'guard' 검색 결과

Add checks to LLM prompts and responses using Llama Guard 3, NeMo Guardrails, and LLM Guard. Configure content screening, conversation boundaries, and sensitive-data filtering, then measure blocking and false-positive rates with labeled test prompts.

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• Adding runtime safety checks to LLM, RAG, or agent apps
• Screening prompts for injection and jailbreak attempts
• Filtering sensitive or toxic model responses

외부의 스마트폰이나 노트북에서 홈 네트워크에 접속하도록 WireGuard 서버 구축, 기기별 키 생성, 통신 경로 설정과 연결 장애 점검을 돕습니다.

• 외부에서 집의 NAS에 접속합니다
• WireGuard 서버를 구축합니다
• VPN을 거칠 통신을 선택합니다

Build safety checks for LLM prompts and responses using Meta's LlamaGuard moderation model. Classify potentially harmful content, block flagged messages, serve checks through an API, and integrate them with NeMo Guardrails.

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• Checking prompts before sending them to an LLM
• Filtering responses before displaying them
• Building a moderation API

Manage AdGuard Home network filtering from the command line. Update blocklists, register devices, configure DNS rules that map names to addresses, and inspect query logs and statistics through the AdGuard Home REST API.

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• Adding and refreshing ad-blocking filter lists
• Mapping local domain names to IP addresses
• Registering devices and managing fixed address assignments

Build automated responses to AWS GuardDuty security findings using EventBridge and Lambda. Configure severity filters, isolate EC2 instances, disable compromised access keys, preserve disk snapshots for investigation, and send notifications to security teams.

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• Triggering responses to high-severity GuardDuty findings
• Isolating EC2 instances and preserving investigation data
• Disabling compromised IAM access keys

Build safety checks for LLM prompts and responses using Meta's LlamaGuard. This skill covers content classification, batch moderation, serving a moderation API, and connecting checks to NeMo Guardrails.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Screening user prompts before sending them to an LLM
• Checking generated responses before displaying them
• Building an API for content safety checks

AI 에이전트의 위험한 명령을 실행 전에 확인하고 파일 편집을 지정 디렉터리로 제한합니다. 보호 모드를 따로 쓰거나 결합해 운영 작업과 자율 실행을 관리합니다.

• 운영 작업 중 위험한 명령 확인
• 특정 디렉터리로 편집 범위 제한
• 자율 실행 시 보호 모드 결합

Detect unauthorized cryptocurrency mining in cloud workloads by combining billing changes, CPU usage, mining pool connections, and process alerts. Follow workflows to preserve evidence, isolate affected resources, and trace how attackers gained access.

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• Investigating sudden cloud billing increases
• Responding to GuardDuty cryptocurrency alerts
• Checking containers for unauthorized mining processes

Set up and operate Amazon GuardDuty to detect threats in AWS. Configure protection features, centralize findings across accounts, investigate suspicious activity, and build automated responses with EventBridge and Lambda, including containment of potentially compromised resources.

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• Setting up threat detection for AWS accounts
• Investigating credential abuse and compromised instances
• Automating responses to high-severity findings

Build a screening pipeline for web pages, PDFs, and images before an AI agent reads them. Extract hidden text, check for malicious instructions with detection models, and record decisions to block or allow content.

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• Screening external content before an agent reads it
• Checking uploaded PDFs and images for hidden instructions
• Scanning documents before adding them to a knowledge base

Deploy Llama Guard, NeMo Guardrails, and LLM Guard input/output scanners as runtime defenses.

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Investigate suspected data theft from AWS S3 by comparing access logs, GuardDuty findings, and Macie classifications. Identify unusual bulk downloads and transfers across accounts, assess sensitive data exposure, and build automated detection rules.

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• Investigating unusual bulk downloads from S3
• Checking transfers to external AWS accounts
• Assessing exposure of sensitive data

Configure safety checks for LLM applications with NeMo Guardrails. Covers attempts to bypass safety rules, input and output validation, masking personal information, and checking factual claims through programmable rules and integrations.

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• Detecting instructions that try to bypass safety rules
• Masking personal information before processing
• Adding factual checks to generated answers

Add safety checks to LLM applications with NeMo Guardrails. This skill guides input and output validation, detection of attempts to bypass safety rules, personal information masking, and checks for unsupported or inaccurate answers.

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• Detect attempts to bypass an AI app's safety rules
• Mask personal information before processing
• Add fact-checking to generated answers

Implements input and output validation guardrails for LLM-powered applications to prevent prompt injection, data leakage, toxic content generation, and hallucinated outputs. Builds a security validation pipeline using NVIDIA NeMo Guardrails Colang definitions, custom Python validators for PII Tespit and content policy enforcement, and the Guardrails AI framework for structured output validation. The guardrails system intercepts both user inputs (blocking injection attempts, stripping PII, ...

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Inspect npm packages and dependency files for malicious installation scripts, credential theft, and self-spreading behavior. Use GuardDog scanning, source inspection, known-malicious version checks, and isolated execution to produce an evidence-based assessment.

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• Vetting a new npm dependency before adoption
• Reviewing dependency files during code review
• Checking lockfiles against known-malicious versions

Submit security scans for AI services, tools, skills, and agents through AI-Infra-Guard, then retrieve progress and results. It also supports jailbreak evaluation, testing whether prompts can bypass a model’s safety restrictions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Scanning AI services for known vulnerabilities
• Auditing MCP tools and skill source packages
• Checking agents from saved settings or YAML files

Claude의 파일별 첫 편집과 생성을 잠시 거부하는 훅을 설정합니다. 호출 위치, 데이터 구조, 사용자 요청을 확인하도록 하여 변경 전 조사를 작업에 포함합니다.

• 편집 전 영향을 받는 모듈 조사하기
• 변경 전 데이터 구조 확인하기
• 테스트와 생성 파일 제외 설정하기

가정이나 소규모 실험실의 네트워크를 변경하기 전에 장비 구성과 복구 절차를 정리합니다. VLAN 분리, DNS 필터링, VPN 도입을 단계적으로 계획하고 점검합니다.

• 일상 기기와 사물 인터넷·방문자 기기를 분리합니다
• DNS 필터링으로의 전환을 계획합니다
• VPN 원격 접속을 검토합니다

Set up encrypted connections between devices with Tailscale and control access by user or device. The skill covers network routing, device name resolution, and self-hosting the coordination server with Headscale.

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• Replacing a traditional VPN with Tailscale
• Restricting server access by team or device
• Connecting remotely to internal networks

Inspect suspicious Android APK files without running them. Analyze permissions, app components, suspicious code patterns, and network indicators, then produce a JSON risk report to support malware triage and detection rule development.

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• Triage suspicious APK files without running them
• Inspect dangerous permission combinations and suspicious API calls
• Extract network indicators for malware detection rules

Helps configure and troubleshoot Linux networks, including firewalls, routing, and DNS, which translates domain names into addresses. Covers traffic capture, isolated test networks, WireGuard VPN setup, and TCP tuning across physical servers, virtual machines, and containers.

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• Investigating connectivity and DNS failures
• Reviewing firewall rules and port forwarding
• Configuring routes based on traffic source

Analyze and enforce repository, runtime, data, spend, endpoint, and approval guardrails without guessing secret formats or content-policy rules. Use when the task requires adobe policy and execution guardrails. Trigger with "Adobe guardrails", "block unsafe Adobe calls", or "Adobe policy checks".

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How AgentGuards enforces its guardrails in Codex — fully automatic, via hooks, with nothing for you to call. Load this if asked how AgentGuards works, whether you need to screen something yourself, or why no agentguards check_input / authorize_action tools are available.

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