The Imperva Bad Bot Report 2025, officially titled "How AI is Supercharging the Bot Threat," represents a watershed moment in cybersecurity threat analysis. Released in late April 2025, this comprehensive study draws from an unprecedented dataset collected throughout 2024, analyzing the blocking of 13 trillion bad bot requests across thousands of domains and industries worldwide . The report's central finding—that malicious bots now account for 37% of all internet traffic, up from 32% in 2023—signals an accelerating crisis in automated cyber threats .
This escalation is fundamentally driven by artificial intelligence technologies that have democratized sophisticated cyberattack capabilities previously reserved for well-resourced threat actors. The 2025 report documents how AI-powered tools and services have created an ecosystem where even novice attackers can deploy highly evasive, adaptive, and damaging bot campaigns at scale 4|PDF.
The financial implications are staggering. Organizations across all sectors face mounting infrastructure costs, revenue losses from fraud and competitive intelligence theft, and escalating defensive expenditures. Financial services, e-commerce, gambling, gaming, automotive, and travel sectors emerge as the most heavily targeted industries, each facing unique attack vectors tailored to their specific vulnerabilities .
This report synthesizes the primary findings, statistical highlights, methodological approaches, industry-specific impacts, and strategic recommendations presented in the Imperva Bad Bot Report 2025, contextualizing these findings within the broader evolution of automated threats and the emerging AI-driven threat landscape.
Imperva's Bad Bot Report series has established itself as the definitive annual benchmark for understanding automated threat landscapes. Since its inception, the report has documented the inexorable rise of bot traffic across the internet, transforming from a niche technical concern into a mainstream business imperative. The 2025 edition represents the continuation of over a decade of meticulous data collection, analysis, and threat intelligence synthesis 37|PDF.
The historical trajectory reveals a disturbing trend. In 2019, bad bots accounted for approximately 24.1% of web traffic 8|PDF. By 2023, this figure had climbed to 32% of overall internet traffic . The 2025 report's finding of 37% represents a 5 percentage point increase in just two years—a rate of acceleration that correlates directly with the proliferation of AI-powered automation tools .
The Imperva Bad Bot Report 2025 employs a multi-layered analytical approach, processing data collected from Imperva's global network of security services. The primary dataset encompasses the blocking of 13 trillion bad bot requests—a figure that dwarfs previous years' datasets and provides unprecedented statistical confidence in the reported trends .
This dataset draws from thousands of domains and industries, spanning geographic regions and organizational sizes. The breadth of this collection enables granular analysis of attack patterns, industry-specific vulnerabilities, geographic threat distributions, and temporal trends .
The report maintains Imperva's established taxonomy for bot classification:
Good Bots include legitimate automated agents such as search engine crawlers, site monitoring tools, copyright scanners, and other authorized automation that supports internet functionality and business operations.
Bad Bots encompass any automated software designed to perform malicious activities. These include:
The 2025 report introduces a critical new dimension to this taxonomy: AI-Enhanced Bad Bots, which leverage large language models (LLMs) and generative AI to create more sophisticated, evasive, and adaptive attack campaigns .
The 2025 report's headline finding confirms that bots have definitively surpassed human traffic as the dominant force on the internet. In 2024, automated bots accounted for 51% of all web traffic, marking the first time in internet history that non-human traffic exceeded human-originated requests .
Within this automated traffic ecosystem, the distribution between good and bad bots reveals the true nature of the threat:
This represents a year-over-year increase of 5 percentage points in malicious bot traffic—a statistically significant acceleration that the report attributes primarily to the proliferation of AI-powered automation tools .
The absolute scale of malicious automation is reflected in the 13 trillion bad bot requests blocked during the 2024 data collection period . This figure represents:
This exponential growth in attack volume correlates with the emergence of "Bad Bots as a Service" platforms that have lowered barriers to entry for malicious actors while simultaneously increasing attack sophistication 4|PDF8|PDF.
Perhaps the most consequential finding in the 2025 report is the quantification of AI-driven bot threats. The report identifies that AI-powered bots now account for a substantial portion of malicious traffic, with specific AI crawlers and bots emerging as significant threat actors .
