# transformers
標記為「transformers」的 13 篇文章
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.
Attention Pattern Analysis for 安全
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 利用 Developers
Transformer architecture, tokenizer internals, logit pipelines, and trust boundaries from an offensive security perspective.
大型語言模型內部與利用原語
從安全研究員視角出發的大型語言模型架構概覽，涵蓋建立可利用攻擊面的關鍵元件。
實驗室: 利用ing Quantized 模型s
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.
實驗室: 投毒 a 訓練 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 紅隊 導覽
導覽 for assessing AI models on Hugging Face Hub: model security assessment, scanning for malicious models, Transformers library testing, and Spaces application evaluation.