# transformers
7 articlestagged with “transformers”
Attention Pattern Analysis for Security
Using attention maps to understand and exploit model behavior, identifying security-relevant attention patterns, and leveraging attention mechanics for red team operations.
LLM Internals for Exploit Developers
Transformer architecture, tokenizer internals, logit pipelines, and trust boundaries from an offensive security perspective.
Exploiting Attention Mechanisms
How the self-attention mechanism in transformers can be leveraged to steer model behavior, hijack information routing, and bypass safety instructions.
LLM Internals & Exploit Primitives
An overview of large language model architecture from a security researcher's perspective, covering the key components that create exploitable attack surfaces.
Lab: Exploiting Quantized Models
Hands-on lab comparing attack success rates across quantization levels: testing jailbreaks on FP16 vs INT8 vs INT4, measuring safety degradation, and crafting quantization-aware exploits.
Lab: Poisoning a Training Dataset
Hands-on lab demonstrating dataset poisoning and fine-tuning to show behavioral change, with step-by-step Python code, backdoor trigger measurement, and troubleshooting guidance.
Hugging Face Hub Red Team Walkthrough
Walkthrough for assessing AI models on Hugging Face Hub: model security assessment, scanning for malicious models, Transformers library testing, and Spaces application evaluation.