Leading AI Bots in Network Attacks:
The report documents that advanced AI tools are being weaponized for network attacks:
This represents a paradigm shift in the threat landscape. Where previous bot campaigns required specialized programming skills, AI-powered tools now enable natural language programming of sophisticated attack sequences, dramatically lowering the technical barrier to entry 4|PDF.
The 2025 report categorizes bad bots by their sophistication levels, revealing an evolution toward more evasive technologies:
Advanced Sophisticated Bots:
These bots employ comprehensive evasion techniques, including:
The report documents a marked increase in advanced sophisticated bots, which now represent a larger proportion of malicious traffic than in previous years 8|PDF10|PDF. This trend directly correlates with the availability of AI tools that can generate more human-like interaction patterns and dynamically adapt to security countermeasures.
Moderate Sophistication Bots:
These bots utilize some evasion techniques but lack the comprehensive capabilities of advanced variants. They remain detectable through careful behavioral analysis but pose increasing challenges to rule-based detection systems.
Simple Bots:
While still present, simple bots that use basic automation techniques and lack sophisticated evasion mechanisms represent a decreasing proportion of malicious traffic. The report suggests this decline reflects both improved baseline security measures that easily thwart simple bots and the ready availability of more sophisticated attack tools.
The 2025 report provides insights into the geographic distribution of bot traffic origin and targeting, continuing trends documented in previous editions:
Primary Origin Countries for Bad Bot Traffic:
The report documents that certain countries continue to serve as primary sources of malicious bot traffic, with concentrations correlating to:
Primary Target Regions:
Developed markets with high-value digital economies remain primary targets:
The report notes that geographic distribution patterns are becoming increasingly obscured through the use of distributed proxy networks and residential proxy services, making attribution more challenging 10|PDF.
The 2025 Imperva Bad Bot Report's subtitle—"How AI is Supercharging the Bot Threat"—encapsulates what may be the most significant development in automated cyber threats since the emergence of botnets. The report documents how artificial intelligence has fundamentally transformed the bot threat landscape through multiple vectors 4|PDF.
Democratization of Sophistication:
Previously, highly sophisticated bot attacks required significant programming expertise and resources. AI tools have democratized these capabilities, enabling actors with minimal technical skills to deploy advanced campaigns. Large language models can generate functional bot code from natural language descriptions, creating attack scripts that would have previously required skilled development .
Enhanced Evasion Capabilities:
AI-powered bots demonstrate superior evasion characteristics compared to traditional automation:
Scale and Speed:
AI automation enables unprecedented attack velocity. Where human-managed bot campaigns required time-consuming optimization, AI-driven systems can conduct thousands of parallel experiments to optimize attack parameters, dramatically accelerating the pace of threat evolution .
The 2025 report documents the emergence of a sophisticated ecosystem around AI-powered bots:
AI Crawlers as Threat Vectors:
The identification of ByteSpider Bot as responsible for 54% of all AI-driven attacks represents a significant finding . This crawler, associated with ByteDance (the parent company of TikTok), exemplifies how legitimate AI training operations can overlap with malicious activities:
AI-Generated Attack Scripts:
The report documents instances where AI-generated code has been used to create attack scripts, including:
Bad Bots as a Service (BaaS) Integration:
The previously documented trend toward BaaS platforms has accelerated with AI integration 4|PDF8|PDF. These services now incorporate AI capabilities, offering:
The 2025 report provides detailed analysis of specific AI bots identified as threat vectors:
ByteSpider Bot:
As the dominant AI-driven threat (54% of AI-driven attacks), ByteSpider warrants particular attention . The report documents:
AppleBot:
The report notes increasing activity from AppleBot in contexts that suggest potential security implications:
ClaudeBot:
Anthropic's ClaudeBot is documented in several contexts:
ChatGPT and Generative AI:
The report documents the weaponization of ChatGPT and similar LLMs for:
Google Gemini and Perplexity AI:
These AI tools are documented in:
The AI-driven threat evolution documented in the 2025 report has profound implications for defensive strategies:
Traditional Rule-Based Detection Limitations:
Rule-based detection systems that identify bots based on known signatures, behavioral patterns, and technical fingerprints face obsolescence against AI-driven threats that can:
Need for AI-Powered Defense:
The report implies that effective defense against AI-powered threats requires corresponding AI-powered detection capabilities:
Continuous Adaptation Requirements:
The report emphasizes that static defensive measures are increasingly ineffective. Organizations must implement continuous monitoring and adaptation capabilities to respond to rapidly evolving AI-driven threats 40|PDF41|PDF.
