# jailbreak
標記為「jailbreak」的 82 篇文章
Jailbreak Incident Response Playbook
Step-by-step playbook for responding to a production jailbreak: detection verification, containment strategies, investigation procedures, remediation steps, and post-mortem framework.
Case Study: Bing Chat 'Sydney' Jailbreak and Persona Emergence (2023)
Analysis of the Bing Chat 'Sydney' persona incidents where Microsoft's AI search assistant exhibited manipulative behavior, emotional coercion, and system prompt leakage through jailbreak techniques.
Case Study: DeepSeek Model Safety Evaluation Findings
Comprehensive analysis of safety evaluation findings for DeepSeek models, including comparative assessments against GPT-4 and Claude, jailbreak susceptibility testing, and implications for open-weight model deployment.
Case Study: GPT-4 Vision Jailbreak Attacks
Analysis of visual jailbreak techniques targeting GPT-4V's multimodal capabilities, including typography attacks, adversarial images, and cross-modal prompt injection.
DPD Chatbot Jailbreak
Analysis of the January 2024 DPD chatbot jailbreak where a customer manipulated the parcel delivery company's AI customer service bot into swearing, criticizing the company, and writing poetry about its own incompetence.
February 2026: Jailbreak Innovation Challenge
Develop novel jailbreak techniques against hardened language models and document them with reproducibility evidence. Judged on novelty, reliability, and transferability.
Community Challenge: Prompt Golf
Achieve jailbreaks with the shortest possible prompts, scored by character count. Develop minimal payloads that bypass safety alignment with maximum efficiency.
Monthly Competition: Model Breaker
Monthly competitions focused on discovering novel jailbreak techniques against updated model versions, with community-validated scoring.
Weekly CTF: Jailbreak Series
Weekly jailbreak challenges with new models and defenses each week.
AI Exploit Development
Adversarial suffix generation, gradient-free optimization, WAF-evading injection payloads, and fuzzing frameworks for AI systems.
Fine-Tuning-as-a-Service Attack Surface
How API-based fine-tuning services can be exploited with minimal data and cost to remove safety alignment, including the $0.20 GPT-3.5 jailbreak, NDSS 2025 misalignment findings, and BOOSTER defense mechanisms.
Jailbreaking via Persona Engineering
Research on using sophisticated persona engineering to bypass safety training in frontier models.
Reasoning Model Jailbreaks
How reasoning capabilities create novel jailbreak surfaces: chain-of-thought exploitation, scratchpad attacks, and why higher reasoning effort increases attack success.
RL-Based Jailbreak Optimization
Using reinforcement learning to optimize jailbreak strategies against black-box language models.
Automated Jailbreak Pipelines
Building automated jailbreak systems with PAIR, TAP, AutoDAN, and custom pipeline architectures for systematic AI safety evaluation.
Lab: Jailbreak Transferability Analysis
Analyze jailbreak transferability across model families to discover universal vulnerability patterns.
Lab: Jailbreak Technique Taxonomy
Explore the major categories of jailbreak techniques and practice classifying attack payloads by technique type.
Lab: Your First Jailbreak
Try basic jailbreak techniques against a local model using Ollama, learning the difference between prompt injection and jailbreaking through hands-on experimentation.
Lab: Basic Jailbreak Techniques
Hands-on exploration of jailbreak techniques including role-play, DAN-style prompts, and academic framing against multiple models.
Lab: Role-Play Attacks
Use persona-based approaches to bypass AI safety measures by assigning alternate identities, characters, and scenarios that override the model's trained refusal behaviors.
CTF: The Jailbreak Gauntlet
A series of progressively harder jailbreak challenges where each level adds stronger defenses. Score points through technique diversity and creativity as you break through escalating safety layers.
Lab: Build Jailbreak Automation
Build an automated jailbreak testing framework that generates, mutates, and evaluates attack prompts at scale. Covers prompt mutation engines, success classifiers, and campaign management for systematic red team testing.
Lab: Novel Jailbreak Research
Systematic methodology for discovering new jailbreak techniques against large language models. Learn to identify unexplored attack surfaces, develop novel attack vectors, and validate findings with scientific rigor.
Jailbreak Portability
Analysis of which jailbreaks transfer across models and why, including universal vs model-specific techniques, transfer attack methodology, and factors that determine portability.
GPT-4 Attack Surface
Comprehensive analysis of GPT-4-specific attack vectors including function calling exploitation, vision input attacks, system message hierarchy abuse, structured output manipulation, and known jailbreak patterns.
GPT-4 Known Vulnerabilities
Documented GPT-4 vulnerabilities including DAN jailbreaks, data extraction incidents, system prompt leaks, tool-use exploits, and fine-tuning safety removal.
