CVE-2012-0802 (GCVE-0-2012-0802)
Vulnerability from cvelistv5 – Published: 2012-06-19 20:00 – Updated: 2024-09-17 04:20
VLAI
EPSS
VEX
Summary
Multiple buffer overflows in Spamdyke before 4.3.0 might allow remote attackers to execute arbitrary code via vectors related to "serious errors in the usage of snprintf()/vsnprintf()" in which the return values may be larger than the size of the buffer.
Severity
No CVSS data available.
CWE
- n/a
Assigner
References
8 references
| URL | Tags |
|---|---|
| http://secunia.com/advisories/47548 | third-party-advisoryx_refsource_SECUNIA |
| http://www.openwall.com/lists/oss-security/2012/01/23/5 | mailing-listx_refsource_MLIST |
| http://www.securityfocus.com/bid/51440 | vdb-entryx_refsource_BID |
| http://www.osvdb.org/78351 | vdb-entryx_refsource_OSVDB |
| http://www.mail-archive.com/spamdyke-release%40sp… | mailing-listx_refsource_MLIST |
| http://secunia.com/advisories/48257 | third-party-advisoryx_refsource_SECUNIA |
| http://www.spamdyke.org/documentation/Changelog.txt | x_refsource_CONFIRM |
| http://security.gentoo.org/glsa/glsa-201203-01.xml | vendor-advisoryx_refsource_GENTOO |
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}
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.