Skill UI

Stress-tests a research paper against the strongest possible rejection argument. It executes a two-stage process: first, generating a single, devastating rejection memo (the 'attack'), and second, having a fresh reviewer defend the paper point-by-point to surface unresolved critical issues. This tool is invaluable for pre-submission quality assurance, especially for theory papers.

A comprehensive guide detailing advanced security threats in Machine Learning and AI systems. Covers model supply chain attacks (RCE via pickle, poisoning), adversarial examples (FGSM, PGD, physical-world), data privacy breaches, and LLM-specific vulnerabilities. Essential for building robust, secure AI pipelines.

This skill outlines the implementation of advanced runtime defenses for production Large Language Model (LLM) applications. It covers using multiple industry-leading guardrail systems—including Llama Guard, NeMo Guardrails, and LLM Guard—to detect and block adversarial attacks such as prompt injection, jailbreaks, and toxic content. It provides a comprehensive defense-in-depth strategy for ensuring model safety and output integrity in real-world deployments.

PyRIT (Python Risk Identification Tool) is an open-source automation framework designed for advanced, multi-turn adversarial red-teaming of conversational LLMs. It simulates real-world attack scenarios by orchestrating an attacker model and a scorer model in a feedback loop. Features include Crescendo (gradual escalation) and Tree-of-Attacks with Pruning (TAP) techniques to detect complex vulnerabilities like prompt injection and jailbreaks in stateful dialogues.

Use this skill to pressure-test and de-risk a business or creative idea before committing time and capital. It simulates an adversarial panel of five independent experts—The Critic, The Champion, The Analyst, The Investigator, and The Customer—to attack, support, and validate your concept from every possible angle. The final Judge provides a decisive GO/RESHAPE/KILL verdict.

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.