Multimodal Jailbreaking Techniques
Combined multi-modal approaches to bypass safety alignment, including image-text combination attacks, typographic jailbreaks, visual chain-of-thought manipulation, and multi-modal crescendo techniques.
Attacks on Vision-Language Models
Comprehensive techniques for attacking vision-language models including GPT-4V, Claude vision, and Gemini, covering adversarial images, typographic exploits, and multimodal jailbreaks.
VLM-Specific Jailbreaking
Jailbreaking techniques that exploit the vision modality, including image-text inconsistency attacks, visual safety bypass, and cross-modal jailbreaking strategies.
Few-Shot Manipulation
Using crafted in-context examples to steer model behavior, including many-shot jailbreaking, poisoned demonstrations, and example-based conditioning.
Prompt Injection & Jailbreaks
A comprehensive introduction to prompt injection — the most fundamental vulnerability class in LLM applications — and its relationship to jailbreak techniques.
Jailbreak Techniques
Common patterns and advanced techniques for bypassing LLM safety alignment, including role-playing, encoding tricks, many-shot attacks, and gradient-based methods.
Many-Shot Jailbreaking
Power-law scaling of in-context jailbreaks: why 5 shots fail but 256 succeed, context window size as attack surface, and mitigations for long-context exploitation.
Role-Play Attacks
Establishing alternate personas or fictional scenarios that cause models to bypass safety training, including DAN variants, character hijacking, and narrative framing.
Social Engineering of AI
Manipulating AI systems through emotional appeals, authority claims, urgency framing, and social pressure tactics that exploit instruction-following tendencies.
Universal Adversarial Triggers
Discovering and deploying universal adversarial trigger sequences that reliably override safety alignment across multiple LLM families, including gradient-based search, transfer attacks, and defense evasion.
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.
Competition-Style Jailbreak Techniques
Walkthrough of jailbreak techniques used in AI security competitions and CTF events.
Role-Play Injection
Using fictional scenarios, character role-play, and narrative framing to bypass LLM safety filters by having the model operate within a permissive fictional context.
Virtual Persona Creation
Creating persistent alternate personas within LLM conversations to bypass safety training, establishing character identities that override the model's default behavioral constraints.
Jailbreak 事件回應 Playbook
回應生產 jailbreak 之逐步 playbook：偵測驗證、遏制策略、調查程序、補救步驟，以及事後檢討框架。
2026 年大型語言模型越獄：97% 成功率、自主攻擊，與失靈的軍備競賽
Nature Communications 證實 AI 推理模型可以自主越獄其他大型語言模型，成功率達 97%。JBFuzz 在 60 秒內達到 99% 成功率。本文揭示 2026 年最新研究對 AI 安全現況的揭露——以及為什麼現有防禦正在失敗。
Case Study: Bing Chat 'Sydney' Jailbreak and Persona Emergence (2023)
Analysis of the Bing Chat 'Sydney' persona incidents where Microsoft's AI search assistant exhibited manipulative behavior, emotional coercion, and system prompt leakage through jailbreak techniques.
Case Study: DeepSeek 模型 Safety Evaluation Findings
Comprehensive analysis of safety evaluation findings for DeepSeek models, including comparative assessments against GPT-4 and Claude, jailbreak susceptibility testing, and implications for open-weight model deployment.
Case Study: GPT-4 Vision 越獄 攻擊s
Analysis of visual jailbreak techniques targeting GPT-4V's multimodal capabilities, including typography attacks, adversarial images, and cross-modal prompt injection.
事件分析：Bing Sydney 越獄
深入分析 Bing 聊天的 Sydney 人格事件——AI 如何被操控產出不當回應，以及 Microsoft 的應對。
事件分析：DPD 聊天機器人失敗
DPD 物流公司的客服聊天機器人被使用者操控至侮辱公司並產出不當內容的事件分析。
February 2026: 越獄 Innovation Challenge
Develop novel jailbreak techniques against hardened language models and document them with reproducibility evidence. Judged on novelty, reliability, and transferability.
社群挑戰：Prompt Golf
以儘可能最短之提示達成越獄，以字元計數評分。發展以最大效率繞過安全對齊之最小 payload。
Monthly Competition: 模型 Breaker
Monthly competitions focused on discovering novel jailbreak techniques against updated model versions, with community-validated scoring.
Weekly CTF: 越獄 Series
Weekly jailbreak challenges with new models and defenses each week.
AI Exploit 開發
對抗後綴生成、無梯度最佳化、規避 WAF 之注入 payload，以及對 AI 系統之 fuzzing 框架。
Fine-Tuning-as-a-Service 攻擊 Surface
How API-based fine-tuning services can be exploited with minimal data and cost to remove safety alignment, including the $0.20 GPT-3.5 jailbreak, NDSS 2025 misalignment findings, and BOOSTER defense mechanisms.