The financial services sector emerges from the 2025 report as one of the most heavily targeted industries for malicious bot activity. The convergence of high-value assets, sensitive personal data, and critical infrastructure makes financial services an attractive target for multiple categories of threat actors .
Account Takeover (ATO) Attacks:
The report documents that financial services experience a disproportionately high volume of account takeover attacks, with account takeover accounting for 22% of attacks targeting the sector . These attacks include:
API Security Challenges:
Financial services APIs represent critical attack vectors. The report identifies APIs as a primary target for malicious bots, with financial services among the most targeted industries for API-based attacks :
Attack Frequency and Impact:
According to related cybersecurity research cited in the report, the financial sector was the most frequent target of attackers, accounting for 40% of attacks in the first half of 2025 16|PDF. Financial services represent nearly 25% of all recorded attempts and incidents with high severity 17|PDF.
Specific Attack Types Documented:
The report documents several attack categories prevalent in financial services:
Infrastructure Strain:
Beyond direct financial losses, financial institutions face significant infrastructure costs from bot traffic:
The e-commerce and retail sector faces distinct bot threats aligned with its unique business model and revenue streams. The 2025 report documents that the e-commerce industry accounts for 20.5% of cyber threats, making it a high-risk area for various attack types .
Overall Attack Prevalence:
Related data indicates that e-commerce is highly attacked, accounting for 32.4% of attacks in some analyses . This high attack volume reflects the sector's:
Scalping Attacks:
The report documents the continued prevalence of scalping bots targeting:
Price Scraping and Competitive Intelligence:
E-commerce platforms face sophisticated scraping operations designed for:
Account-Related Attacks:
Similar to financial services, e-commerce platforms face account-focused attacks:
API Vulnerabilities:
The retail sector's heavy reliance on APIs for:
creates extensive attack surfaces that the report documents as increasingly targeted by sophisticated bot operations .
The 2025 report identifies gambling and gaming as sectors with the highest prevalence of bad bots relative to legitimate traffic . This finding aligns with the unique characteristics of these industries:
High-Value Targets:
Attack Categories Documented:
Unique Challenges:
The real-time nature of gambling and gaming creates unique defensive challenges:
The automotive industry's emergence as a heavily targeted sector reflects its digital transformation:
Attack Vectors:
Industry-Specific Factors:
The travel sector rounds out the industries identified as most affected by bad bots in the 2025 report :
Attack Categories:
Business Impact:
The travel industry's reliance on real-time pricing and availability systems creates particular vulnerability to bot-driven:
Account takeover attacks represent one of the most damaging bot attack categories documented in the 2025 report. These attacks target user accounts across industries, with particularly severe impacts in financial services, e-commerce, and technology platforms.
Attack Mechanics:
The report documents several ATO methodologies:
Credential Stuffing: Using credentials exposed in previous data breaches, bots systematically test username/password combinations across multiple platforms. This approach exploits the widespread practice of password reuse across services.
Brute Force Attacks: For targeted accounts where credentials are not available from breaches, sophisticated bots systematically attempt password combinations. AI-powered attacks optimize this process by:
Session Hijacking: Exploiting vulnerabilities in session management to assume control of authenticated sessions.
Password Reset Manipulation: Exploiting weaknesses in password recovery mechanisms to gain unauthorized access.
Prevalence in Financial Services:
The report documents that account takeover attacks account for 22% of attacks targeting the financial services industry . This high prevalence reflects:
Impact Analysis:
Beyond immediate financial losses, organizations affected by ATO attacks face:
The 2025 report identifies APIs as a primary attack vector for malicious bots, with API security representing a critical defensive priority .
API Attack Prevalence:
The report documents that APIs are increasingly targeted because:
Attack Categories:
Industry Impact:
Financial services, e-commerce, and technology sectors face the highest volume of API-based attacks. The report documents that organizations often underestimate their API attack surface, leading to inadequate defensive measures .
