CVE-2025-49150
Published: 11 June 2025
Summary
CVE-2025-49150 is a medium-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability. Its CVSS base score is 5.9 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exfiltration Over C2 Channel (T1041); ranked at the 41.5th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as Enterprise AI Assistants; in the LLM/Generative AI Risks risk domain; MITRE ATLAS techniques in scope: LLM Prompt Injection (AML.T0051).
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-18134
Vulnerability details
Cursor is a code editor built for programming with AI. Prior to 0.51.0, by default, the setting json.schemaDownload.enable was set to True. This means that by writing a JSON file, an attacker can trigger an arbitrary HTTP GET request that…
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does not require user confirmation. Since the Cursor Agent can edit JSON files, this means a malicious agent, for example, after a prompt injection attack already succeeded, could trigger a GET request to an attacker controlled URL, potentially exfiltrating other data the agent may have access to. This vulnerability is fixed in 0.51.0.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Enterprise AI Assistants
- Risk Domain
- LLM/Generative AI Risks
- OWASP Top 10 for LLMs 2025
- Classification Reason
- Matched keywords: ai, prompt injection
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
The vulnerability allows a compromised Cursor Agent to craft JSON files that trigger arbitrary HTTP GET requests to attacker-controlled URLs, enabling data exfiltration over a C2 channel by embedding sensitive data in the request.
MITRE ATLAS TechniquesAI
MITRE ATLAS techniques
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.