# forensics
標記為「forensics」的 105 篇文章
Adversarial Input Forensics
Forensic techniques for identifying, reconstructing, and analyzing adversarial inputs used to manipulate AI system behavior.
AI Attack Timeline Reconstruction
Techniques for reconstructing the complete timeline of an AI attack from available evidence.
AI Incident Communication Procedures
Communication procedures during AI security incidents including internal escalation and external disclosure.
AI Incident Post-Mortem Template
Comprehensive post-mortem template for AI security incidents covering timeline, impact assessment, root cause, and remediation tracking.
AI Incident Triage Procedures
Standardized triage procedures for AI security incidents covering severity assessment, initial containment, and escalation decision-making.
AI Threat Hunting Techniques
Proactive threat hunting techniques for identifying ongoing attacks against AI systems.
Attack Attribution Techniques
Techniques for attributing AI attacks to specific actors including behavioral analysis, infrastructure tracking, and technique fingerprinting.
Chain of Custody for AI Evidence
Establishing and maintaining chain of custody for AI system evidence including model snapshots, interaction logs, and configuration records.
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.
Conversation Preservation
Preserving AI conversation evidence: interaction log capture, context window reconstruction, multi-turn conversation integrity, tool call chain preservation, and forensic timeline construction.
AI Evidence Preservation
Preserving forensic evidence from AI security incidents: model state snapshots, conversation and interaction preservation, embedding database captures, and chain-of-custody for AI-specific artifacts.
Model State Snapshots
Techniques for capturing and preserving AI model state during incident response: weight snapshots, configuration capture, behavioral fingerprinting, and model artifact integrity verification.
Forensic Tool Development for AI
Building custom forensic tools for AI-specific incident investigation and evidence analysis.
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.
AI System Log Analysis
AI system logging architecture for forensic investigation: inference logs, prompt and completion logs, tool call traces, embedding query logs, and logging infrastructure requirements.
Inference Log Analysis
Analyzing inference logs for AI forensic investigation: detecting anomalous patterns, identifying jailbreak attempts through metadata, token-level analysis, and latency anomaly detection.
Prompt Log Forensics
Forensic investigation of prompt and completion logs: reconstructing attack chains, identifying injection sources, correlating prompts with outcomes, and building attack timelines.
Log Analysis for Injection Detection
Analyzing application and model logs to detect prompt injection attacks including pattern matching, anomaly detection, and behavioral indicators.
Model Behavior Forensics
Forensic analysis of model behavior changes to detect potential compromise or manipulation.
Model Tampering Detection (Ai Forensics Ir)
Detecting unauthorized modifications to model weights, configurations, and serving infrastructure through integrity verification and behavioral analysis.
Prompt Injection Forensics
Forensic investigation techniques for prompt injection incidents including log analysis and payload reconstruction.
Root Cause Analysis for AI Failures
Conducting root cause analysis for AI system failures including distinguishing between attacks, bugs, and drift-related incidents.
Timeline Reconstruction Methodology
Systematic methodology for reconstructing attack timelines from AI system logs, API records, and model behavior observations.
Training Data Breach Forensics
Investigating training data breaches including data extraction evidence and membership inference indicators.
Practice Exam 2: Advanced AI Security
25-question advanced practice exam covering multimodal attacks, training pipeline security, cloud AI security, forensics, and governance.
AI Forensics Practice Exam
Practice exam on AI incident investigation, log analysis, attribution, and evidence preservation.
AI Forensics & IR Assessment
Assessment on AI incident investigation, evidence collection, prompt injection forensics, and response procedures.
AI Forensics Assessment
Test your knowledge of AI incident response, log analysis, evidence preservation, behavioral analysis, and forensic investigation techniques with 15 questions.
AI Incident Response Assessment
Assessment of AI-specific incident response procedures, forensics, and recovery capabilities.
Advanced AI Forensics Assessment
Advanced assessment on model behavior forensics, training data breach analysis, and attack attribution.
Skill Verification: AI Forensics
Practical verification of AI incident forensics including log analysis and attack reconstruction.
Skill Verification: AI Incident Response
Skill verification for AI-specific incident detection, analysis, containment, and recovery.
