Blog
The latest on technical enforcement of crawling preferences.
Scrapling and Crawlee: How Open-Source Scraping Tools Get Detected
A technical analysis of Scrapling and Crawlee, two popular open-source scraping frameworks, examining their anti-detection features and the behavioral signals that content-layer defenses can exploit.
How AI Scraping Infrastructure Works: Proxies, Evasion, and Scale
Inside the technical infrastructure AI companies use to scrape the web: residential proxy networks, fingerprint emulation, CAPTCHA solving, and why traditional defenses fail.
The AI Crawler Compliance Crisis: Who Plays by the Rules?
AI crawler robots.txt compliance dropped from 96.7% to 70% in one year. Analysis of which crawlers comply, what it costs publishers, and what comes next.
Understanding AIPREF: The IETF Standard for AI Content Preferences
AIPREF extends robots.txt with standardized vocabulary for AI training preferences. How the IETF standard works, its syntax, and what it means for publishers.
Data Poisoning FAQ: Technical, Legal, and Policy Answers
Answers to common questions about data poisoning, web crawling, robots.txt, AIPREF, legal status, and enforcement mechanisms for AI training defense.
Publisher Defenses Against AI Scraping: Cost Imposition vs Poisoning
Comparing defense strategies against AI scraping: proof-of-work systems impose costs, data poisoning degrades value. Who pays and what works for publishers.
AI Poisoning Threat Models: Backdoors, RAG, and Supply Chain
Backdoor attacks, model degradation, and RAG poisoning explained. Technical analysis of who can attack, defense costs, and power dynamics in AI training data.
Defensive Data Poisoning: Ethics, Risks, and Alternatives
Analyzing ethical tradeoffs of defensive data poisoning: proportionality, collateral damage, and safer alternatives like proof-of-work and AIPREF standards.
What Is Data Poisoning in Machine Learning?
Data poisoning manipulates AI training data to alter model behavior. Learn how defensive tools like Nightshade protect content from unauthorized AI training.
Why VENOM Exists: From robots.txt to AI Data Enforcement
When robots.txt fails, enforcement mechanisms emerge. VENOM analyzes data poisoning, proof-of-work, and technical countermeasures for AI training governance.