Vulnerability-Lookup

CNVD-2022-88238

Vulnerability from cnvd - Published: 2022-12-17

Title

Caryll OTFCC otfccdump+0x6babea拒绝服务漏洞

Description

Caryll OTFCC是Caryll开源的一个C库和实用程序。用于解析和编写OpenType字体文件。 Caryll OTFCC存在拒绝服务漏洞，该漏洞源于/release-x64/otfccdump+0x6babea处存在分段冲突。攻击者可利用漏洞导致程序崩溃。

Severity

高

Formal description

厂商尚未提供漏洞修复方案，请关注厂商主页更新： https://github.com/caryll/otfcc

Reference

https://drive.google.com/file/d/1x0eGL9tYqc7BEapsTqD78FTu9H8GKZ6l/view?usp=sharing

Impacted products

Name
Caryll otfcc

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JSON

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{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2022-35029",
      "cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2022-35029"
    }
  },
  "description": "Caryll OTFCC\u662fCaryll\u5f00\u6e90\u7684\u4e00\u4e2aC\u5e93\u548c\u5b9e\u7528\u7a0b\u5e8f\u3002\u7528\u4e8e\u89e3\u6790\u548c\u7f16\u5199OpenType\u5b57\u4f53\u6587\u4ef6\u3002\n\nCaryll OTFCC\u5b58\u5728\u62d2\u7edd\u670d\u52a1\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8e/release-x64/otfccdump+0x6babea\u5904\u5b58\u5728\u5206\u6bb5\u51b2\u7a81\u3002\u653b\u51fb\u8005\u53ef\u5229\u7528\u6f0f\u6d1e\u5bfc\u81f4\u7a0b\u5e8f\u5d29\u6e83\u3002",
  "formalWay": "\u5382\u5546\u5c1a\u672a\u63d0\u4f9b\u6f0f\u6d1e\u4fee\u590d\u65b9\u6848\uff0c\u8bf7\u5173\u6ce8\u5382\u5546\u4e3b\u9875\u66f4\u65b0\uff1a\r\nhttps://github.com/caryll/otfcc",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2022-88238",
  "openTime": "2022-12-17",
  "products": {
    "product": "Caryll otfcc"
  },
  "referenceLink": "https://drive.google.com/file/d/1x0eGL9tYqc7BEapsTqD78FTu9H8GKZ6l/view?usp=sharing",
  "serverity": "\u9ad8",
  "submitTime": "2022-09-28",
  "title": "Caryll OTFCC otfccdump+0x6babea\u62d2\u7edd\u670d\u52a1\u6f0f\u6d1e"
}

CVE-2022-35029 (GCVE-0-2022-35029)

Vulnerability from cvelistv5 – Published: 2022-09-22 16:54 – Updated: 2025-05-27 15:27

Summary

OTFCC commit 617837b was discovered to contain a segmentation violation via /release-x64/otfccdump+0x6babea.

Severity

6.5 (Medium)


                        
                          CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:H

SSVC

Exploitation: none Automatable: no Technical Impact: partial

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2025-05-27 15:27 UTC

CWE

n/a
CWE-787 - Out-of-bounds Write

Assigner

mitre mitre root

References

2 references

URL	Tags
https://drive.google.com/file/d/1x0eGL9tYqc7BEaps…	x_refsource_MISC
https://github.com/Cvjark/Poc/blob/main/otfcc/CVE…	x_refsource_MISC

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JSON

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            "url": "https://github.com/Cvjark/Poc/blob/main/otfcc/CVE-2022-35029.md"
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        ],
        "title": "CVE Program Container"
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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.

Detection rules are retrieved from Rulezet.

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.

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.