CVE-2008-7138 in Eye-Fi Manager
Summary
by MITRE
The Manager in Eye-Fi 1.1.2 generates predictable snonce values based on the time of day, which allows remote attackers to bypass authentication and upload arbitrary images by guessing the snonce.
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Analysis
by VulDB Data Team • 12/17/2017
The Eye-Fi Manager software version 1.1.2 contains a critical authentication bypass vulnerability that stems from predictable sequence number generation. This flaw resides in the cryptographic implementation where the system generates snonce values based on temporal information rather than using cryptographically secure random number generation. The vulnerability creates a significant security risk as it allows remote attackers to predict these sequence numbers and subsequently bypass the authentication mechanism required for image uploads.
The technical implementation of this vulnerability demonstrates poor entropy generation practices that align with CWE-330, which addresses the use of insufficiently random values in cryptographic contexts. The snonce values are generated using time-based algorithms that provide attackers with a predictable pattern, making it feasible to guess valid sequence numbers without requiring brute force attacks against a large search space. This predictable nature of the sequence numbers directly violates fundamental security principles for cryptographic protocols.
From an operational perspective, this vulnerability enables unauthorized remote access to Eye-Fi devices, allowing attackers to upload arbitrary images without proper authentication. The impact extends beyond simple unauthorized access as it provides a pathway for potential data exfiltration, image manipulation, and possible further exploitation of the device. The attack vector is particularly concerning because it requires no local access or physical presence, making it a significant risk for wireless devices that are typically deployed in sensitive environments.
The vulnerability's exploitation aligns with ATT&CK technique T1566, which covers credential harvesting through social engineering and network-based attacks. Attackers can leverage this flaw to establish persistent access to Eye-Fi devices, potentially creating a backdoor for future malicious activities. The temporal predictability of the snonce values means that attackers only need to synchronize their attacks with the device's time-based generation patterns, significantly reducing the complexity of the attack.
Mitigation strategies should include immediate firmware updates to implement cryptographically secure random number generation for sequence number creation, along with proper entropy sources that are not based on predictable temporal information. Organizations should also implement network segmentation to limit access to Eye-Fi devices and consider deploying additional authentication layers. The vulnerability highlights the importance of proper cryptographic implementation practices and demonstrates how seemingly minor implementation flaws can create significant security risks in networked devices.