CVE-2024-47869 in Gradio
Summary
by MITRE • 10/11/2024
Gradio is an open-source Python package designed for quick prototyping. This vulnerability involves a **timing attack** in the way Gradio compares hashes for the `analytics_dashboard` function. Since the comparison is not done in constant time, an attacker could exploit this by measuring the response time of different requests to infer the correct hash byte-by-byte. This can lead to unauthorized access to the analytics dashboard, especially if the attacker can repeatedly query the system with different keys. Users are advised to upgrade to `gradio>4.44` to mitigate this issue. To mitigate the risk before applying the patch, developers can manually patch the `analytics_dashboard` dashboard to use a **constant-time comparison** function for comparing sensitive values, such as hashes. Alternatively, access to the analytics dashboard can be disabled.
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Analysis
by VulDB Data Team • 10/11/2024
The vulnerability identified as CVE-2024-47869 affects Gradio, an open-source Python framework used for rapid prototyping of machine learning applications and web interfaces. This security flaw resides within the analytics_dashboard function where the system performs hash comparisons using a non-constant time algorithm. The timing attack vulnerability stems from the implementation's failure to use constant-time comparison functions when validating authentication tokens or hash values. Attackers can exploit this weakness by measuring response times between different requests to infer the correct hash values through byte-by-byte analysis. The vulnerability specifically targets the authentication mechanism that protects access to the analytics dashboard, making it a critical concern for systems where unauthorized access to analytics data could compromise sensitive information or system integrity.
The technical implementation of this vulnerability follows the pattern described in CWE-330, which addresses the use of weak random number generators and timing attacks in cryptographic implementations. The flaw occurs because standard string comparison functions in Python execute character-by-character and return immediately upon detecting a mismatch, creating measurable time differences that attackers can exploit. This timing variation allows an attacker to determine the correct hash value by sending multiple requests and observing response times, effectively performing a side-channel attack against the authentication system. The vulnerability is particularly dangerous because it enables credential guessing attacks where an attacker can iteratively determine the correct hash value without requiring brute force computation, as the timing differences provide direct feedback on the correctness of each guessed byte.
The operational impact of this vulnerability extends beyond simple unauthorized access to the analytics dashboard. Systems using affected Gradio versions could experience data leakage through compromised analytics access, potentially exposing user behavior patterns, system usage statistics, or even underlying model performance metrics. The timing attack methodology aligns with techniques documented in the MITRE ATT&CK framework under the T1212 technique for exploitation of information disclosures. Organizations relying on Gradio for machine learning applications or data science prototyping may face reputational damage, regulatory compliance issues, and potential exposure of sensitive training data or model parameters. The vulnerability affects any system where Gradio's analytics dashboard is enabled and accessible, particularly in enterprise environments where analytics data may contain proprietary or confidential information.
Mitigation strategies for CVE-2024-47869 follow established security practices for preventing timing attacks and align with the recommendations in the OWASP Top Ten security controls. The primary solution involves upgrading to Gradio version 4.44 or later, which implements proper constant-time comparison functions for hash validation. Prior to upgrading, developers can manually implement constant-time comparison using functions like those provided in the cryptography library or by implementing custom comparison functions that process all bytes regardless of matches. Alternative mitigations include disabling the analytics dashboard entirely through configuration settings or implementing additional access controls such as IP whitelisting, authentication barriers, or rate limiting to reduce the effectiveness of timing attacks. Organizations should also consider implementing monitoring for unusual access patterns that might indicate exploitation attempts and ensure proper patch management processes are in place to address similar vulnerabilities in other components of their software stack.