agent-bom runtime
AI Runtime Security Monitoring and Audit Log Correlation Analysis
Install & Use
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Help me install this AI Skill: agent-bom runtime. It is used for: AI Runtime Security Monitoring and Audit Log Correlation Analysis Full Skill content: https://321skill.com/skills/agent-bom-runtime-x-10/raw/index.md Read that page and install it.
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This Skill addresses the pain point of lacking security visibility during AI application runtime. In real-world deployments, an AI Agent's context graph, audit logs, and CVE vulnerabilities are often scattered across different systems, making manual correlation highly inefficient and obscuring hidden threats like lateral movement. It helps teams continuously monitor the security posture at runtime through context graph analysis, correlation of runtime audit logs with CVE discoveries, and vulnerability analytics queries.
Usage is straightforward. Simply install the agent-bom package via pipx, then invoke the three core tools through natural language instructions within your AI coding assistant: context_graph (builds the Agent context graph and analyzes lateral movement), runtime_correlate (cross-correlates audit logs with CVE data), and analytics_query (queries vulnerability trends, historical states, and runtime events). You can then directly ask the AI, "Analyze the context graph of the current scan results" or "Correlate proxy-audit.jsonl with CVE discoveries," and it will execute automatically and return the results.
It is well-suited for teams or individuals needing to monitor AI runtime security, especially operations engineers and security researchers who have deployed AI Agents and wish to audit their behavior or investigate potential vulnerabilities. For enterprises requiring compliance audits, security operations, or developers looking to integrate security monitoring into CI/CD pipelines, this tool can significantly reduce the manual analysis workload.
After installation, it is recommended to first familiarize yourself with the context graph structure using the context_graph tool, then gradually employ runtime_correlate for log correlation. Note that it primarily relies on user-provided audit log files (JSONL format) and in-memory scan results; it does not automatically discover files or initiate network requests. For persistent analysis, an optional ClickHouse connection can be configured, but it will never automatically infer or leak credentials.
Key Features
Unlike traditional static vulnerability scanners (e.g., Trivy), agent-bom-runtime focuses on analyzing the context graph and detecting lateral movement during AI Agent runtime, and directly correlates audit logs with CVE discoveries, rather than just performing static image scanning.
Limitations
Requires Python 3.11+. Optional dependencies include kubectl (for Kubernetes context) and ClickHouse (for analytical storage). It does not support automatic discovery of network endpoints or persistent storage unless the user proactively configures ClickHouse.
FAQ
Does it require internet access or API keys?
No, unless you configure the optional ClickHouse for persistent analysis. All operations are performed locally on in-memory data and user-provided log files.
Which operating systems are supported?
Supports macOS, Linux, and Windows via pipx or pip installation.
Installation guide for AI assistants
If your AI coding assistant (Claude Code, Cursor, TRAE etc.) can see this page, send it this message to auto-install:
Visit https://321skill.com/skills/agent-bom-runtime-x-10/raw/index.md to read the original Skill definition (Markdown format) for agent-bom runtime, and install it according to the instructions.
Raw Markdown URL for AI: /skills/agent-bom-runtime-x-10/raw/index.md