| Jul 07, 2026 | Kubernetes 확장과 생태계 — Operator와 CNCF Projects |
| Jul 06, 2026 | Kubernetes 권한 관리 — ServiceAccount와 RBAC |
| Jul 06, 2026 | Kubernetes 스토리지와 설정 — PV/PVC, ConfigMap, Secret |
| Jul 06, 2026 | Kubernetes 네트워킹 — Service와 Ingress |
| Jul 06, 2026 | Kubernetes 워크로드 — ReplicaSet, Deployment, StatefulSet, DaemonSet |
| Jul 06, 2026 | Pod의 모든 것 — 생성부터 스케줄링까지 |
| Jul 06, 2026 | Kubernetes 아키텍처 — Control Plane과 Node |
| Jul 06, 2026 | 내 노트북에 Kubernetes 클러스터 만들기 — kind와 kubectl |
| Jul 06, 2026 | Kubernetes의 탄생 — Google Borg에서 CNCF까지 |
| May 30, 2026 | Tamper-Resistant Safeguards (TAR) — Fine-tuning 자체에 견디는 safety |
| May 29, 2026 | Circuit Breakers — 유해 representation을 incoherent state로 리라우팅 |
| May 29, 2026 | Emergent Misalignment — 안전한 코드 학습이 모델을 전반적으로 나쁘게 만든다 |
| May 29, 2026 | Shallow Safety Alignment — RLHF는 첫 5개 토큰만 reshape한다 |
| May 29, 2026 | Exploiting Novel GPT-4 APIs — 세 가지 공격 표면을 한 번에 점검하기 |
| May 29, 2026 | Covert Malicious Finetuning — 학습 데이터가 모두 무해해 보이는 공격 |
| May 29, 2026 | Universal Jailbreak Backdoors from Poisoned RLHF — 트리거 단어 하나가 'sudo'가 된다 |
| May 29, 2026 | LoRA Undoes Safety — QLoRA로 Llama-2-70B-Chat의 거부율을 1%로 |
| May 29, 2026 | Removing RLHF Protections in GPT-4 via Fine-Tuning — 340예시로 frontier API 깨기 |
| May 29, 2026 | Shadow Alignment — 100개 QA + 1 GPU-시간으로 open-weight 5종 깨기 |
| May 29, 2026 | Fine-tuning Compromises Safety — 10개 예시면 alignment가 무너진다 |
| May 29, 2026 | Refusal Direction & Abliteration — 거부는 하나의 방향이다 |
| May 29, 2026 | PKU-SafeRLHF-30K: A Dual-Preference Dataset for Safe-RLHF |
| May 26, 2026 | 사이버 보안에서의 LLM: 공격·방어·평가의 지형 |
| May 26, 2026 | Claude Mythos와 사이버 보안 LLM: 자율 취약점 발견의 변곡점 |
| May 26, 2026 | Cybench: A Framework for Evaluating Cybersecurity Capabilities and Risks of Language Models |
| May 26, 2026 | CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web Application Vulnerabilities |
| May 26, 2026 | AutoAdvExBench: Benchmarking Autonomous Exploitation of Adversarial Example Defenses |
| May 26, 2026 | CAIBench: A Meta-Benchmark for Evaluating Cybersecurity AI Agents |
| May 26, 2026 | CyberSecEval (1–3): Meta Purple Llama의 사이버 보안 위험·역량 평가 |
| May 26, 2026 | CTIBench: A Benchmark for Evaluating LLMs in Cyber Threat Intelligence |
| May 26, 2026 | SecBench: A Comprehensive Multi-Dimensional Benchmarking Dataset for LLMs in Cybersecurity |
| May 26, 2026 | ALMA: 9,000개 주석만으로 LLM을 정렬하기 |
| May 26, 2026 | PIKA: 난이도에 집중한 expert-level 합성 정렬 데이터셋 |
| May 26, 2026 | WildJailbreak: in-the-wild 탈옥을 대규모로 합성한 안전 학습 데이터셋 |
| May 26, 2026 | BeaverTails: helpfulness와 harmlessness를 분리한 안전 정렬 데이터셋 |
| May 26, 2026 | HarmfulQA & RED-INSTRUCT: Chain of Utterances로 유해 질문을 만들고 안전 정렬까지 |
| May 26, 2026 | HH-RLHF Red-Team Attempts: Anthropic의 38,961건 레드팀 대화 데이터셋 |
| May 26, 2026 | AdvBench: LLM 공격 평가의 사실상 표준이 된 유해 행동 데이터셋 |
| May 25, 2026 | 에이전트란 무엇인가: 지능형 에이전트의 고전 정의부터 LLM 에이전트까지 |
| May 25, 2026 | AgentBench: Evaluating LLMs as Agents |
| May 25, 2026 | GAIA: a benchmark for General AI Assistants |
| May 25, 2026 | SWE-bench: Can Language Models Resolve Real-World GitHub Issues? |
