# investigation
標記為「investigation」的 32 篇文章
Attack Attribution Techniques
Techniques for attributing AI attacks to specific actors including behavioral analysis, infrastructure tracking, and technique fingerprinting.
Attribution of AI Attacks
Techniques for attributing AI attacks to threat actors based on attack patterns and indicators.
Cross-System Attack Correlation
Correlating attack indicators across multiple AI systems and traditional IT infrastructure to identify coordinated campaigns and lateral movement.
Data Breach Investigation for AI Systems
Investigating data breaches involving AI systems including training data exposure, model memorization exploitation, and embedding inversion attacks.
Evidence Analysis Techniques for AI Incidents
Advanced techniques for analyzing evidence from AI security incidents including log correlation, model behavior analysis, and artifact examination.
Forensic Tooling for AI Systems
Overview of forensic tools and techniques specifically designed for AI system investigation including model analyzers, log parsers, and behavior profilers.
AI Forensics & Incident Response
Overview of forensic investigation and incident response for AI systems: why traditional IR falls short, the AI incident lifecycle, and the unique challenges of non-deterministic systems.
Prompt Log Forensics
Forensic investigation of prompt and completion logs: reconstructing attack chains, identifying injection sources, correlating prompts with outcomes, and building attack timelines.
Tool Call Forensics
Forensic investigation of agent tool calls: detecting unauthorized tool usage, analyzing parameter manipulation evidence, identifying exfiltration traces, and reconstructing agent action chains.
Model Behavior Forensics (Ai Forensics Ir)
Overview of model forensics: determining if a model has been tampered with, behavioral analysis methodology, and the relationship between model artifacts and observable behavior.
Prompt Injection Forensics
Forensic investigation techniques for prompt injection incidents including log analysis and payload reconstruction.
September 2026: Incident Response Challenge
Investigate simulated AI security incidents from logs, artifacts, and system traces. Reconstruct attack timelines, identify root causes, and write incident reports.
Lab: AI Incident Investigation
Investigate logs and artifacts from a compromised AI system to reconstruct the attack chain, identify the vulnerability exploited, and determine the scope of the breach.
CTF: AI Forensics Investigation
Analyze logs, model outputs, and system artifacts to reconstruct an AI security incident. Develop forensic analysis skills for AI-specific attack patterns, data exfiltration traces, and adversarial prompt detection.
Simulation: AI Supply Chain Attack Investigation
Investigate and respond to a supply chain compromise affecting an AI system's model weights, training data pipeline, and third-party dependencies.
Incident Response Playbook for AI Security Breaches
Walkthrough for building an incident response playbook tailored to AI security breaches, covering detection triggers, triage procedures, containment strategies, investigation workflows, remediation validation, and post-incident review processes.
攻擊 Attribution Techniques
Techniques for attributing AI attacks to specific actors including behavioral analysis, infrastructure tracking, and technique fingerprinting.
Attribution of AI 攻擊s
Techniques for attributing AI attacks to threat actors based on attack patterns and indicators.
Cross-System 攻擊 Correlation
Correlating attack indicators across multiple AI systems and traditional IT infrastructure to identify coordinated campaigns and lateral movement.
Data Breach Investigation for AI Systems
Investigating data breaches involving AI systems including training data exposure, model memorization exploitation, and embedding inversion attacks.
Evidence Analysis Techniques for AI Incidents
進階 techniques for analyzing evidence from AI security incidents including log correlation, model behavior analysis, and artifact examination.
Forensic 工具ing for AI Systems
概覽 of forensic tools and techniques specifically designed for AI system investigation including model analyzers, log parsers, and behavior profilers.
AI 鑑識與事件應變
AI 系統鑑識調查與事件應變的概覽：為何傳統 IR 不足、AI 事件生命週期，以及非決定性系統的獨特挑戰。
Prompt Log Forensics
Forensic investigation of prompt and completion logs: reconstructing attack chains, identifying injection sources, correlating prompts with outcomes, and building attack timelines.
工具呼叫鑑識
代理工具呼叫之鑑識調查：偵測未授權工具使用、分析參數操弄證據、辨識外洩痕跡，並重建代理動作鏈。
提示詞注入 Forensics
Forensic investigation techniques for prompt injection incidents including log analysis and payload reconstruction.
LLM 鑑識：事件應變者入門
LLM 安全事件鑑識調查入門——涵蓋證據收集、日誌分析、攻擊重建、模型行為分析與鑑識工具。
September 2026: Incident Response Challenge
Investigate simulated AI security incidents from logs, artifacts, and system traces. Reconstruct attack timelines, identify root causes, and write incident reports.
實驗室: AI Incident Investigation
Investigate logs and artifacts from a compromised AI system to reconstruct the attack chain, identify the vulnerability exploited, and determine the scope of the breach.
CTF：AI 鑑識調查
分析日誌、模型輸出與系統產物以重建 AI 安全事件。發展針對 AI 特有攻擊模式、資料外洩痕跡，與對抗提示偵測之鑑識分析技能。
模擬：AI 供應鏈攻擊調查
調查並回應影響 AI 系統之模型權重、訓練資料管線與第三方依賴之供應鏈受損。
Incident Response Playbook for AI 安全 Breaches
導覽 for building an incident response playbook tailored to AI security breaches, covering detection triggers, triage procedures, containment strategies, investigation workflows, remediation validation, and post-incident review processes.