CVE-2024-3574
Published: 16 April 2024
Summary
CVE-2024-3574 is a high-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Scrapy Scrapy. Its CVSS base score is 7.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Steal Application Access Token (T1528); ranked at the 30.7th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
This vulnerability is AI-related — categorised as Data Processing Libraries; in the Privacy and Disclosure risk domain.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-0622
Vulnerability details
In scrapy version 2.10.1, an issue was identified where the Authorization header, containing credentials for server authentication, is leaked to a third-party site during a cross-domain redirect. This vulnerability arises from the failure to remove the Authorization header when redirecting…
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across domains. The exposure of the Authorization header to unauthorized actors could potentially allow for account hijacking.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Data Processing Libraries
- Risk Domain
- Privacy and Disclosure
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Scrapy is a web scraping framework used for data extraction, commonly in AI/ML data pipelines for collecting training data, and the CVE was reported on an AI/ML bug bounty platform (huntr).
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
The vulnerability leaks Authorization headers containing application access tokens or credentials to third-party sites via cross-domain redirects, facilitating T1528: Steal Application Access Token.
Affected Assets
Mitigating Controls
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Automated marking applies security attributes to system outputs, making it harder for attackers to exploit unmarked sensitive information leading to unauthorized exposure.
Proper attribute retention and permitted-value enforcement limits unauthorized actors from accessing sensitive information lacking correct labels.
Prevents unauthorized exposure of sensitive information by prohibiting untrusted external systems from processing or storing it.
By enforcing authorization matching prior to sharing, the control reduces the risk of exposing sensitive information to unauthorized actors.
Review and removal of nonpublic information from publicly accessible systems directly prevents exposure of sensitive data to unauthorized actors.
Data mining protection mechanisms detect and block unauthorized bulk extraction of sensitive data, directly mitigating exposure to unauthorized actors.
Literacy training teaches users to recognize and avoid actions that result in unauthorized exposure of sensitive information.
Retaining and monitoring training records confirms personnel have completed privacy and security awareness training on handling sensitive data, reducing the chance of unauthorized exposure due to lack of knowledge.