Full Report
Generative AI scraper bots are gray bots designed to extract or scrape large volumes of data from websites, often to train generative AI models. In this report we look at what the data tells us about Gen AI gray bot activity facing organizations today.
Analysis Summary
# Tool/Technique: Bots (Good, Bad, and Gray/Gen AI Scraper Bots)
## Overview
Bots are automated software programs designed for online activities. The article focuses primarily on "gray bots," specifically Generative AI (Gen AI) scraper bots, which aggressively extract large volumes of website data, often for training AI models. While not overtly malicious like "bad bots" (used for fraud or credential stuffing), gray bots pose significant business risks due to aggressive scraping, resource consumption, data theft, and distortion of analytics.
## Technical Details
- Type: Technique (Automated Data Scraping/Crawling)
- Platform: Web applications, Websites (General Internet)
- Capabilities: Automated data extraction, high-volume request generation, persistent activity. Modern gray bots utilize sophisticated techniques that blur the lines between legitimate crawling and harmful activity.
- First Seen: The concept of bots is long-standing; the rise of prolific Gen AI era gray bots mentioned occurred in late 2024/early 2025.
## MITRE ATT&CK Mapping
The activity described primarily relates to reconnaissance and resource theft, though broad. A strong mapping involves automated collection:
- **TA0043 - Impact**
- T1487 - Resource Hijacking (For resource consumption/DDoS potential)
- **TA0048 - Inhibit System Recovery** (If activity leads to disruption)
- **TA0047 - Collection**
- T1005 - Data from Local System (Conceptually applies to data gathered from web resources)
## Functionality
### Core Capabilities
- **Data Scraping:** Extracting large volumes of data (including creative or commercial data) from websites.
- **Consistency:** Maintaining high volumes of requests consistently over 24 hours, contrasting with traditional traffic waves.
- **Resource Consumption:** Increasing server load, bandwidth consumption, and cloud CPU usage, leading to degraded performance and increased costs.
### Advanced Features
- **AI Model Training:** The primary goal for Gen AI scraper bots is gathering data used to train large language and generative AI models.
- **Analytics Distortion:** Distorting website analytics by generating synthetic user behavior, making true business tracking difficult.
- **Ethical/Legal Gray Area:** Operating in a manner that may violate copyright or data privacy regulations (e.g., in healthcare/finance sectors).
## Indicators of Compromise
*Note: Specific forensic artifacts like hashes are generally not applicable to generic bot identification, which relies more on signatures and behavior. Network indicators are defanged.*
- File Hashes: N/A (Relates to network traffic analysis)
- File Names: N/A
- Registry Keys: N/A
- Network Indicators:
* **Observed User Agents:** ClaudeBot, Bytespider, PerplexityBot, DeepSeekBot, TikTok's bot (Bytespider), OpenAI/GPTbot, Google-Extended (though the latter may follow protocols).
- Behavioral Indicators:
* Unusually high request rates (e.g., 17,000 requests per hour consistently).
* Persistent, non-wave-like traffic patterns unusual for human interaction or traditional web crawlers.
* Requests exhibiting patterns optimized for high-volume data extraction rather than typical browsing workflows.
## Associated Threat Actors
These are associated with the entities developing the AI models, rather than traditional criminal threat actors:
- Anthropic (Creator of ClaudeBot)
- TikTok/ByteDance (Creator of Bytespider)
- OpenAI
- Google
- DeepSeek
- Perplexity AI
## Detection Methods
- **Signature-based detection:** Identifying known bot User Agents (e.g., ClaudeBot, Bytespider).
- **Behavioral detection:** Monitoring for high-volume, consistent traffic patterns inconsistent with genuine user behavior. Analyzing request sequencing and speed.
- **Fingerprinting:** Utilizing comprehensive fingerprinting techniques to identify automated scripts, regardless of User Agent masking.
- **Protocol Checking:** Checking adherence to established web protocols like `robots.txt`.
## Mitigation Strategies
- **Robots.txt Implementation:** Deploying `robots.txt` directives specifically naming the scraper bots to request exclusion from crawling. (Note: This is not legally binding and can be ignored by malicious bots).
- **Advanced Bot Protection:** Implementing solutions featuring behavior-based detection, adaptive machine learning, and real-time blocking capabilities (e.g., Barracuda Advanced Bot Protection).
- **Rate Limiting:** Implementing aggressive rate limiting based on request volume, IP, and frequency.
- **Analytics Cleaning:** Actively filtering bot traffic out of core analytics to ensure data integrity.
- **Legal/Policy Review:** Reviewing data usage policies regarding scraping public-facing content.
## Related Tools/Techniques
- **ClaudeBot:** Specifically mentioned as a highly active Gen AI scraper bot.
- **Bytespider:** TikTok's AI scraper bot, noted as particularly aggressive.
- **PerplexityBot, DeepSeekBot:** Other identified Gen AI scraper bots.
- **OpenAI/GPTbot:** Mentioned in context of established crawler documentation.
- **Web Scraper Bots (General):** Older, less sophisticated scraping tools.