[January 2026 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. These insights can be used to establish proactive prevention and response
strategies in preparation for periods of increased attack activity.
The graph below visualizes the number of web attacks detected by AIWAF on a weekly basis throughout
December 2025.
An analysis of the data detected by AIWAF during December 2025 showed that an average of more than
370,000 web attacks were detected per day. This level is similar to that of the previous month, indicating
that threats targeting web servers continue to grow in sophistication. In addition, attack activity was
observed to be higher on weekends (Saturday and Sunday) than on weekdays, which can be interpreted as
a strategic approach aimed at periods when web server usage tends to be lower outside of regular business
hours.
In particular, December 14 recorded the highest concentration of web attacks during the month, 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 exfiltrate 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 heightened
caution in protecting sensitive data.
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 foundation for establishing
future detection and mitigation 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 over the course of December 2025 revealed a wide
range of web attack types. Among them, certain categories exhibited clear patterns, such as being heavily
concentrated during specific periods or accounting for a significant 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 in December 2025.
According to statistics on web attack types detected by AIWAF during December 2025, SQL Injection
accounted for the largest share at 33.93% of total detections. This was followed by Application Vulnerability
(12.78%), Default Page (12.29%), System File Access (12.07%), and Bad User-Agent (9.7%). These results
highlight 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 techniques continue to evolve in diverse ways. This attack typically occurs when values supplied
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.
Systems that rely on dynamic queries or lack sufficient input validation are particularly vulnerable to such
attacks.
The second most prevalent category, Application Vulnerability (12.78%), refers to attacks that target
inherent security flaws within applications, such as insufficient authentication, weak session management,
or configuration errors. Notably, 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
development of future cloud security policies.
The third most prevalent category, Default Page (12.29%), 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 of system software, which can be leveraged for subsequent
attacks. Although generally passive in nature, this attack type is frequently scanned at scale by automated
tools, making early detection and response essential.
The fourth most prevalent category, System File Access (12.07%), 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 result from improper web server configurations, insufficient
access controls, or failures in directory path validation. If successful, they can lead to privilege escalation or
the installation of backdoors.
The fifth most prevalent category, Bad User-Agent (9.7%), primarily targets requests that use malicious or
abnormal User-Agent strings to probe systems or masquerade as automated scanners or crawlers. During
the reconnaissance phase, abnormal User-Agent strings can reveal information about an attacker’s tools and
scanning objectives such as vulnerability scanning, crawling, or scraping providing clues for follow-on
attacks. This traffic often combines detection evasion techniques (e.g., 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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