CVE-2025-49824
Published: 17 June 2025
Summary
CVE-2025-49824 is a low-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability. Its CVSS base score is 1.7 (Low).
Operationally, ranked at the 42.0th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-18563
Vulnerability details
conda-smithy is a tool for combining a conda recipe with configurations to build using freely hosted CI services into a single repository. Prior to version 3.47.1, the travis_encrypt_binstar_token implementation in the conda-smithy package has been identified as vulnerable to an…
more
Oracle Padding Attack. This vulnerability results from the use of an outdated and insecure padding scheme during RSA encryption. A malicious actor with access to an oracle system can exploit this flaw by iteratively submitting modified ciphertexts and analyzing responses to infer the plaintext without possessing the private key. This issue has been patched in version 3.47.1.
- CWE(s)
Related Threats
No named actor attribution yet. ATT&CK technique mapping in progress for this CVE.
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.