'data leak' 검색 결과

Conducts privacy auditing of AI models including training data extraction testing, membership inference attacks, model inversion testing, and attribute inference assessment. Uses ML Privacy Meter and related tools to quantify privacy leakage. Keywords: model audit, membership inference, privacy meter, model inversion, training data extraction.

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Instruments code to track the flow of untrusted or sensitive data at runtime, enabling detection of injection vulnerabilities, data leaks, and privilege violations. Use when users need to: (1) Track untrusted input propagation through code, (2) Detect SQL injection, XSS, or command injection vulnerabilities, (3) Identify sensitive data leaks, (4) Monitor privilege escalation paths, (5) Perform dynamic taint analysis for security testing. Supports Python, Java, JavaScript, and C/C++ with configurable taint sources and sinks.

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Supabase security expert to audit RLS (Row Level Security), RBAC, relational databases, prevent data leakage, and utilize Supabase Linter / Ahli keamanan Supabase untuk audit RLS (Row Level Security), RBAC, database relasional, pencegahan kebocoran data, dan pemanfaatan Supabase Linter.

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Firebase security expert to audit Security Rules (Firestore/Realtime Database/Storage), authentication, API keys, data leakage prevention, and App Check configuration / Ahli keamanan Firebase untuk audit Security Rules (Firestore/Realtime Database/Storage), autentikasi, API keys, pencegahan kebocoran data, dan konfigurasi App Check.

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Audit existing model training code — find reproducibility issues, data leakage, and missing best practices. Use when asked to "audit our training code", "is our training reproducible", or "check for training data leakage".

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Use Therapeutics Data Commons through the PyTDC Python package for registry discovery, approved dataset access, task-aware splits (scaffold, cold-start, temporal, combination), evaluator metrics, benchmark groups, and bounded molecular-oracle workflows. Use this skill to find which TDC datasets exist for a therapeutic task, load them with a split that does not leak, score predictions with the task's own official metric rather than a generic one, and run benchmark groups reproducibly. Also trigger on PyTDC, Therapeutics Data Commons, tdc.single_pred, tdc.multi_pred, ADMET Benchmark Group, scaffold split, get_split, or molecular oracles such as GSK3B, JNK3 and DRD2.

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Decide what to PERSIST in your data structures (graph nodes, DB tables, cache layouts) based on the QUERIES you must answer, not the DEFINITIONS that justify the data. Definitions tell you logical identity; storage strategy is independent. Materializing every definitional component leads to O(N×M) blow-ups that masquerade as memory leaks. Use when: (1) designing a new graph-node type, edge type, DB table, or cache layout; (2) about to name a `subgraph` / `comprises` / `members` / `contains` collection on a parent entity; (3) memory growth proportional to (entities × per-entity-elements); (4) "memory leak" debugging that survives multiple targeted patches — the leak may be storage shape, not retention; (5) pre-implementation sketch includes "for each X, store all reached nodes"; (6) research/spec doc says "X = ⟨A, B, C⟩" and you're tempted to make it three storage edges per X.

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Monitors dark web forums, marketplaces, paste sites, and ransomware leak sites for mentions of organizational assets, leaked credentials, threatened attacks, and threat actor communications to provide early warning intelligence. Use when establishing dark web monitoring coverage, investigating specific data breach claims, or enriching incident investigations with dark web context. Activates for requests involving dark web OSINT, leak site monitoring, credential exposure, Recorded Future dark web, or Tor hidden service intelligence.

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- Check and interpret data-breach exposure for an email, username, phone or name using Have I Been Pwned, the Pwned Passwords k-anonymity range API, DeHashed, IntelX and Snusbase. Use when checking breach or leak exposure, finding which services an account was registered with, interpreting a combolist or credential dump, assessing credential compromise, or auditing your own leaked personal data

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머신러닝 모델의 운영 적용을 위해 데이터 규격, 재현 가능한 학습, 품질 평가, 배포, 모니터링과 롤백을 정리하고 구현 계획과 검토 항목을 만듭니다.

• 실험 코드를 학습 파이프라인으로 만듭니다
• 모델 공개 전 품질 기준을 정합니다
• 데이터 누출과 전처리 차이를 조사합니다

Create, edit, and clean spreadsheet files, including Excel workbooks and CSV data. Build formulas and formatting with openpyxl, a Python library, then recalculate and validate workbooks to check for formula errors and file problems.

