[November 2025 Web Attack Trend Analysis]
1. Weekly Web Attack Trend Analysis
By analyzing weekly web attack trends, it is possible to identify specific periods during which web attacks
were heavily concentrated. Based on these insights, organizations can establish proactive prevention and
response strategies to prepare for periods of frequent attack activity.
The graph below visualizes the number of web attacks detected by AIWAF on a weekly basis throughout
November 2025.
An analysis of the data detected by AIWAF during November 2025 showed that an average of more than
370,000 web attacks were detected per day. This represents a notable increase compared to the previous
month, indicating that threats targeting web servers continue to grow in sophistication. In addition,
the frequency of attacks was higher on weekends (Saturday and Sunday) than on weekdays, which can be
interpreted as a strategic approach aimed at periods when web server usage is lower outside of regular
business hours.
In particular, November 14 recorded the highest concentration of web attacks during the entire period,
with SQL Injection accounting for the largest proportion of detected attack types on that day.
SQL Injection is a representative attack technique that manipulates databases to gain system privileges or
leak internal information. Attackers commonly use this method to bypass user authentication mechanisms
or to enumerate database structures, and due to these characteristics, organizations must exercise special
caution in protecting sensitive information.
In fact, AIWAF classifies SQL Injection as a high-risk attack type and maintains multiple detection patterns
for it. These findings highlight the need for continuous monitoring and precise response strategies for
major web attack types, including SQL Injection, and will serve as an important basis for establishing future
detection and blocking policies.
2. Web Attack Trends by Attack Type
By analyzing web attack trends by attack type based on detection logs, it is possible to systematically
identify which types of attacks occurred most frequently over the course of a month. Such analysis goes
beyond simple statistics and serves as a key foundation for establishing organizational security policies and
strengthening response frameworks.
An analysis of the detection logs collected by AIWAF during November 2025 revealed that a wide range
of web attack types were detected. Among them, certain attack categories showed clear patterns, such as
being heavily concentrated during specific periods or accounting for a large proportion of the total number
of attacks. In particular, classic yet still highly threatening attack types such as SQL Injection and Application
Vulnerabilities ranked among the most prevalent. These attacks tend to be carried out repeatedly, often
using automated attack tools or botnets.
The graph below visualizes the distribution of web attack types detected by AIWAF during November 2025.
According to statistics on web attack types detected by AIWAF during November 2025, SQL Injection
accounted for the largest share at 31.16% of total detections. This was followed by Application Vulnerability
(14.83%), Default Page (14.24%), System File Access (12.84%), and Bad User-Agent (8.31%). These results
indicate the need for more precise countermeasures and proactive preventive actions against specific
attack types.
First, SQL Injection is a highly critical attack type that consistently ranks near the top of the OWASP Top 10,
and its attack techniques continue to evolve in diverse ways. This attack typically occurs when values
provided through user input are incorporated directly into SQL queries and executed. Attackers exploit this
to perform abnormal authentication bypasses, enumerate database structures, and steal sensitive data.
In particular, systems that use dynamic queries or lack sufficient input validation are especially vulnerable
to such attacks.
The second most prevalent category, Application Vulnerability (14.83%), refers to attack types that target
inherent security flaws in applications, such as insufficient authentication, session management weaknesses,
and configuration errors. In particular, detections of authentication bypass attempts and API abuse have
been increasing in cloud-based SaaS systems and API-driven services, a trend that may influence the
formulation of future cloud security policies.
The third most prevalent category, Default Page (14.24%), targets pages that retain default settings after
installation or system message pages. During the reconnaissance phase, these pages may expose
information about the type and configuration state of system software, which can then be leveraged for
subsequent attacks. Although generally passive in nature, this attack type is often scanned at scale by
automated tools, making early detection and response essential.
The fourth most prevalent category, System File Access (12.84%), refers to attempts by attackers to exploit
vulnerabilities in web applications to gain unauthorized access to internal system files and directories or to
manipulate arbitrary files. Such attacks may arise from issues such as improper web server configuration,
insufficient access controls, or failures in directory path validation. If successful, they can lead to system
privilege escalation or the installation of backdoors.
The fifth most prevalent category, Bad User-Agent (8.31%), primarily targets requests that use malicious
or abnormal User-Agent strings to probe systems or to masquerade as automated scanners or crawlers.
During the reconnaissance phase, abnormal User-Agent strings can reveal information about the attacker’s
tools and scanning objectives (e.g., vulnerability scanning, crawling, scraping), providing clues for further
attacks. This type of traffic often combines detection evasion techniques such as impersonating legitimate
browsers or using known malicious strings with large-scale automation, making it difficult to regard as
purely passive. Because such traffic is frequently generated in high volumes by automated scanners, early
detection and blocking are critically important.
The Web Attack Trend Report provides the latest web vulnerability analyses, industry-specific attack patterns,
and key CVE-based vulnerability information, all based on processed data from the AI/ML-powered threat intelligence platform AILabs.
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