agent-bom runtime

This Skill addresses the pain points of runtime security monitoring for AI agents. In practical development, AI applications generate a large volume of audit logs during operation, but there is a lack of effective context correlation and vulnerability discovery methods. This makes it difficult to promptly detect security incidents such as lateral movement and privilege abuse. Traditional security tools often focus only on the system call level and cannot understand the context relationships and behavioral patterns of AI agents.

Usage is straightforward. You simply need to install agent-bom via pipx, then invoke three core tools using natural language instructions: context_graph for building agent context graphs and analyzing lateral movement paths, runtime_correlate for cross-correlating runtime audit logs with CVE discoveries, and analytics_query for querying vulnerability trends, historical states, and runtime events. After that, you can gain security insights with simple commands like 'analyze audit logs from the last hour'.

It is well-suited for teams or individuals who need to monitor the runtime security of AI agents, especially operations and security engineers who have already deployed agent applications in production environments and require continuous auditing of their behavioral security. For AI application developers, it can also help identify potential security vulnerabilities during the development phase, ensuring the integrity and trustworthiness of AI systems before deployment.

It is recommended to uniformly integrate this tool when deploying AI agents, incorporating it into CI/CD pipelines for routine security audits. Note that it primarily relies on user-provided audit log files (in JSONL format) and does not automatically scan networks or file systems, offering strong privacy protection. Optional integration with ClickHouse enables persistent analysis but is not mandatory.