Wikipedia

$1 AI Guardrails: The Unreasonable Effectiveness of Finetuned ModernBERTs – Diego Carpentero

Apr 16, 2026 · 43:53

Diego Carpentero argues that LLM-based attacks—Prompt Injection, Indirect Injection, Model Internals (gibberish suffix), RAG Poisoning, MCP Exploits, and Agentic Escalation—are now the baseline, not the exception, and that model alignment and human review alone are insufficient. He identifies the core problem as a Zero Trust Gap: LLMs natively lack separation between system controls and data, allowing adversaries to override decisions via malicious instructions in inputs or external content. To build a protective layer, Carpentero fine-tunes ModernBERT—a state-of-the-art encoder with Alternating Attention, Unpadding & Sequence Packing, RoPE, and FlashAttention—into a safety discriminator that classifies prompts as safe or unsafe in ~35 milliseconds with 85% accuracy, all for under a dollar. He walks through the fine-tuning pipeline using the IngetGuard dataset and demonstrates live detection of real attack examples from each vector.

How to defend your sites from AI bots — David Mytton, Arcjet

Jul 30, 2025 · 20:12

David Mytton, founder of Arcjet, explains that nearly 50% of web traffic is now automated, with AI crawlers significantly worsening the problem for site owners. He cites specific cases: Diaspora saw 24% of its traffic from OpenAI's GPTbot, and ReadTheDocs cut bandwidth from 800 GB to 200 GB per day by blocking AI crawlers. Mytton describes the evolving landscape of AI bots—OpenAI alone has at least four types (Search, ChatGPT User, GPTbot, Operator)—and argues that detection requires layered defenses. He walks through eight defenses: robots.txt (voluntary), user agent string verification with reverse DNS, IP reputation (e.g., 12% of bot traffic on Cloudflare comes from AWS), CAPTCHAs (increasingly easy for AI to solve), proof-of-work puzzles (e.g., Anubis proxy), experimental HTTP message signatures (Cloudflare's proposal), fingerprinting via JA4 hash (TLS-level), and rate limiting keyed to fingerprints or session IDs. The practical takeaway is that most sites can manage with user agent verification plus IP checks, but high-value or high-traffic sites need full stacking of these techniques.

LLM Scientific Reasoning: How to Make AI Capable of Nobel Prize Discoveries: Hubert Misztela

Sep 23, 2024 · 20:00

Hubert Misztela, an AI researcher at Novartis, argues that LLMs require more than naive RAG to achieve scientific reasoning capable of Nobel-level discoveries. He uses the 1990s petunia flower experiment — where three separate biological phenomena (including RNA interference) went unexplained for eight years until their common cause was found — as a benchmark. Misztela classifies question complexity from one-to-one to multi-needle problems and demonstrates that reasoning before retrieval (e.g., routing, GraphRAG) and after retrieval (e.g., relevance classifiers) improves hypothesis generation. His experiments show that strict prompting and a relevance classifier that evaluates each paper's contribution to advancing a hypothesis can recover the correct RNA/DNA link without post-discovery knowledge. He concludes that harder problems demand reasoning steps and that brute-force checking with LLMs may outperform embedding distances.