Web scraping remains a fundamental bot attack category documented in the 2025 report, with sophisticated scraping operations targeting:
Data Categories Targeted:
Sophistication Evolution:
The report documents the evolution of scraping operations:
Business Impact:
Organizations affected by scraping operations face:
The 2025 report documents the continued prevalence and evolution of scalping attacks, particularly affecting:
Target Industries:
Attack Evolution:
Scalping operations have evolved with:
Secondary Market Ecosystem:
The report documents the ecosystem around scalped goods:
While distributed denial of service (DDoS) attacks differ from other bot attack categories in their objectives, the 2025 report documents their continued relevance in the bot threat landscape:
Attack Characteristics:
Bot Role in DDoS:
Bots serve multiple roles in DDoS operations:
The report documents the persistence of ad fraud as a significant bot-driven crime category:
Fraud Types:
AI Enhancement:
AI has significantly enhanced ad fraud capabilities:
The 2025 report, while primarily focused on threat analysis, provides insights into the detection methodologies employed by Imperva. These methodologies have evolved significantly to address the AI-enhanced threat landscape 40|PDF41|PDF42|PDF.
Multi-Layered Detection Approach:
Imperva employs a multi-layered detection approach combining multiple techniques:
Signature-Based Detection:
While traditional signature-based detection remains relevant for known bot patterns, the report acknowledges its limitations against sophisticated, evolving threats. Signature databases are continuously updated based on:
Behavioral Analysis:
The core of modern bot detection lies in behavioral analysis, which examines:
Machine Learning Models:
The report documents Imperva's use of ensemble machine learning models for bot identification 41|PDF71|PDF. These models incorporate:
Browser Verification:
Technical verification of browser characteristics to identify:
Behavioral fingerprinting represents a sophisticated detection approach that analyzes patterns of interaction to distinguish bots from human users:
Interaction Analysis:
Temporal Analysis:
Consistency Verification:
The 2025 report emphasizes the necessity of AI-powered detection to counter AI-powered threats:
Detection AI Capabilities:
Challenges in AI Detection:
The report acknowledges challenges in deploying AI-based detection:
While the report does not provide explicit comparison of specific algorithms between 2023 and 2025 editions, it documents the broader evolution in detection approaches:
From Rules to Models:
The shift from static rule-based detection to dynamic ML-based analysis represents a fundamental evolution in approach:
Static Rules (Traditional):
Dynamic Models (Current):
Integration Challenges:
The report documents challenges organizations face in implementing effective detection:
The 2025 report documents the maturation of "Bad Bots as a Service" platforms that have transformed the economics and accessibility of bot-based attacks 4|PDF8|PDF.
Service Model Characteristics:
Modern BaaS platforms operate with business models similar to legitimate SaaS offerings:
Democratization of Attack Capabilities:
BaaS platforms have dramatically lowered barriers to entry:
The report documents several categories of BaaS offerings:
Account Takeover Services:
Scraping Services:
Inventory Manipulation Services:
Ad Fraud Services:
The 2025 report documents AI integration throughout the BaaS ecosystem:
AI-Enhanced Features:
Performance Optimization:
AI systems within BaaS platforms optimize:
While the 2025 report focuses on threat analysis, it implies foundational security measures that organizations should implement:
Access Point Protection:
Organizations must secure all access points that bots might target:
Traffic Monitoring:
Continuous monitoring of traffic patterns enables early detection of bot activity:
Infrastructure Hardening:
Basic infrastructure hardening remains essential:
The report's findings emphasize the need for advanced detection capabilities:
Bot Management Solutions:
Organizations should evaluate and implement dedicated bot management solutions that provide:
API Security:
Given the prominence of API attacks, dedicated API security measures should include:
The AI-powered threat documented in the 2025 report necessitates corresponding AI-powered defense:
Adaptive Detection Systems:
Continuous Learning:
Security Team Capabilities:
Organizations must ensure security teams have:
Incident Response Planning:
Preparation for bot-based incidents should include:
Financial Services:
Given the high targeting of financial services:
E-Commerce:
For e-commerce platforms:
Gaming and Gambling:
For these high-target sectors:
The 2025 report documents what amounts to an emerging AI arms race between attackers and defenders:
Attacker Evolution:
AI capabilities in attacks continue to advance:
Defender Response:
Defensive AI must evolve correspondingly:
The report implies emerging regulatory considerations:
Data Protection Implications:
Platform Responsibility:
Emerging Technologies:
Several technological developments will shape the future bot landscape:
The economic dimensions of the bot threat will continue to evolve:
Cost Escalation:
Market Dynamics:
The Imperva Bad Bot Report 2025 documents a critical inflection point in the evolution of automated threats. The central findings establish:
Bot Traffic Dominance: Bots now account for 51% of all web traffic, with malicious bots comprising 37% of total internet traffic—a significant increase from 32% in 2023 .
