Skill UI

A comprehensive guide detailing security patterns and audit checklists for Automated Market Maker (AMM) smart contracts in DeFi. Covers critical vulnerabilities such as flash loans, slippage, reentrancy attacks, price manipulation, and integer overflow/underflow. Essential for reviewing major DeFi protocols like Uniswap.

This tool performs comprehensive safety scanning on untrusted inputs, such as user submissions, API payloads, or webhook data. It detects sophisticated threats like prompt injection, jailbreak attempts, and adversarial attacks using advanced AI defense mechanisms, ensuring data integrity and security before model processing or execution.

This comprehensive skill provides best practices for secure coding across multiple domains. It guides developers on managing secrets, validating user input, preventing SQL injection, implementing robust authentication and authorization (including RLS), and mitigating XSS attacks. Use this when handling sensitive data, APIs, or user-generated content to ensure the highest level of security.

This example demonstrates unsafe or malicious review data inputs, specifically designed for calibration and security testing. It simulates prompt injection attacks and must only be used in controlled environments to validate secure data processing and system robustness.

A comprehensive guide for researchers detailing how to strengthen the reproducibility of security claims for top conferences like CCS. It covers mapping every security claim (attacks, defenses, measurements) to verifiable evidence, managing artifact availability when sharing is restricted, and defining the achievable degree of reproduction (Turnkey, Scripted, Descriptive). This ensures academic rigor and transparency in published security work.

Provides comprehensive guidelines for researchers packaging and releasing artifacts (data, code, instruments) derived from real communities in CSCW research. It emphasizes ethical constraints, de-identification techniques (such as quote search and join attacks), and best practices for protecting studied communities' privacy, ensuring responsible sharing.

This guide provides a strategic framework for authors responding to rigorous peer reviews in high-stakes academic venues. It details how to manage both short-term rebuttals and full revision submissions, covering objection triage (misreading, evidence gaps, adaptive attacks), precise scope definitions, and the formal documentation required for ethics and methodology. Learn to structure your response to maximize acceptance probability.

A comprehensive guide for authors targeting the International Symposium on Research in Attacks, Intrusions and Defenses (RAID). This skill helps authors optimize their paper's positioning, strengthen the evidence chain (focusing on operational relevance and realistic evaluation), and structure submissions to meet the rigorous standards of top-tier cybersecurity conferences, thereby maximizing acceptance chances. It covers scope alignment, contribution types, and structural best practices.

A comprehensive guide for strengthening causal claims in corporate and empirical finance research. This resource details advanced econometric methods—including IV, DiD, RDD, and natural experiments—and provides strategies to preempt common methodological attacks (e.g., endogeneity, parallel-trends violations) required by top-tier academic journals like the Journal of Finance.

This guide provides a comprehensive framework for authors responding to peer reviews in major security conferences. It details how to triage reviewers' comments, structure technical responses (pointing to evidence, not arguing), handle complex objections like adaptive attacks and ethics, and effectively participate in the interactive discussion phase to refine the paper.

A comprehensive guide detailing the high standards required for presenting security evidence at major conferences. It covers methodologies for proving claims—including attacks, defenses, and detection systems—across live internet measurements, controlled testbeds, and emulation. Key requirements include adaptive adversary modeling, rigorous measurement hygiene, and adherence to ethical review protocols to ensure results are reproducible and relevant to real-world networks.

This skill calculates and documents the residual re-identification risk of de-identified datasets. It goes beyond simple direct identifier removal by assessing the risk posed by quasi-identifiers (age, ZIP, dates). It computes structural metrics like k-anonymity and l-diversity, and runs empirical linkage attacks to provide a defensible memo supporting HIPAA Expert Determination for determining if a dataset is safe for release.