint sensor security. Compromises device security for data theft or illicit activities. Exploits software vulnerabilities for unauthorized access to biometric data.
False Acceptance Attack The system accepts a fingerprint that doesn’t belong to the authorized user. Identity theft, unauthorized access, or malicious intentions. Can occur due to poor sensor quality, a high tolerance threshold, or similarity between different individuals’ fingerprints.
False Rejection Attack The system rejects a fingerprint that belongs to the authorized user. Identity theft, unauthorized access. Can occur due to poor sensor quality, a low tolerance threshold, environmental changes, or alterations to the user’s fingerprint.
Substitution Attack Tricks the system with an artificial fingerprint. Identity theft or unauthorized access. Can be done using materials like gelatin, silicone, latex, or wax.
Modification Attack Tricks the system with a modified fingerprint. Identity theft or to conceal the user’s identity. Can be done using techniques like gluing, cutting, scraping, or burning.
Impersonation Attack Tricks the system with another user’s fingerprint, either with their consent or by force. Identity theft using force, threats, bribery, or seduction. Uses the fingerprint of another user who has given consent or has been coerced into doing so.
Adversarial Generation Attack Tricks the system with images of fingerprints generated by an adversarial generative adversarial network (GAN). Bypasses liveness detection methods based on deep learning. Mimics the appearance of real fingerprints.
Acoustic Analysis Attack Tricks the system by listening to the sounds emitted by the fingerprint sensor during fingerprint capture. Can reconstruct the fingerprint image from acoustic signals. Use noise cancellation techniques, encrypt acoustic signals, or use liveness detection methods
Partial Print Attack Tricks the system with a partial fingerprint from the registered fingerprint. Increases the false acceptance rate by exploiting the similarity between partial prints of different users. Can use a portion of the registered fingerprint.
Privilege Escalation Attack Exploits vulnerabilities in the operating system or application to obtain higher privileges than those granted by fingerprint authentication Can access sensitive data, manipulate system files, perform unauthorized actions, or bypass security measures Use strong passwords, enforce multi-factor authentication, limit user privileges, patch system vulnerabilities, monitor user activities, and audit logs
Spoofing Attack Imitates a legitimate fingerprint or identity to deceive the system or the user Can gain access, steal information, spread malware, or impersonate someone. Use liveness detection methods, verify the authenticity, avoid trusting unknown sources, and report spoofing attempts
PrintListener: Side-channel Attack Utilizes acoustic signals from finger friction on touchscreens to replicate fingerprints Gain unauthorized access to devices and services protected by fingerprint authentication Implement noise interference, use advanced fingerprint sensors resistant to acoustic analysis, enable multifactor authentication, regularly update security protocols

For more information on new attack type “PrintListener” (a specific acoustic analysis attack), readers are encouraged to explore the detailed article at https://freemindtronic.com/printlistener-technology-fingerprints/.
These attacks expose vulnerabilities in fingerprint sensor technology and underline the need for robust security measures.

Attacks on Lock Patterns (For Lock Screen Authentication)

Lock patterns, often used on mobile devices for screen unlocking, are susceptible to various attack techniques:

Brute Force Attack Attempts all possible lock pattern combinations. Gains unauthorized device access. Systematically tests different pattern combinations.
Replica Fingerprint Attack Uses a 3D printer to create a replica fingerprint. Unauthorized access or identity theft. Produces a replica for sensor authentication.
Sensor Vulnerabilities Exploits sensor technology vulnerabilities. Compromises device security for malicious purposes. Identifies and exploits sensor technology weaknesses.
BrutePrint Attack Intercepts messages, emulating the fingerprint sensor. Gains unauthorized access, often with hardware components. Exploits communication protocol vulnerabilities.

These attacks target the vulnerabilities in lock pattern authentication and underscore the importance of strong security practices.

