'payload' 검색 결과

WAF bypass techniques checklist: encoding bypass (URL/HTML/Unicode/double encoding), case variation, comment injection, HTTP header manipulation, chunked encoding, IP rotation, timing attacks, and payload obfuscation per WAF vendor. Use when WAF is blocking payloads during web app tests.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Security payloads, bypass tables, wordlists, gf pattern names, always-rejected bug list, and conditionally-valid-with-chain table. Use when you need specific payloads for XSS/SSRF/SQLi/XXE/NoSQLi/command injection/SSTI/IDOR/path-traversal/HTTP smuggling/WebSocket/MFA bypass, or bypass techniques. Submittability and the always-rejected / what-NOT-to-submit decision are owned by triage-validation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

OpenClaw의 Docker 환경에서 전체 동작을 확인하는 테스트를 작성하고 수정합니다. 모의 서버와 실제 서비스로 전송 내용, 이미지 이해, 최종 응답을 검증합니다.

• Docker 종단 간 테스트 추가 및 수정
• 의존 서비스로 전송한 요청 내용 확인
• 실제 모델 제공 서비스의 동작 검증

Read and update application settings through APIs, including system prompts, reasoning limits, IP filters, and login requirements. Also manage proxy and cache settings, with guidance on how reasoning settings affect requests sent to AI providers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review and update system prompts
• Adjust reasoning request settings
• Manage IP filters and login requirements

Review forms, API requests, and browser storage to find personal data that no active feature needs. Identify unnecessary collection, suggest removal or anonymisation, and check analytics and logs for exposed personal identifiers and missing retention periods.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing personal data collected by forms
• Finding personal identifiers in analytics and logs
• Reviewing browser storage and retention periods

Review AI skills and bundles without running their code. Checks for data leaks, suspicious permissions, hidden code, and system changes across desktop and mobile platforms, then reports flagged actions, risk scores, and suggested mitigations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review an AI skill before installing it
• Check a skill bundle for data leaks and suspicious actions
• Inspect Android and iOS threats in skill files

Store and retrieve execution output, model responses, reports, and operation receipts in YYLO Ledger. Choose storage and retention settings, attach provenance describing the source, and verify immutable evidence using record identifiers and content digests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preserving test reports and execution output
• Recording AI responses with their source information
• Storing operational plans and contracts as evidence

Solve sandboxed CTF security challenges by reconstructing encoding and encryption steps, extracting data hidden in media, and tracing mobile app signing or storage behavior. Preserve exact parameters and evidence to reproduce decoded content or signed requests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reconstructing encoding and encryption chains in CTF challenges
• Extracting data hidden in images and other media
• Tracing APK or IPA request-signing logic

Trace how requests become rendered pages in a CTF sandbox. Compare display restrictions with server enforcement, inspect data passed to the browser, and preserve a minimal example that demonstrates where rendering behavior diverges.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Trace routes through server rendering
• Inspect page data passed to the browser
• Compare display restrictions with handler enforcement

Organize authorized bug bounty investigations from scope review through reconnaissance, asset discovery, vulnerability testing, and reporting. Use vulnerability-specific guides to prioritize checks and prepare reproducible findings with evidence, impact, and remediation advice.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review a bug bounty program's scope and testing rules
• Gather public information and discover target assets
• Plan API authorization and business logic checks

Analyze DNS logs to find signs of hidden data transfers. Use query length, character randomness, traffic volume, and response sizes to identify suspicious domains and support monitoring rules with Python, Suricata, and Splunk examples.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating possible data theft through DNS traffic
• Ranking suspicious domains found in DNS logs
• Creating DNS tunneling detection rules

Investigate Windows malware that runs in memory or abuses legitimate system tools. Use logs, registry entries, and memory dumps to trace persistence, identify suspicious activity, and produce detection rules and an analysis report.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating suspicious PowerShell activity
• Checking WMI persistence mechanisms
• Finding malicious code in memory dumps

Screen AI application inputs and historical logs for prompt injection, where malicious instructions try to redirect the model. Combine pattern matching, text structure scoring, and a DeBERTa classifier to flag suspicious content and produce JSON results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Screening chatbot and AI agent inputs
• Checking external text used in retrieval-based AI applications
• Auditing historical interaction logs for injection attempts

Plan authorized spearphishing simulations, personalized phishing tests aimed at specific people. Prepare scenarios and email infrastructure, track delivery and participant responses, and document findings to assess email security controls and improve security awareness.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing authorized phishing simulations for specific personnel
• Preparing test email delivery infrastructure
• Measuring delivery, opens, clicks, and reporting behavior

Inspect suspicious PDFs without opening them in a reader. Use PDFiD, pdf-parser, and peepdf to examine internal objects, extract scripts, embedded files, and URLs, and document how the suspected attack works.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Triage suspicious PDF email attachments
• Investigate embedded JavaScript and automatic actions
• Extract embedded files and external URLs

Search Windows PowerShell logs for traces of Empire, a framework used after a system is compromised. Identify characteristic launch commands, encoded code, module calls, and communication paths, then report suspicious events with timelines and severity scores.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating suspected Empire activity
• Checking encoded commands and communication paths
• Building detection rules from Empire indicators

Analyze Windows PowerShell event logs to reconstruct scripts split across records and identify encoded commands, suspicious downloads, and security scanning bypass attempts. Produce a report with recovered scripts, risk scores, and mappings to documented attack techniques.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating suspicious PowerShell activity
• Reconstructing scripts split across log records
• Finding encoded commands and suspicious downloads

Examine suspicious Word, Excel, and PowerPoint documents by extracting macros and untangling code designed to hide its purpose. Identify download addresses, execution triggers, and attack steps, then document findings and indicators for detecting related threats.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating suspicious email attachments
• Finding download addresses hidden in VBA code
• Examining legacy Excel 4.0 macros

Analyze malware network captures to investigate command-and-control traffic, data theft, and downloaded files. Use packet analysis tools to identify suspicious destinations and recurring connections, extract artifacts, and create detection rules from observed patterns.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Analyzing malware sandbox network captures
• Investigating command-and-control traffic
• Tracing data theft and file downloads

Research malware families through Malpedia, connecting aliases, associated threat groups, and relationships between malware that loads other malware. Collect YARA rules, patterns used to identify malware, and organize references to support investigations and detection work.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Resolving different vendor names for the same malware
• Mapping malware associated with a threat group
• Documenting chains in which malware loads other malware

Create fillable Word forms with text fields, date controls, dropdowns, and clickable checkboxes. Protect static document content while leaving designated fields editable, or prepare mail-merge placeholders for templates that receive data later.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating protected employee onboarding forms
• Building surveys with dropdowns and checkboxes
• Preparing contract templates with editable fields

Refresh installed NanoClaw channel and agent provider code from its registry branches. Update all integrations or a selected subset, check a structured result report, and validate the updated installation with builds and tests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Refreshing integrations after a NanoClaw update
• Updating selected channels or agent providers
• Updating from a fork with an upstream remote

Plan a Claude Managed Agent around one clear job, then route work through setup, launch preparation, evaluation, and scheduling. Produce execution plans and curl commands that users run with their own Anthropic API key.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Defining one clear job for a managed agent
• Preparing launch commands for an Anthropic account
• Planning evaluation and revision with an iteration limit

Evaluate a Claude Managed Agent against a written rubric, a set of checkable criteria, and guide improvements within a fixed attempt limit. After a version passes, prepare separate evaluation cases to check performance beyond the examples used for revision.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Defining checkable criteria for agent outputs
• Improving an agent within a fixed attempt limit
• Testing a passing version on unseen cases