NVIDIA/SkillSpector
SkillSpector
SkillSpector is a security scanner designed to detect vulnerabilities and malicious patterns in AI agent plugins and extensions before they are installed. It functions as a runtime guardrail that calculates numeric risk scores and assigns severity labels to provide installation recommendations or block risky external extensions.
The project distinguishes itself by using language models to perform semantic code analysis, evaluating code intent and context to reduce false positives. It also employs fingerprint-based issue suppression to track and ignore previously accepted risks across repeated scan cycles.
The tool covers software supply chain security through dependency scanning against public security databases and supports multi-source asset ingestion from local directories, remote URLs, and repositories. It provides vulnerability reporting in multiple machine-readable and human-readable formats for integration into CI/CD pipelines.
Features
- AI Agent Vulnerability Scanners - Analyzes AI agent plugins and extensions to identify malicious patterns and security vulnerabilities before installation.
- LLM-Based Analysis - Uses language models to perform semantic code analysis and evaluate intent to reduce false positives in vulnerability detection.
- LLM-Powered Semantic Analyzers - Uses a language model to evaluate code semantics and reduce false positives during security analysis.
- Security Intent Analysis - Uses large language models to evaluate code intent and context, reducing false positives in security detection.
- CVE Dependency Scanners - Identifies known vulnerabilities in the skill dependency tree by querying public security databases.
- Dependency Vulnerability Scanners - Identifies known vulnerabilities in third-party dependencies by querying public security databases.
- Security Guardrails - Provides runtime guardrails that block the installation of risky AI skills based on security risk scores.
- AI Agent Scanners - Serves as a specialized security scanner for detecting vulnerabilities and malicious patterns in AI agent skills.
- Software Supply Chain Security - Secures the AI plugin supply chain by scanning external dependencies and remote repositories for known CVEs.
- Asset Risk Scoring - Calculates numeric risk scores and severity levels for AI plugins to provide installation recommendations.
- Vulnerability Dependency Mapping - Implements the process of mapping a plugin's dependency tree against public security databases to identify known vulnerabilities.
- Static Code Analysis - Performs static analysis of plugin source code using LLMs to detect security risks without executing the code.
- Fingerprint-Based Suppressions - Employs unique hashes to track and suppress previously accepted security risks across repeated scan cycles.
- Multi-Source Asset Ingestion - Retrieves target code from local directories, remote URLs, or repositories for unified security processing.
- File and URL Scans - Inspects files from remote URLs, repositories, and local directories to identify security risks in AI skills.
- AI Application Security - Security scanner for detecting malicious patterns in AI agent skills.