Advanced Attacks

Advanced attacks employ sophisticated techniques and technologies to compromise fingerprint systems:

Presentation Attack Presents manipulated images or counterfeit fingerprints. Espionage, identity theft, or malicious purposes. Crafts counterfeit fingerprints or images to deceive sensors.
Rapid Identification Attack Uses advanced algorithms to swiftly identify fingerprints. Corporate espionage, financial gain, or enhanced security. Quickly identifies fingerprints from extensive datasets.
Digital Footprint Attack Collects and analyzes the online data and activity of the target, using open source intelligence tools or data brokers Can obtain personal information, preferences, habits, or vulnerabilities of the target. Use privacy settings, delete unwanted data, avoid oversharing, and monitor online reputation

These advanced attacks leverage technology and data to compromise fingerprint-based security.

Network-Based Attacks

Network-based attacks are those that target the communication or data transmission between the device and the fingerprint authentication system. These attacks can compromise the integrity, confidentiality, or availability of the biometric data or the user session. In this section, we will discuss four types of network-based attacks: phishing, session hijacking, privilege escalation, and spyware.

Phishing Attack Technique: Phishing attacks involve sending fraudulent messages to victims, enticing them to click on a link or download an attachment. These malicious payloads may contain code designed to steal their fingerprints or redirect them to a fake website requesting authentication. Motivations: Phishing attacks are motivated by the desire to deceive and manipulate users into revealing their fingerprint data or login credentials. Strategies: Phishing attackers employ various tactics, such as crafting convincing emails, spoofing legitimate websites, and using social engineering to trick users.
Session Hijacking Attack Technique: Session hijacking attacks aim to intercept or impersonate an authenticated user’s session, exploiting communication protocol vulnerabilities or using spyware. Motivations: Session hijacking is typically carried out to gain unauthorized access to sensitive information or systems, often for financial gain or espionage. Strategies: Attackers employ packet sniffing, session token theft, or malware like spyware to compromise and take control of active user sessions.
Spyware Attack Technique: Spyware attacks infect the device with spyware to capture fingerprint data. Motivations: Spyware attacks are driven by the objective of illicitly obtaining biometric data for malicious purposes, such as identity theft or unauthorized access. Strategies: Attackers use spyware to secretly record and transmit fingerprint information, often through backdoors or covert channels, without the victim’s knowledge.
Predator Files Infects Android phones with a spyware application that can access their data, including fingerprint information. Sold to multiple governments for targeting political opponents, journalists, activists, and human rights defenders in over 50 countries. Use spyware detection and removal tools, update system software, avoid downloading untrusted applications, and scan devices regularly

As we can see from the table above, network-based attacks pose a serious threat to fingerprint authentication systems and users’ privacy and security. Therefore, it is essential to implement effective countermeasures and best practices to prevent or mitigate these attacks. In the next section, we will explore another category of attacks: physical attacks.

Electronic Devices for Biometric Attacks

Some electronic devices are designed to target and compromise fingerprint authentication systems. Here are some notable examples:

Device Description Usage STRATEGIES
Cellebrite UFED A portable device capable of extracting, decrypting, and analyzing data from mobile phones, including fingerprint data. Used by law enforcement agencies worldwide. Used by law enforcement agencies to access digital evidence on mobile phones. Applies substances to damage or obscure sensor surfaces.
GrayKey A black box device designed to unlock iPhones protected by passcodes or fingerprints using a “brute force” technique. Sold to law enforcement and government agencies for investigative purposes. Sold to law enforcement and government agencies for investigative purposes to unlock iPhones. Use strong passwords, enable encryption, disable USB access, and update system software.
Chemical Attacks Alters or erases fingerprints on sensors. Prevents identification or creates false identities. Use fingerprint enhancement techniques, verify the authenticity, and use liveness detection methods

These devices pose a high risk to biometric systems because they can allow malicious actors to access sensitive information or bypass security measures. They are moderate to high in ease of execution because they require physical access to the target devices and the use of costly or scarce devices. Their historical success is variable because it depends on the quality of the devices and the security of the biometric systems. They are currently relevant because they are used by various actors, such as government agencies, law enforcement, or hackers, to access biometric data stored on mobile phones or other devices. This comprehensive overview of attack types, techniques, motivations, and strategies is crucial for improving biometric authentication system security.

