CVE-2013-4347 in python-oauth2
Summary
by MITRE
The (1) make_nonce, (2) generate_nonce, and (3) generate_verifier functions in SimpleGeo python-oauth2 uses weak random numbers to generate nonces, which makes it easier for remote attackers to guess the nonce via a brute force attack.
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Analysis
by VulDB Data Team • 03/21/2022
The vulnerability identified as CVE-2013-4347 affects the python-oauth2 library developed by SimpleGeo and specifically targets the nonce generation mechanisms within the OAuth 1.0a implementation. This flaw resides in three critical functions: make_nonce, generate_nonce, and generate_verifier, which are responsible for creating random values used to prevent replay attacks in the OAuth authentication process. The issue stems from the use of weak random number generation algorithms that do not provide sufficient entropy for cryptographic security purposes.
The technical flaw in this vulnerability manifests through the utilization of pseudo-random number generators that lack proper cryptographic strength. When these functions generate nonces, they employ random number generation methods that are predictable or have insufficient randomness properties to withstand brute force attacks. This weakness directly violates the fundamental security requirements for nonce generation in cryptographic protocols, as outlined in the cryptographic standards and best practices established by organizations such as NIST and the IETF. The vulnerability creates a scenario where an attacker can systematically guess valid nonces by performing brute force operations, significantly reducing the security margin of the OAuth implementation.
The operational impact of this vulnerability extends beyond simple authentication bypasses, as it fundamentally undermines the security model of the OAuth protocol. Attackers who can predict or guess nonces gain the ability to replay previous authentication requests, potentially leading to unauthorized access to protected resources. This weakness particularly affects systems that rely on OAuth for third-party authentication and authorization, making them susceptible to man-in-the-middle attacks and session hijacking attempts. The vulnerability aligns with the ATT&CK framework's concept of credential access through the exploitation of weak cryptographic implementations, specifically targeting the credential validation process. The impact is particularly severe in environments where sensitive data is accessed through OAuth-protected APIs, as successful exploitation could lead to complete system compromise.
Mitigation strategies for this vulnerability require immediate remediation through library updates and code modifications. Organizations should upgrade to patched versions of the python-oauth2 library or implement proper cryptographic random number generation using libraries such as the secrets module in Python 3.6+ or the os.urandom() function for generating cryptographically secure random values. The fix involves replacing the weak random number generators with cryptographically secure alternatives that meet the requirements specified in NIST SP 800-90A for random number generation. Additionally, system administrators should conduct comprehensive security assessments of all applications using this library and implement monitoring for suspicious authentication patterns that might indicate nonce guessing attempts. The vulnerability serves as a critical reminder of the importance of cryptographic best practices and the necessity of using well-vetted, secure random number generation methods in security-critical applications.