Skill Verification: AI Forensics Investigation
Hands-on verification of AI forensics investigation capabilities with simulated incident scenarios.
Advanced Topics Study Guide
Study guide covering AI security research techniques, automation, forensics, emerging attack vectors, and tool development for advanced practitioners.
Forensics and IR Study Guide
Study guide for AI forensics and incident response topics with scenario-based preparation.
AI Forensics Study Guide
Study guide for AI forensics assessments covering investigation techniques, evidence handling, and attribution.
Capstone: AI Incident Response Exercise
Respond to a simulated AI security incident through triage, investigation, containment, remediation, and post-mortem reporting.
Cloud AI Logging and Forensics
Setting up comprehensive logging and forensic capabilities for cloud-deployed AI systems.
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.
Tool Building Hackathon: Forensics Suite
Community hackathon building forensic analysis tools for AI incident investigation, including log parsers, timeline reconstructors, and attribution aids.
Lab: Backdoor Detection in Fine-Tuned Models
Analyze a fine-tuned language model to find and characterize an inserted backdoor, using behavioral probing, activation analysis, and statistical testing techniques.
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.
Forensics Detective Challenge
Analyze logs and artifacts from an AI security incident to reconstruct the attack chain and identify the attacker's technique.
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.
Evidence Collection & Chain of Custody
How to collect and preserve evidence during AI red team engagements: screenshots, API logs, reproducibility requirements, and chain-of-custody procedures.
AI Incident Response Checklist
Step-by-step checklist for responding to AI security incidents, from initial detection through containment, investigation, remediation, and post-incident review.
Evidence Handling Procedures
Proper procedures for collecting, documenting, and preserving evidence during AI red team engagements to ensure findings are defensible.
Secure Input/Output Logging for Defense
Implement secure logging for LLM input/output pairs that supports forensic analysis without exposing sensitive data.
Evidence Collection Methods for AI Red Teams
Comprehensive methods for collecting, preserving, and organizing red team evidence from AI system assessments, including API logs, screenshots, reproduction scripts, and chain-of-custody procedures.
Adversarial Input Forensics
Forensic techniques for identifying, reconstructing, and analyzing adversarial inputs used to manipulate AI system behavior.
AI 攻擊 Timeline Reconstruction
Techniques for reconstructing the complete timeline of an AI attack from available evidence.
AI Incident Communication Procedures
Communication procedures during AI security incidents including internal escalation and external disclosure.
AI Incident Post-Mortem Template
Comprehensive post-mortem template for AI security incidents covering timeline, impact assessment, root cause, and remediation tracking.
AI Incident Triage Procedures
Standardized triage procedures for AI security incidents covering severity assessment, initial containment, and escalation decision-making.
AI Threat Hunting Techniques
Proactive threat hunting techniques for identifying ongoing attacks against AI systems.
攻擊 Attribution Techniques
Techniques for attributing AI attacks to specific actors including behavioral analysis, infrastructure tracking, and technique fingerprinting.
Chain of Custody for AI Evidence
Establishing and maintaining chain of custody for AI system evidence including model snapshots, interaction logs, and configuration records.
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.
Conversation Preservation
Preserving AI conversation evidence: interaction log capture, context window reconstruction, multi-turn conversation integrity, tool call chain preservation, and forensic timeline construction.
AI 證據保存
自 AI 安全事件保存鑑識證據：模型狀態快照、對話與互動保存、embedding 資料庫捕獲，與為 AI 特定產物之監管鏈。
模型 State Snapshots
Techniques for capturing and preserving AI model state during incident response: weight snapshots, configuration capture, behavioral fingerprinting, and model artifact integrity verification.
Forensic 工具 Development for AI
Building custom forensic tools for AI-specific incident investigation and evidence analysis.
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。
AI 系統記錄分析
為鑑識調查之 AI 系統記錄架構：推論記錄、提示與 completion 記錄、工具呼叫軌跡、embedding 查詢記錄，與記錄基礎設施要求。
推論記錄分析
為 AI 鑑識調查分析推論記錄：偵測異常模式、經 metadata 辨識越獄嘗試、token 層級分析與延遲異常偵測。
Prompt Log Forensics
Forensic investigation of prompt and completion logs: reconstructing attack chains, identifying injection sources, correlating prompts with outcomes, and building attack timelines.