BrutePrint: A Novel Attack on Fingerprint Systems on Phones

Fingerprint systems on phones are not only vulnerable to spoofing or data breach attacks; they are also exposed to a novel attack called BrutePrint. This attack exploits two zero-day vulnerabilities in the smartphone fingerprint authentication (SFA) framework. BrutePrint allows attackers to bypass the attempt limit and liveness detection mechanisms of fingerprint systems on phones. It also enables them to perform unlimited brute force attacks until finding a matching fingerprint.

How BrutePrint Works

Fingerprint Systems Really Secure : BrutePrint

BrutePrint works by hijacking the fingerprint images captured by the sensor. It applies neural style transfer (NST) to generate valid brute-forcing inputs from arbitrary fingerprint images. BrutePrint also exploits two vulnerabilities in the SFA framework:

  • Cancel-After-Match-Fail (CAMF): this vulnerability allows attackers to cancel the authentication process after a failed attempt. It prevents the system from counting the failed attempts and locking the device.
  • Match-After-Lock (MAL): this vulnerability allows attackers to infer the authentication results even when the device is in “lock mode”. It guides the brute force attack.To perform a BrutePrint attack, attackers need physical access to the phone, a database of fingerprints, and a custom-made circuit board that costs about 15 dollars. The circuit board acts as a middleman between the sensor and the application. It intercepts and manipulates the fingerprint images.

How to Prevent BrutePrint

BrutePrint is a serious threat to phone users who rely on fingerprint systems to protect their devices and data. It shows that fingerprint systems on phones are not as secure as they seem. They need more robust protection mechanisms against brute force attacks. Some of the possible ways to prevent BrutePrint are:

  • Updating the phone’s software: this can help fix the vulnerabilities exploited by BrutePrint and improve the security of the SFA framework.
  • Using multifactor authentication: this can increase the level of security and reduce the risks of spoofing or brute force attacks. It combines fingerprint authentication with another factor, such as a password, a PIN code, a pattern lock screen ,or other trust criteria that allows patented segmented key authentication technology developed by Freemindtronic in Andorra .
  • Use of DataShielder HSM solutions: these are solutions developed by Freemindtronic in Andorra that allow you to create HSM (Hardware Security Module) on any device, without a server or database, to encrypt any type of data. DataShielder HSM solutions also include EviSign technology, which enables advanced electronic signing of documents. DataShielder HSM solutions are notably available in Defense versions, which offer a high level of protection for civil and/or military applications.

Assessing Attack Techniques: Ease of Execution and Current Relevance

In our pursuit of understanding fingerprint system vulnerabilities, it is crucial to assess not only the types and forms of attacks but also their practicality and current relevance. This section provides an in-depth evaluation of each attack technique, considering factors such as the ease of execution, historical success rates, and their present-day applicability.

Attack Techniques Overview

Let’s analyze the spectrum of attack techniques, considering their potential danger, execution simplicity, historical performance, and present-day relevance.

Attack Type Level of Danger Ease of Execution Historical Success Current Relevance
Residual Fingerprint Attack Medium Moderate Variable Ongoing
Code Injection Attack High Moderate Variable Still Relevant
Acoustic Analysis Attack Medium Low Fluctuating Ongoing Concerns
Brute Force Attack High Low Variable Contemporary
Replica Fingerprint Attack Medium Moderate Fluctuating Still Relevant
Sensor Vulnerabilities High Moderate Variable Ongoing Significance
BrutePrint Attack High High Variable Continues to Pose Concerns
Presentation Attack High Moderate Diverse Still Pertinent
Rapid Identification Attack High Low Variable Ongoing Relevance
Digital Footprint Attack High Low Fluctuating Currently Pertinent
Chemical Attacks High Low Variable Ongoing Relevance
Phishing Attack High Moderate Variable Modern Threat
Session Hijacking Attack High Low Variable Ongoing Relevance
Privilege Escalation Attack High Low Variable Remains Significant
Adversarial Generation Attack High Moderate Variable Still in Use
Acoustic Analysis Attack (Revisited) Medium Low Fluctuating Ongoing Concerns
Partial Print Attack Medium Low Variable Currently Relevant
Electronic Devices for Biometric Attacks High Moderate to High Variable Currently Relevant
PrintListener (Specific Acoustic Analysis Attack) High Moderate Emerging Highly Relevant

Understanding the Evaluation:

  • Level of Danger categorizes potential harm as Low, Moderate, or High.
  • Ease of Execution is categorized as Low, Medium, or High.
  • Historical Success highlights fluctuating effectiveness.
  • Current Relevance signifies ongoing concerns in contemporary security landscapes.

