
ABUSE.MOM — 规矩点,否则你将被曝光
| 签名 | 描述 | 分数 | 严重性 |
|---|---|---|---|
| Danger medium hits: 2 | 中等风险:管理面板、配置文件 | +20 | |
| 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 103.153.79.173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。
来自同一/24子网的其他被封锁IP——表明该网络范围存在系统性滥用。
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 |
| 2021 | Unknown | Low | Service on port 2021 |
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。
103.153.79.173 has been assigned a threat score of 65/100 (High). 这将其归类为高严重性威胁。建议对敏感基础设施进行主动封锁。
The following attack categories were identified:
IP地址103.153.79.173已追溯至Láng Thượng, Vietnam,运营在HN-VIETSERVER的网络中。我们的威胁检测系统根据观察到的恶意行为模式标记了此地址。 在1天的时间内,此IP产生了1次恶意请求,平均每天约1次请求。 该IP从数据中心基础设施运营,是有组织攻击行动中使用的典型地址。 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Vietnam的190个恶意IP,使其成为全球威胁活动的重要贡献者。 评分65/100需要主动监控和速率限制。建议对敏感系统进行完全封锁。
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.
Credential stuffing uses stolen username-password pairs from data breaches to attempt logins across many websites. Since users frequently reuse passwords, these automated attacks achieve success rates of 0.1-2%, which translates to thousands of compromised accounts from millions of attempts.
WAFs inspect HTTP traffic to block common attacks but require careful tuning. Overly aggressive rules cause false positives while permissive configurations miss attacks. Modern WAFs combine signature matching with behavioral analysis and machine learning.