CNVD-2025-12335
Vulnerability from cnvd - Published: 2025-06-10
VLAI
Title
Adobe After Effects越界写入漏洞(CNVD-2025-12335)
Description
Adobe After Effects是一款用于创建动态图形、视觉效果和动态影像的专业软件。
Adobe After Effects中存在越界写入漏洞。该漏洞源于程序未能正确处理内存边界,导致在分配的内存缓冲区之外写入数据。攻击者可以利用该漏洞在当前用户的上下文中执行任意代码。
Severity
高
Patch Name
Adobe After Effects越界写入漏洞(CNVD-2025-12335)的补丁
Patch Description
Adobe After Effects是一款用于创建动态图形、视觉效果和动态影像的专业软件。
Adobe After Effects中存在越界写入漏洞。该漏洞源于程序未能正确处理内存边界,导致在分配的内存缓冲区之外写入数据。攻击者可以利用该漏洞在当前用户的上下文中执行任意代码。目前,供应商发布了安全公告及相关补丁信息,修复了此漏洞。
Formal description
厂商已提供漏洞修复方案,请关注厂商主页更新: https://helpx.adobe.com/security/products/after_effects/apsb25-23.html
Reference
https://nvd.nist.gov/vuln/detail/CVE-2025-27183
Impacted products
| Name | ['Adobe Adobe After Effects <=24.6.4', 'Adobe Adobe After Effects 25.1'] |
|---|
{
"cves": {
"cve": {
"cveNumber": "CVE-2025-27183",
"cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2025-27183"
}
},
"description": "Adobe After Effects\u662f\u4e00\u6b3e\u7528\u4e8e\u521b\u5efa\u52a8\u6001\u56fe\u5f62\u3001\u89c6\u89c9\u6548\u679c\u548c\u52a8\u6001\u5f71\u50cf\u7684\u4e13\u4e1a\u8f6f\u4ef6\u3002\n\nAdobe After Effects\u4e2d\u5b58\u5728\u8d8a\u754c\u5199\u5165\u6f0f\u6d1e\u3002\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u7a0b\u5e8f\u672a\u80fd\u6b63\u786e\u5904\u7406\u5185\u5b58\u8fb9\u754c\uff0c\u5bfc\u81f4\u5728\u5206\u914d\u7684\u5185\u5b58\u7f13\u51b2\u533a\u4e4b\u5916\u5199\u5165\u6570\u636e\u3002\u653b\u51fb\u8005\u53ef\u4ee5\u5229\u7528\u8be5\u6f0f\u6d1e\u5728\u5f53\u524d\u7528\u6237\u7684\u4e0a\u4e0b\u6587\u4e2d\u6267\u884c\u4efb\u610f\u4ee3\u7801\u3002",
"formalWay": "\u5382\u5546\u5df2\u63d0\u4f9b\u6f0f\u6d1e\u4fee\u590d\u65b9\u6848\uff0c\u8bf7\u5173\u6ce8\u5382\u5546\u4e3b\u9875\u66f4\u65b0\uff1a\r\nhttps://helpx.adobe.com/security/products/after_effects/apsb25-23.html",
"isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
"number": "CNVD-2025-12335",
"openTime": "2025-06-10",
"patchDescription": "Adobe After Effects\u662f\u4e00\u6b3e\u7528\u4e8e\u521b\u5efa\u52a8\u6001\u56fe\u5f62\u3001\u89c6\u89c9\u6548\u679c\u548c\u52a8\u6001\u5f71\u50cf\u7684\u4e13\u4e1a\u8f6f\u4ef6\u3002\r\n\r\nAdobe After Effects\u4e2d\u5b58\u5728\u8d8a\u754c\u5199\u5165\u6f0f\u6d1e\u3002\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u7a0b\u5e8f\u672a\u80fd\u6b63\u786e\u5904\u7406\u5185\u5b58\u8fb9\u754c\uff0c\u5bfc\u81f4\u5728\u5206\u914d\u7684\u5185\u5b58\u7f13\u51b2\u533a\u4e4b\u5916\u5199\u5165\u6570\u636e\u3002\u653b\u51fb\u8005\u53ef\u4ee5\u5229\u7528\u8be5\u6f0f\u6d1e\u5728\u5f53\u524d\u7528\u6237\u7684\u4e0a\u4e0b\u6587\u4e2d\u6267\u884c\u4efb\u610f\u4ee3\u7801\u3002\u76ee\u524d\uff0c\u4f9b\u5e94\u5546\u53d1\u5e03\u4e86\u5b89\u5168\u516c\u544a\u53ca\u76f8\u5173\u8865\u4e01\u4fe1\u606f\uff0c\u4fee\u590d\u4e86\u6b64\u6f0f\u6d1e\u3002",
"patchName": "Adobe After Effects\u8d8a\u754c\u5199\u5165\u6f0f\u6d1e\uff08CNVD-2025-12335\uff09\u7684\u8865\u4e01",
"products": {
"product": [
"Adobe Adobe After Effects \u003c=24.6.4",
"Adobe Adobe After Effects 25.1"
]
},
"referenceLink": "https://nvd.nist.gov/vuln/detail/CVE-2025-27183",
"serverity": "\u9ad8",
"submitTime": "2025-04-11",
"title": "Adobe After Effects\u8d8a\u754c\u5199\u5165\u6f0f\u6d1e\uff08CNVD-2025-12335\uff09"
}
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Nomenclature
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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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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.