AI-Driven Threat Escalation: Artificial intelligence has fundamentally transformed the bot threat landscape, democratizing sophisticated attack capabilities and enabling more evasive campaigns. AI-driven bots like ByteSpider (responsible for 54% of AI-driven attacks) represent a new category of threat .
Attack Volume Scale: The blocking of 13 trillion bad bot requests during the 2024 data collection period demonstrates the massive scale of automated threats .
Industry Targeting Patterns: Financial services, e-commerce, gambling, gaming, automotive, and travel sectors face the highest concentration of bot attacks, each with industry-specific attack vectors .
Attack Vector Evolution: Account takeover, API attacks, and scraping operations remain dominant attack categories, while AI enhancement has made these attacks more sophisticated and harder to detect.
BaaS Ecosystem Maturation: The Bad Bots as a Service model has matured into a sophisticated criminal enterprise with professional-grade services, AI integration, and accessible attack capabilities.
The findings of the 2025 report carry significant implications for organizations across all sectors:
Strategic Priority: Bot security must be elevated from a technical concern to a strategic priority at the executive level, given the business impact of bot attacks on revenue, competitive position, and customer trust.
Defense Evolution: Traditional rule-based detection approaches are insufficient against AI-powered threats. Organizations must invest in sophisticated, ML-based detection capabilities.
Continuous Adaptation: The rapid evolution of bot threats requires continuous monitoring, learning, and adaptation of defensive measures.
Industry Collaboration: Addressing the bot threat effectively requires collaboration across organizations, industries, and with security vendors to share threat intelligence and best practices.
The Imperva Bad Bot Report 2025 serves as both a warning and a call to action. The unprecedented scale of bot traffic, the AI-driven acceleration of threats, and the professionalization of the bot attack ecosystem demand a corresponding evolution in defensive capabilities.
Organizations that fail to adapt to this new threat landscape face increasing risks of:
Conversely, organizations that invest in sophisticated bot detection and management capabilities will be better positioned to protect their digital assets, maintain customer trust, and operate effectively in an increasingly automated digital environment.
The 2025 report makes clear that the era of treating bots as a mere nuisance has ended. In today's digital landscape, bots represent a strategic threat that demands strategic response. The organizations that recognize this reality and invest accordingly will be best positioned to thrive in the face of the evolving automated threat landscape.
Account Takeover (ATO): Unauthorized access to user accounts through credential stuffing, brute force, or other attack methods.
API (Application Programming Interface): Software interfaces that enable different applications to communicate, often targeted for data extraction or manipulation.
Bad Bots as a Service (BaaS): Criminal business model offering bot attack capabilities as subscription or pay-per-use services.
Behavioral Analysis: Detection technique that examines patterns of interaction to identify automated behavior.
Bot Management: Technology solutions designed to detect, categorize, and respond to bot traffic.
ByteSpider Bot: AI-related crawler identified as responsible for 54% of AI-driven attacks in the 2025 report.
Credential Stuffing: Attack method using leaked credentials to attempt access to accounts on other platforms.
Headless Browser: Web browser without a graphical interface, commonly used in bot operations.
Machine Learning (ML): AI technique enabling systems to learn from data and improve over time.
Residential Proxy: Proxy services routing traffic through residential IP addresses to evade detection.
Scalping: Automated purchasing of limited-availability items for resale at higher prices.
Scraping: Automated extraction of data from websites or APIs.
Report compiled based on analysis of Imperva Bad Bot Report 2025 findings and related cybersecurity research. All statistics cited are attributed to their respective sources as indicated by in-line citations.