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• Create a workbook with formulas and formatting
• Edit an existing spreadsheet
• Restructure messy CSV or TSV data

Search contacts and calendar events already stored in Muse when a paired device is unavailable. Retrieve relevant records, check whether results are complete, or delete Muse's local contact or calendar copies without changing data on the devices.

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• Find saved contacts while a device is offline
• Check cached calendar events for a date range
• Search contacts by name, phone number, or email

Build and check clinical machine learning workflows with PyHealth, from loading electronic health records, signals, or images to training and evaluation. Includes patient-level data splits, prediction task checks, model checkpoints, and medical code mapping.

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• Prototype mortality and readmission prediction models
• Check patient splits and data leakage
• Train and evaluate sleep staging models

Measure Python execution time and memory use to locate slow or resource-heavy code. Use profiling results to guide improvements in data structures, caching, input and output operations, algorithms, and parallel processing.

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• Finding slow functions in Python applications
• Reducing memory use and investigating leaks
• Improving data processing and database queries

Analyze proxy, DNS, and firewall traffic logs to uncover unapproved SaaS and cloud services. Group usage by domain, compare it with approved applications, and produce a report with traffic volumes, user counts, risk scores, and remediation recommendations.

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• Finding unapproved SaaS and cloud services
• Measuring traffic volumes and users by service
• Prioritizing services by potential data leakage risk

Investigate suspicious mobile apps by examining permissions, network traffic, and runtime behavior. Use observed evidence to identify signs of data theft, SMS interception, credential stealing, or communication with attacker control servers, then classify the suspected malware.

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• Triaging suspicious APK or IPA samples
• Investigating possible data leaks from mobile apps
• Checking for SMS interception and credential theft

Map an organization's internet-facing assets using public information, including domains, indexed services, employee details, and leaked data. Compile findings into a reconnaissance report to support authorized security assessments and identify potential exposure.

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• Gathering public information before a penetration test
• Finding unknown internet-facing assets
• Checking public sources for leaked credentials and documents

Write and review .NET calls to native C/C++ libraries using P/Invoke. Covers function declarations, string conversion, memory ownership, and platform differences to help investigate crashes, memory leaks, and corrupted data.

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• Create .NET declarations from C/C++ headers
• Investigate crashes and memory leaks in native calls
• Review string encoding and memory ownership

Test LLM applications for malicious instructions, information leaks, and unsafe tool use. The skill runs attack cases and checks observable evidence, helping distinguish suspicious replies from reproducible failures with concrete impact.

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• Test chat APIs for prompt injection
• Check document-based AI for hidden instructions
• Verify unsafe tool calls by an AI agent

Assess Kubernetes and Docker security across exposed APIs, access permissions, service account tokens, and container boundaries. Use actual data reads or command output to establish impact and distinguish information exposure from broader compromise.

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• Reviewing exposed Kubernetes APIs and kubelet ports
• Checking RBAC permissions and service account tokens
• Assessing etcd and Dashboard data exposure

Assess GraphQL APIs in financial services for unauthorized transfers, rounding errors, duplicate transactions, and exposed personal data. The skill guides checks of account permissions and ledger changes to distinguish actual financial impact from misleading success responses.

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• Reviewing transfer and withdrawal permissions
• Checking rounding and duplicate transaction handling
• Inspecting access to identity verification data

Investigate LDAP and XPath injection in directory searches and XML-backed authentication. The skill guides checks for login bypasses and information leaks, uses control responses to reduce false positives, and distinguishes Active Directory from other LDAP directories.

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• Reviewing directory-backed corporate login
• Testing employee and address-book searches
• Checking authentication backed by XML data

Optimize Python code for reduced memory usage and improved memory efficiency. Use when asked to reduce memory footprint, fix memory leaks, optimize data structures for memory, handle large datasets efficiently, or diagnose memory issues. Covers object sizing, generator patterns, efficient data structures, and memory profiling strategies.

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Think and work like an expert AI Researcher. Use when a task calls for AI Researcher judgment. Reasons from data generating processes, inductive biases, and compute-data-algorithm trade-offs through train/val/test discipline, seed sweeps, ablation ladders, and standards like NeurIPS reproducibility checklists, model cards, and lm-eval-harness, while treating data leakage (Kapoor & Narayanan taxonomy), benchmark contamination, spurious correlations, and reward-model overoptimization as first-class failure modes.

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