CVE-2025-54133
Published: 02 August 2025
Summary
CVE-2025-54133 is a medium-severity OS Command Injection (CWE-78) vulnerability in Anysphere Cursor. Its CVSS base score is 5.3 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploitation for Client Execution (T1203); ranked in the top 49.6% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as Enterprise AI Assistants; in the Privacy and Disclosure risk domain.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-23406
Vulnerability details
Cursor is a code editor built for programming with AI. In versions 1.17 through 1.2, there is a UI information disclosure vulnerability in Cursor's MCP (Model Context Protocol) deeplink handler, allowing attackers to execute 2-click arbitrary system commands through social…
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engineering attacks. When users click malicious `cursor://anysphere.cursor-deeplink/mcp/install` links, the installation dialog does not show the arguments being passed to the command being run. If a user clicks a malicious deeplink, then examines the installation dialog and clicks through, the full command including the arguments will be executed on the machine. This is fixed in version 1.3.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Enterprise AI Assistants
- Risk Domain
- Privacy and Disclosure
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: ai, anysphere, mcp, model context protocol
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
The vulnerability enables arbitrary system command execution via malicious deeplinks (cursor://) that hide arguments in the UI, exploitable through social engineering; maps to client software exploitation (T1203), user execution of malicious links (T1204.001), and spearphishing links (T1566.002).
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.