By assessing these attack techniques meticulously, we can gauge their practicality, historical significance, and continued relevance.

The type of attack by electronic devices for biometric systems is very dangerous because it can allow malicious actors to access sensitive information or bypass the protections of biometric systems. Its ease of execution is moderate to high, as it requires physical access to target devices and the use of expensive or difficult-to-obtain devices. Its historical success is variable because it depends on the quality of the devices used and the security measures implemented by the biometric systems. It is currently relevant because it is used by government agencies, law enforcement or hackers to access biometric data stored on mobile phones or other devices.

Statistical Insights into Fingerprint Systems

Fingerprint systems have found wide-ranging applications, from law enforcement and border control to banking, healthcare, and education. They are equally popular among consumers who use them to unlock devices or access online services. However, questions linger regarding their reliability and security. Let’s delve into some pertinent statistics:

According to Acuity Market Intelligence, 2018 saw more than 1.5 billion smartphones equipped with fingerprint sensors, constituting 60% of the global market.

The IAFIS Annual Report of 2020 revealed that more than 1.3 billion fingerprint records were stored in national and international databases in 2019.

According to the National Institute of Standards and Technology (NIST), the average false acceptance rate of fingerprint systems in 2018 was 0.08%, marking an 86% decrease compared to 2013.

These statistics shed light on the widespread adoption of fingerprint systems and their improved accuracy over time. Nevertheless, they also underline that these systems, while valuable, are not without their imperfections and can still be susceptible to errors or manipulation.

Real-World Cases of Fingerprint System Corruption: Phone Cases

Fingerprint system corruption can also affect phone users, who rely on fingerprint sensors to unlock their devices or access online services. However, these sensors are not foolproof and can be bypassed or exploited by skilled adversaries. These attacks can result in device theft, data breaches, or other security issues.

Here are some examples of fingerprint system corruption that involve phones:

  • German hacker Jan Krissler, alias Starbug, remarkably unlocked the smartphone of the German Defense Minister Ursula von der Leyen in 2014 using a high-resolution photo of her thumb taken during a press conference. He employed image processing software to enhance the photo’s quality and created a counterfeit fingerprint printed on paper.
  • A terrorist attack at the Istanbul airport killed 45 people and injured more than 200 in 2016. The investigators found that the three suicide bombers used fake fingerprints to enter Turkey and avoid security checks. They copied the fingerprints of other people from stolen or forged documents.
  • Researchers from Tencent Labs and Zhejiang University discovered in 2020 that they could bypass a fingerprint lock on Android smartphones using a brute force attack, that is when a large number of attempts are made to discover a password, code or any other form of security protection.
  • Experts from Cisco Talos created fake fingerprints capable of fooling the sensors of smartphones, tablets and laptops as well as smart locks in 2020, but it took them a lot of effort.
  • A case of identity theft was discovered in France in 2021, involving the use of fake fingerprints to obtain identity cards and driving licenses. The suspects used silicone molds to reproduce the fingerprints of real people, and then glued them on their fingers to fool the biometric sensors.
  • Researchers from the University of Buffalo developed a method in 2021 to create artificial fingerprints from images of fingers. These fingerprints can fool the sensors of smartphones, but also more advanced biometric systems, such as those used by police or airports.
  • A report by Kaspersky revealed in 2021 that banking apps on smartphones are vulnerable to attacks by falsified fingerprints. Attackers can use malware to intercept biometric data from users and use them to access their accounts.

These cases highlight the significant threats posed by fingerprint system corruption to phone users. Therefore, it is important to protect these systems against external and internal threats while integrating advanced technologies to enhance security and reliability.

