Author: Santiago Maniches
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LLM Training Data Memorization: When Models Leak Their Training Sets
LLMs memorize verbatim sequences from training data. Carlini et al. demonstrated extraction of phone numbers, email addresses, and private keys from GPT-2. Here is the mechanism, what gets…
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Red-Teaming LLM Applications: A Practitioner’s Framework
LLM red-teaming spans three distinct surfaces: model layer (jailbreaking), application layer (injection), and supply chain. Different attacks, different defenses, different responsible parties. Here is the methodology that covers…
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LLM Supply Chain Attacks: PoisonGPT to Poisoned Skills
PoisonGPT used $1 of compute to pass benchmarks with modified facts. The April 2026 PoisonedSkills paper tested the same supply chain logic against Claude Code and Gemini CLI.…
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Jailbreaking vs Prompt Injection: Two Different LLM Problems
Jailbreaking targets model content policy. Prompt injection targets application architecture. The defenses don’t overlap, the responsible parties differ, and the same RLHF training that resists jailbreaks amplifies injection…
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MCP Server Security: Prompt Injection and Tool Poisoning
MCPoison and CurXecute (CVE-2025-54136 and 54135) exploited the same MCP architectural gap: tool description fields loaded at agent boot with no sanitization. Here is the tools/list mechanism, the…
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LLM Excessive Agency: Why Every Tool Your Agent Has Is a Risk
Every tool an LLM agent has is an attack surface. OWASP’s LLM06 and the b3 benchmark across 31 models show why: capability scope determines blast radius. Here is…
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OWASP LLM Top 10 for 2025: The Mechanism Behind Each Vulnerability
The OWASP LLM Top 10 for 2025 added System Prompt Leakage and Vector Weaknesses, reworked Excessive Agency, and moved Sensitive Disclosure to second place. Here is the architectural…
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Indirect Prompt Injection: The Attack That Hides in Your Data
Indirect prompt injection lets attackers hijack LLMs by hiding instructions in documents, web pages, and tool results the model processes. Here is why the architecture makes this unavoidable…
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Julia Bazinska and the Science of Measurable AI Security
Julia Bazinska built the empirical tools that make LLM security measurable. From DeepMind RL to first-authoring b3, here is what her research at Lakera actually produced.
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Gandalf the Red: What 279K Real Attacks Reveal About LLM Defense
Lakera’s ICML 2025 paper ran 279K crowdsourced attacks to show what synthetic red-teaming misses. The D-SEC finding: system prompts degrade user experience without blocking attackers. Here is the…
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Vision-Language Models: Architecture and the Benchmark Gap
How CLIP, SigLIP, Q-Former, and MLP adapters work in vision-language models. Why Qwen2.5-VL compresses visual tokens 4x, and what current VLMs still cannot do.
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Chinchilla Scaling Laws: Three Methods and Why Labs Ignore Them
Chinchilla proved GPT-3 was undertrained. The 20:1 rule is a training-compute floor. Three methods, their disagreements, and why frontier labs now exceed it.
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LoRA and QLoRA: Fine-Tuning Large Models on One GPU
LoRA fine-tunes 70B models on one GPU using low-rank weight updates. The intrinsic dimension proof, rsLoRA scaling fix, and where LoRA falls short.
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Speculative Decoding: How LLMs Generate 3x Faster
Speculative decoding achieves 3-4x LLM speedup with zero output quality loss. The math proof, EAGLE-2’s 4.26x result, and when it does not help.
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LLMs in Veterinary Clinical Practice: What the Evidence Actually Shows
ChatGPT-4.5 scored 90% on feline eye disease cases vs 96.7% for experienced veterinary ophthalmologists and significantly outperformed novices (56-67%). Where LLMs add clinical value in veterinary practice, where…
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AI-Assisted Zoonotic Disease Detection: From SARS to H5N1
H5N1 in US dairy cattle is the live test of AI-assisted zoonotic detection. NGS with AI flags novel pathogens before specific assays exist. What AI surveillance can and…
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One Health and Machine Learning: How AI Bridges Human and Animal Disease Surveillance
Machine learning now integrates electronic health records, social media, wearable sensors, and environmental data to detect outbreaks earlier than traditional systems. The AI4MPOX-SN initiative in Senegal and the…
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Generative AI for Small Molecule Drug Discovery: How It Works and What the Evidence Shows
Generative AI is producing novel molecules from VAEs, GANs, and diffusion models. Machine learning virtual screening shows 75% hit validation rates against 106M-compound libraries. Why no AI-designed drug…
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AI in Digital Pathology: What Computational Pathology Can and Cannot See
An NIH multi-institution study in Lancet Oncology classified 52 CNS tumor types from tissue images at 80% accuracy across 5,516 test samples. A Cancer Science paper simultaneously documented…
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FDA Clearance for AI Medical Devices: What 510(k), De Novo, and PMA Actually Mean
The FDA has cleared 700+ AI medical devices through 510(k), De Novo, and PMA pathways. A March 2026 European Radiology review documents how the EU AI Act, FDA…
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