Analyzes LLM applications for vulnerabilities across the ten OWASP AI categories.
npx skills add https://github.com/do360now/security-agents --skill llm-top-10
Please help me install this Agent Skill. Skill: llm-top-10 Source: https://github.com/do360now/security-agents/tree/main/.claude/skills/llm-top-10 Command: npx skills add https://github.com/do360now/security-agents --skill llm-top-10
This Skill enables systematic security evaluation of AI applications leveraging large language models, focusing on identifying vulnerabilities and risks.
Review the SKILL.md to understand the ten OWASP categories, then execute analysis routines on your codebase or infrastructure setup to identify gaps.
High-intent search queries and answers about installing and using this skill.
To identify data leakage in LLM systems, perform automated security analysis on data handling and vector store configurations. This review maps data flows against OWASP Top 10 categories to detect leakage paths and enforce best practices for sensitive information protection.
Checking vector store security in AI applications involves examining infrastructure configurations for poisoning risks and unauthorized access. A systematic review identifies vulnerabilities in vector store integration and supply chain dependencies, ensuring robust data protection.
Yes, an automated security review covers OWASP Top 10 risks for LLM systems. It systematically evaluates codebase, architecture, and dependencies to detect vulnerabilities like prompt injection, data leakage, and output misuse while enforcing best practices.
Perform a security audit on your AI codebase for output misuse by analyzing sanitization routines and application logic. This review identifies potential output manipulation vulnerabilities and provides actionable remediation insights to secure LLM responses.
Yes, you can assess dependency safety and supply chain risks for ML models. The review evaluates external libraries and model sources against OWASP criteria to detect poisoning and supply chain vulnerabilities, ensuring secure integration.
Your LLM application needs an architecture security evaluation to uncover systemic risks across data handling and model interactions. Reviewing architecture identifies gaps in configurations and enforces best practices, mitigating vulnerabilities before deployment.
J4X-SecurityConsolidate security audit findings into a deduplicated known-issues.json register.
zapabobScan dependencies and custom code for security vulnerabilities.
sadie100Audit infrastructure, applications, and development processes for security vulnerabilities.
hoanghn61Test LLM applications for OWASP LLM Top 10 vulnerabilities.