DataShielder HSM: A Counter-Espionage Solution for Fingerprint System Security

You have learned in the previous sections that fingerprint systems are not foolproof. They can be corrupted by attacks that expose your secrets and sensitive data. To prevent malicious actors from capturing them, you need an effective and reliable encryption solution, even if your phone is compromised.

Freemindtronic, the leader in NFC HSM technologies, designed, developed, published and manufactured DataShielder HSM in Andorra. It is a range of solutions that you need. You can use either EviCore NFC HSM or EviCore HSM OpenPGP technology with DataShielder HSM. It lets you encrypt your data with segmented keys that you generate randomly yourself. The key segments are securely encrypted and stored in different locations. To access your secrets and your sensitive data encrypted in AES 256 quantum, you need to bring all segments together for authentication.

DataShielder HSM has two versions: DataShielder NFC HSM for civil and military use, and DataShielder NFC HSM Defense for sovereign use. DataShielder NFC HSM Defense integrates two technologies: EviCore NFC HSM and EviCore HSM OpenPGP. They allow you to create a hardware security module (HSM) without contact on any medium, without server, without database, totally anonymous, untraceable and undetectable.

DataShielder HSM is a user-friendly and compatible solution with all types of phone, with or without NFC, Android or Apple. It can be used for various purposes, such as securing messaging services, encrypting files or emails, signing documents or transactions, or generating robust passwords.

DataShielder HSM is a counter-espionage solution that enhances the security of fingerprint systems. It protects your data and secrets from unauthorized access, even if your fingerprint is compromised.

Current Trends and Developments in Fingerprint Biometrics

Fingerprint biometrics is a constantly evolving field. It seeks to improve the performance, reliability and security of existing systems. But it also develops new technologies and applications. Here are some current or expected trends and developments in this field.

  • Multimodality: it consists of combining several biometric modalities (fingerprint, face, iris, voice, etc.) to increase the level of security and reduce the risks of error or fraud. For example, some smartphones already offer authentication by fingerprint and facial recognition.
  • Contactless biometrics: it consists of capturing fingerprints without the need to touch a sensor. This technique avoids the problems related to the quality or contamination of fingerprints. And it improves the comfort and hygiene of users. For example, some airports already use contactless scanners to verify the identity of travelers.
  • Behavioral biometrics: it consists of analyzing the behavior of users when they interact with a biometric system. For instance, the way they place their finger on the sensor or the pressure they exert. This technique adds a dynamic factor to identification. And it detects attempts of impersonation or coercion. For example, some banking systems already use behavioral biometrics to reinforce the security of transactions.

Standards and Regulations for Fingerprint Systems

The use of fingerprint systems is subject to standards and regulations. They aim to ensure the quality, compatibility and security of biometric data. These standards and regulations can be established by international, national or sectoral organizations. Here are some examples of standards and regulations applicable to fingerprint systems.

  • The ISO/IEC 19794-2 standard: it defines the format of fingerprint data. It allows to store, exchange and compare fingerprints between different biometric systems. It specifies the technical characteristics, parameters and procedures to be respected to ensure the interoperability of systems.
  • The (EU) 2019/1157 regulation: it concerns the strengthening of the security of identity cards and residence permits issued to citizens of the European Union and their relatives. It provides for the mandatory introduction of two fingerprints in a digital medium integrated into the card. It aims to prevent document fraud and identity theft.
  • The Data Protection Act: it regulates the collection, processing and storage of personal data, including biometric data. It imposes on data controllers to respect the principles of lawfulness, fairness, proportionality, security and limited duration of data. It guarantees to data subjects a right of access, rectification and opposition to their data.

Examples of Good Practices for Fingerprint System Security

Fingerprint systems offer a convenient and effective way to authenticate people. But they are not without risks. It is important to adopt good practices to strengthen the security of fingerprint systems and protect the rights and freedoms of users. Here are some examples of good practices to follow by end users, businesses and governments.