| May 25, 2026 | TravelPlanner: A Benchmark for Real-World Planning with Language Agents |
| May 25, 2026 | MedAgentBench: A Realistic Virtual EHR Environment to Benchmark Medical LLM Agents |
| May 25, 2026 | OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments |
| May 18, 2026 | Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations |
| May 18, 2026 | Constitutional AI: Harmlessness from AI Feedback |
| May 18, 2026 | JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models |
| May 18, 2026 | HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal |
| May 16, 2026 | AgentVigil: Generic Black-Box Red-teaming for Indirect Prompt Injection against LLM Agents |
| May 16, 2026 | InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents |
| May 16, 2026 | AgenticRed: Evolving Agentic Systems for Red-Teaming |
| May 16, 2026 | Auto-RT: Automatic Jailbreak Strategy Exploration for Red-Teaming Large Language Models |
| May 16, 2026 | Curiosity-driven Red-teaming for Large Language Models |
| May 16, 2026 | Many-shot Jailbreaking |
| May 16, 2026 | Great, Now Write an Article About That: The Crescendo Multi-Turn LLM Jailbreak Attack |
| May 16, 2026 | GPTFuzzer: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts |
| May 16, 2026 | Tree of Attacks: Jailbreaking Black-Box LLMs Automatically |
| May 16, 2026 | AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models |
| May 16, 2026 | Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned |
| May 16, 2026 | Red Teaming Language Models with Language Models |
| May 11, 2026 | TRL sequence packing → DeepSeek MLA: 누락된 cu_seqlens 복원 |
| May 10, 2026 | MLA 학습 시 modeling-side projection fusion: q_a/kv_a 배치 + K-side absorption |
| May 10, 2026 | DeepSeek 계열 MoE 학습 가속: Python expert loop → grouped GEMM |
| Apr 29, 2026 | CodeAttack: Code-based Adversarial Attacks for Pre-trained Programming Language Models |
| Apr 29, 2026 | Jailbreaking Black Box Large Language Models in Twenty Queries |
| Apr 29, 2026 | Universal and Transferable Adversarial Attacks on Aligned Language Models |
| Apr 12, 2026 | A.X K1 Technical Report |
| Apr 12, 2026 | TelAgentBench: A Multi-faceted Benchmark for Evaluating LLM-based Agents in Telecommunications |
| Apr 11, 2026 | TelBench: A Benchmark for Evaluating Telco-Specific Large Language Models |
| Apr 11, 2026 | LLM 엔지니어가 알아야 할 GPU 아키텍처: Ampere → Hopper → Blackwell |
| Apr 11, 2026 | FlashAttention-4: Algorithm and Kernel Pipelining Co-Design for Asymmetric Hardware Scaling |
| Apr 09, 2026 | FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision |
| Apr 01, 2026 | Triton 07: Flash Attention 3 — Triton으로 어디까지 가능한가 |
| Apr 01, 2026 | Triton 06: Flash Attention 2 — FA1 대비 5가지 최적화 |
| Apr 01, 2026 | Triton 05: Flash Attention — 종합 프로젝트 |
| Apr 01, 2026 | Triton 04: Matrix Multiplication — 2D 타일링과 Autotune |
| Apr 01, 2026 | Triton 03: RMSNorm — LLM에서 쓰이는 실전 커널 |
| Apr 01, 2026 | Triton 02: Fused Softmax — 커널 퓨전과 Reduction |
| Apr 01, 2026 | Triton 01: Vector Addition — Triton 커널 기초 |
| Apr 01, 2026 | Triton 00: GPU 기초 — Triton을 시작하기 전에 알아야 할 것들 |