Log Analysis for Injection Detection
Analyzing application and model logs to detect prompt injection attacks including pattern matching, anomaly detection, and behavioral indicators.
模型 Behavior Forensics
Forensic analysis of model behavior changes to detect potential compromise or manipulation.
模型 Tampering Detection (Ai Forensics Ir)
Detecting unauthorized modifications to model weights, configurations, and serving infrastructure through integrity verification and behavioral analysis.
提示詞注入 Forensics
Forensic investigation techniques for prompt injection incidents including log analysis and payload reconstruction.
Root Cause Analysis for AI Failures
Conducting root cause analysis for AI system failures including distinguishing between attacks, bugs, and drift-related incidents.
Timeline Reconstruction Methodology
Systematic methodology for reconstructing attack timelines from AI system logs, API records, and model behavior observations.
訓練 Data Breach Forensics
Investigating training data breaches including data extraction evidence and membership inference indicators.
Practice Exam 2: 進階 AI 安全
25-question advanced practice exam covering multimodal attacks, training pipeline security, cloud AI security, forensics, and governance.
AI Forensics Practice Exam
Practice exam on AI incident investigation, log analysis, attribution, and evidence preservation.
AI Forensics & IR 評量
評量 on AI incident investigation, evidence collection, prompt injection forensics, and response procedures.
章節評量：AI 鑑識
15 題校準評量，測試你對 AI 鑑識與事件應變的理解——證據收集、日誌分析與模型行為調查。
AI Incident Response 評量
評量 of AI-specific incident response procedures, forensics, and recovery capabilities.
進階 AI Forensics 評量
進階 assessment on model behavior forensics, training data breach analysis, and attack attribution.
Skill Verification: AI Forensics
Practical verification of AI incident forensics including log analysis and attack reconstruction.
Skill Verification: AI Incident Response
Skill verification for AI-specific incident detection, analysis, containment, and recovery.
Skill Verification: AI Forensics Investigation
Hands-on verification of AI forensics investigation capabilities with simulated incident scenarios.
進階 Topics Study 指南
Study guide covering AI security research techniques, automation, forensics, emerging attack vectors, and tool development for advanced practitioners.
Forensics and IR Study 指南
Study guide for AI forensics and incident response topics with scenario-based preparation.
AI Forensics Study 指南
Study guide for AI forensics assessments covering investigation techniques, evidence handling, and attribution.
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。
Capstone: AI Incident Response 練習
Respond to a simulated AI security incident through triage, investigation, containment, remediation, and post-mortem reporting.
Cloud AI Logging and Forensics
Setting up comprehensive logging and forensic capabilities for cloud-deployed AI systems.
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.
工具 Building Hackathon: Forensics Suite
Community hackathon building forensic analysis tools for AI incident investigation, including log parsers, timeline reconstructors, and attribution aids.
實驗室: Backdoor Detection in Fine-Tuned 模型s
Analyze a fine-tuned language model to find and characterize an inserted backdoor, using behavioral probing, activation analysis, and statistical testing techniques.
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.
Forensics Detective Challenge
Analyze logs and artifacts from an AI security incident to reconstruct the attack chain and identify the attacker's technique.
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。
證據蒐集與保管鏈
在 AI 紅隊委任期間如何蒐集並保存證據：截圖、API 日誌、可重現性要求，以及保管鏈程序。
AI Incident Response Checklist
Step-by-step checklist for responding to AI security incidents, from initial detection through containment, investigation, remediation, and post-incident review.
Evidence Handling Procedures
Proper procedures for collecting, documenting, and preserving evidence during AI red team engagements to ensure findings are defensible.
Secure Input/Output Logging for 防禦
Implement secure logging for LLM input/output pairs that supports forensic analysis without exposing sensitive data.
Evidence Collection Methods for AI 紅隊s
Comprehensive methods for collecting, preserving, and organizing red team evidence from AI system assessments, including API logs, screenshots, reproduction scripts, and chain-of-custody procedures.