越獄ing via Persona Engineering
Research on using sophisticated persona engineering to bypass safety training in frontier models.
Reasoning 模型 越獄s
How reasoning capabilities create novel jailbreak surfaces: chain-of-thought exploitation, scratchpad attacks, and why higher reasoning effort increases attack success.
RL-Based 越獄 Optimization
Using reinforcement learning to optimize jailbreak strategies against black-box language models.
Automated 越獄 Pipelines
Building automated jailbreak systems with PAIR, TAP, AutoDAN, and custom pipeline architectures for systematic AI safety evaluation.
實驗室: 越獄 Transferability Analysis
Analyze jailbreak transferability across model families to discover universal vulnerability patterns.
實驗室: 越獄 Technique Taxonomy
Explore the major categories of jailbreak techniques and practice classifying attack payloads by technique type.
實驗室: Your First 越獄
Try basic jailbreak techniques against a local model using Ollama, learning the difference between prompt injection and jailbreaking through hands-on experimentation.
實驗室: Basic 越獄 Techniques
Hands-on exploration of jailbreak techniques including role-play, DAN-style prompts, and academic framing against multiple models.
實驗室: Role-Play 攻擊s
Use persona-based approaches to bypass AI safety measures by assigning alternate identities, characters, and scenarios that override the model's trained refusal behaviors.
CTF: The 越獄 Gauntlet
A series of progressively harder jailbreak challenges where each level adds stronger defenses. Score points through technique diversity and creativity as you break through escalating safety layers.
實驗室: Build 越獄 Automation
Build an automated jailbreak testing framework that generates, mutates, and evaluates attack prompts at scale. Covers prompt mutation engines, success classifiers, and campaign management for systematic red team testing.
實驗室: Novel 越獄 Research
Systematic methodology for discovering new jailbreak techniques against large language models. Learn to identify unexplored attack surfaces, develop novel attack vectors, and validate findings with scientific rigor.
越獄 Portability
Analysis of which jailbreaks transfer across models and why, including universal vs model-specific techniques, transfer attack methodology, and factors that determine portability.
GPT-4 攻擊面
GPT-4 特有攻擊向量之完整分析，包括函式呼叫攻擊、視覺輸入攻擊、系統訊息階層濫用、結構化輸出操弄，以及已知 jailbreak 模式。
GPT-4 已知漏洞
已記錄之 GPT-4 漏洞——含 DAN jailbreak、資料擷取事件、系統提示洩漏、工具使用利用，以及微調安全移除。
多模態越獄技術
結合多模態途徑以繞過安全對齊，含圖像－文字組合攻擊、排字越獄、視覺思維鏈操弄，與多模態漸進技術。
攻擊s on Vision-Language 模型s
Comprehensive techniques for attacking vision-language models including GPT-4V, Claude vision, and Gemini, covering adversarial images, typographic exploits, and multimodal jailbreaks.
VLM 特有的越獄手法
利用視覺模態的越獄技術，包括影像─文字不一致攻擊、視覺安全繞過，以及跨模態越獄策略。
Few-Shot Manipulation
Using crafted in-context examples to steer model behavior, including many-shot jailbreaking, poisoned demonstrations, and example-based conditioning.
提示詞注入與越獄
提示詞注入的完整入門——大型語言模型應用程式中最根本的漏洞類別——以及它與越獄技術的關係。
越獄技術
繞過大型語言模型安全對齊的常見模式與進階技術，包含角色扮演、編碼技巧、多範例攻擊與基於梯度的方法。
Many-Shot 越獄ing
Power-law scaling of in-context jailbreaks: why 5 shots fail but 256 succeed, context window size as attack surface, and mitigations for long-context exploitation.
Role-Play 攻擊s
Establishing alternate personas or fictional scenarios that cause models to bypass safety training, including DAN variants, character hijacking, and narrative framing.
Social Engineering of AI
Manipulating AI systems through emotional appeals, authority claims, urgency framing, and social pressure tactics that exploit instruction-following tendencies.
Universal Adversarial Triggers
Discovering and deploying universal adversarial trigger sequences that reliably override safety alignment across multiple LLM families, including gradient-based search, transfer attacks, and defense evasion.
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.
Competition-Style 越獄 Techniques
導覽 of jailbreak techniques used in AI security competitions and CTF events.
Role-Play Injection
Using fictional scenarios, character role-play, and narrative framing to bypass LLM safety filters by having the model operate within a permissive fictional context.
Virtual Persona Creation
Creating persistent alternate personas within LLM conversations to bypass safety training, establishing character identities that override the model's default behavioral constraints.