CNVD-2026-06088
Vulnerability from cnvd - Published: 2026-01-21
VLAI
Title
Tenda AX1806 sub_4CA50函数堆栈缓冲区溢出漏洞
Description
Tenda AX1806是中国腾达公司的一个WiFi6无线路由器。
Tenda AX1806存在堆栈缓冲区溢出漏洞,该漏洞源于sub_4CA50函数中security_5g参数未能正确验证输入数据的长度大小,攻击者可利用该漏洞导致拒绝服务。
Severity
高
Formal description
目前厂商尚未发布升级程序修复该安全问题,详情见厂商官网: https://www.tenda.com.cn/
Reference
https://github.com/0-fool/VulnbyCola/blob/main/Tenda/AX-1806/8/1.md
Impacted products
| Name | Tenda AX1806 v1.0.0.1 |
|---|
{
"cves": {
"cve": {
"cveNumber": "CVE-2025-70753",
"cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2025-70753"
}
},
"description": "Tenda AX1806\u662f\u4e2d\u56fd\u817e\u8fbe\u516c\u53f8\u7684\u4e00\u4e2aWiFi6\u65e0\u7ebf\u8def\u7531\u5668\u3002\n\nTenda AX1806\u5b58\u5728\u5806\u6808\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8esub_4CA50\u51fd\u6570\u4e2dsecurity_5g\u53c2\u6570\u672a\u80fd\u6b63\u786e\u9a8c\u8bc1\u8f93\u5165\u6570\u636e\u7684\u957f\u5ea6\u5927\u5c0f\uff0c\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u5bfc\u81f4\u62d2\u7edd\u670d\u52a1\u3002",
"formalWay": "\u76ee\u524d\u5382\u5546\u5c1a\u672a\u53d1\u5e03\u5347\u7ea7\u7a0b\u5e8f\u4fee\u590d\u8be5\u5b89\u5168\u95ee\u9898\uff0c\u8be6\u60c5\u89c1\u5382\u5546\u5b98\u7f51\uff1a\r\nhttps://www.tenda.com.cn/",
"isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
"number": "CNVD-2026-06088",
"openTime": "2026-01-21",
"products": {
"product": "Tenda AX1806 v1.0.0.1"
},
"referenceLink": "https://github.com/0-fool/VulnbyCola/blob/main/Tenda/AX-1806/8/1.md",
"serverity": "\u9ad8",
"submitTime": "2026-01-19",
"title": "Tenda AX1806 sub_4CA50\u51fd\u6570\u5806\u6808\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e"
}
Loading…
Loading…
Drag nodes to arrange them. Double-click a related vulnerability to open it, or a source to visit its link. The Physics button on the left tunes the layout and the simulation; the legend at the bottom left hides or shows nodes by kind and by sighting type.
Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
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.
Vulnerability timeline
Sightings and recorded dates, grouped by day · UTC
Loading timeline…
Loading…
Loading…
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.
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.