Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 31·5 min read
LLM-Based Agents for Software and Systems Security: Approaches, Applications, and Assessment
TL;DR
Large language model (LLM)-based agents are being developed to automate software and systems security workflows, but their authority and behavior remain unbounded and unauditable. A systematic review highlights the current state of these agents, their applications, and assessment challenges.
✦ Why It Matters
Evaluate your current security workflows to identify areas where LLM-based agents could be integrated for automation.
Key Takeaways
- Explore LLM-based agents for automating security workflows.
- Assess the authority and auditability of deployed agents.
- Identify specific security tasks suitable for LLM applications.
- Stay informed about emerging research in LLM security applications.
How It Works
LLM-based agents utilize architectures that incorporate perception, memory, reasoning, and planning to automate multi-step security workflows. They can retain state across tasks and adapt their actions based on previous outcomes, allowing for a more dynamic response to security challenges.