CVE-2023-6482 in Fingerprint Driver
Summary
by MITRE • 01/27/2024
Use of encryption key derived from static information in Synaptics Fingerprint Driver allows
an attacker to set up a TLS session with the fingerprint sensor and send restricted commands to the fingerprint sensor. This may allow an attacker, who has physical access to the sensor, to enroll a fingerprint into the template database.
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Analysis
by VulDB Data Team • 02/20/2024
The vulnerability identified as CVE-2023-6482 resides within the Synaptics Fingerprint Driver implementation where encryption keys are derived from static information rather than dynamically generated cryptographic material. This fundamental flaw in key derivation undermines the security posture of the fingerprint sensor system by creating predictable cryptographic materials that can be exploited by adversaries. The static nature of key derivation means that once an attacker understands the algorithm and can access the sensor, they can potentially reconstruct the encryption keys used for secure communication between the fingerprint reader and the host system. This weakness directly violates established cryptographic best practices and represents a significant deviation from industry standards such as those outlined in NIST SP 800-133 for key management and the CWE-327 weakness category for use of insecure cryptographic algorithms.
The technical exploitation of this vulnerability occurs through the establishment of a TLS session with the fingerprint sensor, which allows an attacker with physical access to the device to send restricted commands that would normally be protected from unauthorized execution. The fingerprint sensor's communication protocol appears to rely on the compromised static key derivation process to authenticate and encrypt communications, making it possible for an attacker to bypass normal access controls. This attack vector is particularly concerning because it leverages the physical proximity requirement that is often considered a security boundary, but the static key derivation renders this boundary ineffective. The attacker can potentially enroll new fingerprints into the template database, effectively creating unauthorized access credentials that can be used to bypass biometric authentication mechanisms. This represents a direct violation of the principle of least privilege and can be categorized under ATT&CK technique T1550.002 for use of valid credentials, as the attacker essentially gains legitimate access through compromised cryptographic infrastructure.
The operational impact of this vulnerability extends beyond simple unauthorized fingerprint enrollment, as it fundamentally compromises the integrity and confidentiality of the biometric authentication system. The compromised sensor can be used to create multiple unauthorized enrollment templates, potentially allowing attackers to impersonate legitimate users or gain persistent access to systems protected by fingerprint authentication. This vulnerability affects devices where Synaptics fingerprint drivers are deployed, including laptops, desktops, and other computing platforms that utilize fingerprint sensors for user authentication. The risk is particularly elevated in enterprise environments where physical security controls may be insufficient, as the vulnerability can be exploited by insiders or individuals with physical access to devices. Organizations may face significant security implications including potential data breaches, unauthorized system access, and compromise of user identities that rely on fingerprint authentication as a primary security control. The vulnerability also highlights the importance of proper key management practices and demonstrates how static key derivation can create cascading security failures in embedded systems and IoT devices where physical access is often unavoidable.
Mitigation strategies for CVE-2023-6482 should focus on immediate firmware updates from Synaptics to address the static key derivation implementation and replace it with dynamically generated cryptographic keys that incorporate proper entropy sources. System administrators should implement additional physical security controls such as device enclosures, tamper detection mechanisms, and monitoring for unauthorized physical access attempts. Network segmentation and access controls should be strengthened to limit the potential impact of successful exploitation, while also ensuring that fingerprint authentication systems are properly integrated with broader identity and access management frameworks. Regular security assessments should be conducted to identify similar vulnerabilities in other embedded systems and cryptographic implementations, with particular attention to how static information is used in key derivation processes. Organizations should also consider implementing additional authentication factors such as multi-factor authentication to reduce reliance on single biometric factors that may be compromised through vulnerabilities like this one. The remediation process should include thorough testing of updated firmware to ensure that the key derivation process properly incorporates cryptographic randomness while maintaining compatibility with existing fingerprint sensor functionality.