  • For end users: it is recommended not to disclose their fingerprints to third parties, not to use the same finger for different biometric systems, and to check regularly the state of their fingerprints (cuts, burns, etc.) that may affect recognition. It is also advisable to combine fingerprint authentication with another factor, such as a password or a PIN or other trust criteria that allows the patented segmented key authentication technology developed by Freemindtronic in Andorra.
  • For businesses: it is necessary to comply with the applicable regulation on the protection of personal data, and to inform employees or customers about the use and purposes of fingerprint systems. It is also essential to secure biometric data against theft, loss or corruption, by using encryption, pseudonymization or anonymization techniques.
  • For governments: it is essential to define a clear and consistent legal framework on the use of fingerprint systems, taking into account ethical principles, fundamental rights and national security needs. It is also important to promote international cooperation and information exchange between competent authorities, in compliance with existing standards and conventions.

Responses to Attacks

Fingerprint systems can be victims of attacks aimed at bypassing or compromising their operation. These attacks can have serious consequences on the security of people, property or information. It is essential to know how to react in case of successful attack against a fingerprint system. Here are some recommendations to follow in case of incident.

  • Detecting the attack: it consists of identifying the type, origin and extent of the attack, using monitoring, auditing or forensic analysis tools. It is also necessary to assess the potential or actual impact of the attack on the security of the system and users.
  • Containing the attack: it consists of isolating the affected system or the source of the attack, by cutting off network access, disabling the biometric sensor or blocking the user account. It is also necessary to preserve any evidence that may facilitate investigation.
  • Notifying the attack: it consists of informing competent authorities, partners or users concerned by the attack, in compliance with legal and contractual obligations. It is also necessary to communicate on the nature, causes and consequences of the attack, as well as on the measures taken to remedy it.
  • Repairing the attack: it consists of restoring the normal functioning of the fingerprint system, by eliminating the traces of the attack, resetting the settings or replacing the damaged components. It is also necessary to revoke or renew the compromised biometric data, and verify the integrity and security of the system.
  • Preventing the attack: it consists of strengthening the security of the fingerprint system, by applying updates, correcting vulnerabilities or adding layers of protection. It is also necessary to train and raise awareness among users about good practices and risks related to fingerprint systems.

Next Steps for Fingerprint Biometrics Industry

Fingerprint biometrics is a booming field, which offers many opportunities and challenges for industry, society and security. Here are some avenues for reflection on the next steps for this field.

  • Research and development: it consists of continuing efforts to improve the performance, reliability and security of fingerprint systems, but also to explore new applications and technologies. For example, some researchers are working on artificial fingerprints generated by artificial intelligence, which could be used to protect or test biometric systems.
  • Future investments: it consists of supporting the development and deployment of fingerprint systems, by mobilizing financial, human and material resources. For example, according to a market study, the global market for fingerprint systems is expected to reach 8.5 billion dollars in 2025, with an average annual growth rate of 15.66%.
  • Expected innovations: it consists of anticipating the needs and expectations of users, customers and regulators, by offering innovative and adapted solutions. For example, some actors in the sector envisage creating fingerprint systems integrated into human skin, which could offer permanent and inviolable identification.


Fingerprint systems are a convenient and fast way to authenticate users, based on their unique fingerprint patterns. They have many applications in device protection and online service access. However, these systems are not immune to attacks by skilled adversaries, who can manipulate and exploit them. These attacks can lead to unauthorized access, data breaches, and other security issues.

To prevent these threats, users need to be vigilant and enhance security with additional factors, such as PINs, passwords, or patterns. Moreover, regular system updates are crucial to fix emerging vulnerabilities.

Fingerprint systems are still a valuable and common form of authentication. But users must understand their weaknesses and take steps to strengthen system integrity and data protection. One of the possible steps is to use DataShielder HSM solutions, developed by Freemindtronic in Andorra. These solutions allow creating HSM (Hardware Security Module) on any device, without server or database, to encrypt and sign any data. DataShielder HSM solutions also include EviSign technology, which allows electronically signing documents with a legally recognized value. DataShielder HSM solutions are available in different versions, including Defense versions, which offer a high level of protection for civil and military applications.