
ABUSE.MOM — 规矩点,否则你将被曝光
| 签名 | 描述 | 分数 | 严重性 |
|---|---|---|---|
| Danger medium hits: 3 | 中等风险:管理面板、配置文件 | +30 | |
| 404 ratio 40-60% | 大多数请求返回404——目录枚举 | +15 | |
| Probe pattern 302->404 same path | 自动分析检测到行为异常 | +20 | |
| Foreign referer seen | 来自无关外部域名的Referer | +10 |
从服务器访问日志重建的HTTP请求。出于安全考虑,目标域名已隐藏。
* Typical request patterns for detected signatures. Actual target domains are redacted.
IP 65.108.159.129正在枚举目录。在10次以上404错误后配置fail2ban apache-404 jail。禁用目录列表。
来自Shodan的网络侦察数据。开放端口可能表示正在运行的服务、错误配置或潜在的攻击面。
| Port | Service | Risk | Description |
|---|---|---|---|
| 22 | SSH | Low | Secure Shell — common brute force target for remote access |
| 1080 | Unknown | Low | Service on port 1080 |
| 8080 | HTTP-Alt | Low | HTTP alternative port — often used for admin panels or proxies |
| CVE ID | Link |
|---|---|
| CVE-2024-37894 | NVD → |
| CVE-2025-62168 | NVD → |
| CVE-2024-25111 | NVD → |
| CVE-2024-45802 | NVD → |
| CVE-2025-59362 | NVD → |
🔴 此主机有5个已知CVE与其暴露的服务相关联。多个漏洞表明补丁管理存在漏洞。 请在NVD数据库中查看每个CVE的详细信息。
数据来源:Shodan InternetDB。独立于abuse.mom进行扫描。
该IP已通过全球邮件服务器和防火墙使用的主要DNS黑名单进行检查。
已检查:Spamhaus、SpamCop、Barracuda、SORBS、CBL、UCEProtect。
65.108.159.129 has been assigned a threat score of 75/100 (High). 此分数表明高威胁严重性。该IP显示出明确的恶意行为模式,需要立即采取防御措施。
The following attack categories were identified:
我们的监控基础设施已将65.108.159.129(地理位置为Helsinki, Finland,运营在Hetzner Online GmbH的网络中)识别为可疑网络活动的来源。 我们的传感器在1天内捕获了来自此地址的1次恶意请求,反映出每天约1次的持续攻击节奏。 该IP被归类为托管/数据中心基础设施,通常与用于自动化攻击活动、僵尸网络命令控制或大规模漏洞扫描的租用服务器相关联。 该IP表现出目录枚举行为,系统地请求不存在的路径以发现隐藏文件和配置错误的资源。 我们的记录显示来自Finland的101个恶意IP,使其成为全球威胁活动的重要贡献者。 威胁评分75/100将此IP置于高风险类别。建议在防火墙级别进行封锁。
This IP belongs to a hosting or data center provider. Malicious traffic from hosting infrastructure often originates from compromised VPS instances, rented servers used for scanning campaigns, or abused free-tier cloud accounts. Hosting providers typically respond to abuse reports within 24-72 hours.
Path traversal attacks attempt to access files outside the intended directory by manipulating file path references. Attackers use sequences like ../ to reach sensitive system files such as /etc/passwd or application configuration files.
Machine learning models analyze vast amounts of network traffic to identify attack patterns invisible to rule-based systems. Supervised models classify known attack types while unsupervised models detect anomalies that may indicate novel threats.