GCVE Workshop - 22 September 2026 (14:00-18:00), Luxembourg Before The Vulnopticon Conference - Registration

CNVD-2017-26764

Vulnerability from cnvd - Published: 2017-09-14
VLAI
Title
Apple Bluetooth LEAP and Apple TV堆缓冲区溢出漏洞
Description
低能量音频协议Low-Energy Audio Protocol (LEAP),该协议旨在将音频流传输到低能量音频外设(如低能量耳机或Siri Remote),Apple TV是苹果公司推出的一款高清电视机顶盒产品,用户可以通过Apple TV在线收看电视节目 Apple Bluetooth Low-Energy Audio Protocol (LEAP) iOS版本9.3.5及更低版本,及Apple TV tvOS 7.2.2及更低版本中存在堆缓冲区溢出漏洞,由于通过LEAP发送的音频命令未被正确验证,可以将大型音频命令发送到目标设备,允许攻击者使用内存破坏来完全控制该设备。
Severity
Formal description

厂商尚未提供漏洞修补方案,请关注厂商主页及时更新: https://www.apple.com/

Reference
https://www.kb.cert.org/vuls/id/240311 https://www.armis.com/blueborne/#/technical
Impacted products
Name
['Apple Bluetooth Low-Energy Audio Protocol iOS <=9.3.5', 'Apple tvOS <=7.2.2']
Show details on source website

{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2017-14315"
    }
  },
  "description": "\u4f4e\u80fd\u91cf\u97f3\u9891\u534f\u8baeLow-Energy Audio Protocol (LEAP)\uff0c\u8be5\u534f\u8bae\u65e8\u5728\u5c06\u97f3\u9891\u6d41\u4f20\u8f93\u5230\u4f4e\u80fd\u91cf\u97f3\u9891\u5916\u8bbe\uff08\u5982\u4f4e\u80fd\u91cf\u8033\u673a\u6216Siri Remote\uff09\uff0cApple TV\u662f\u82f9\u679c\u516c\u53f8\u63a8\u51fa\u7684\u4e00\u6b3e\u9ad8\u6e05\u7535\u89c6\u673a\u9876\u76d2\u4ea7\u54c1\uff0c\u7528\u6237\u53ef\u4ee5\u901a\u8fc7Apple TV\u5728\u7ebf\u6536\u770b\u7535\u89c6\u8282\u76ee\r\n\r\nApple Bluetooth Low-Energy Audio Protocol (LEAP) iOS\u7248\u672c9.3.5\u53ca\u66f4\u4f4e\u7248\u672c\uff0c\u53caApple TV tvOS 7.2.2\u53ca\u66f4\u4f4e\u7248\u672c\u4e2d\u5b58\u5728\u5806\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\uff0c\u7531\u4e8e\u901a\u8fc7LEAP\u53d1\u9001\u7684\u97f3\u9891\u547d\u4ee4\u672a\u88ab\u6b63\u786e\u9a8c\u8bc1\uff0c\u53ef\u4ee5\u5c06\u5927\u578b\u97f3\u9891\u547d\u4ee4\u53d1\u9001\u5230\u76ee\u6807\u8bbe\u5907\uff0c\u5141\u8bb8\u653b\u51fb\u8005\u4f7f\u7528\u5185\u5b58\u7834\u574f\u6765\u5b8c\u5168\u63a7\u5236\u8be5\u8bbe\u5907\u3002",
  "discovererName": "Ben Seri and Gregory Vishnepolsky of Armis",
  "formalWay": "\u5382\u5546\u5c1a\u672a\u63d0\u4f9b\u6f0f\u6d1e\u4fee\u8865\u65b9\u6848\uff0c\u8bf7\u5173\u6ce8\u5382\u5546\u4e3b\u9875\u53ca\u65f6\u66f4\u65b0\uff1a\r\nhttps://www.apple.com/",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2017-26764",
  "openTime": "2017-09-14",
  "products": {
    "product": [
      "Apple Bluetooth Low-Energy Audio Protocol iOS \u003c=9.3.5",
      "Apple tvOS \u003c=7.2.2"
    ]
  },
  "referenceLink": "https://www.kb.cert.org/vuls/id/240311\r\nhttps://www.armis.com/blueborne/#/technical",
  "serverity": "\u9ad8",
  "submitTime": "2017-09-13",
  "title": "Apple Bluetooth LEAP and Apple TV\u5806\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e"
}



Log in or create an account to share your comment.




Tags
Taxonomy of the tags.


Loading…

Loading…

Loading…

Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.

Sightings

Author Source Type Date Other

Nomenclature

  • Seen: The vulnerability was mentioned, discussed, or observed by the user.
  • Confirmed: The vulnerability has been validated from an analyst's perspective.
  • Published Proof of Concept: A public proof of concept is available for this vulnerability.
  • Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
  • Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
  • Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
  • Not confirmed: The user expressed doubt about the validity of the vulnerability.
  • Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.

Loading…

Detection rules are retrieved from Rulezet.

Loading…

Loading…

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.


Loading…