'data-poisoning' 검색 결과

Simulate supply chain and adversarial machine learning attacks by injecting poisoned data or targeted backdoors into training and fine-tuning datasets. Use this skill when assessing the integrity controls of MLOps pipelines or evaluating the resilience of AI models against highly targeted, stealthy manipulation intended to alter model behavior on specific triggers.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Execute and analyze AI Data Poisoning attacks. By subtly injecting malicious or targeted misinformation into an LLM's training or fine-tuning dataset, an attacker can covertly manipulate the model's future outputs, implant backdoors, or enforce biases without altering the model architecture.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Identify and simulate Data Poisoning attacks aimed at degrading or skewing an AI model's accuracy. This skill focuses on Adversarial Machine Learning concepts where attackers inject malicious or mislabelled data points into training or fine-tuning datasets (e.g., feedback loops) to bias the AI.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Check training data and machine learning models for malicious changes, including backdoors that cause unexpected behavior on specific inputs. Combine data quality checks, model analysis, and file integrity verification, then document findings and retest after remediation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Screening third-party data before training
• Checking downloaded model weights before deployment
• Investigating unexpected behavior on specific inputs

Assess the security of RAG systems, which retrieve documents to support AI answers. Test whether stored vectors expose source text, leak documents between tenants, or allow inserted content to dominate search results, then document findings and controls.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assessing source-text exposure from stored vectors
• Checking data isolation between tenants
• Testing retrieval manipulation in a test collection

This skill should be used when the user asks to "scan AI systems for security threats", "check for prompt injection vulnerabilities", "assess model security posture", "detect data poisoning risks", or "audit AI/ML pipeline security".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Exploit AI applications using Indirect Prompt Injection. This skill focuses on hiding malicious instructions within data sources (web pages, documents, emails) that the LLM processes, causing the AI to execute unintended actions or leak data without direct user interaction.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Test for indirect prompt injection vulnerabilities where malicious instructions are injected through external data sources (websites, emails, documents, database records) that the LLM processes. Use this skill when assessing LLM-integrated applications that process user-generated content, retrieve web pages, parse emails, or read documents. Covers injection via web content, email bodies, user profiles, and database records that are fed to LLM context.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Identify poisoned training data and backdoored models across the ML pipeline.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Test vector stores for embedding inversion, cross-tenant leakage, and poisoning.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.