Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·11d ago·5 min read
APTInvestBench: Evaluating Autonomous APT Investigation under Varying Telemetry
TL;DR
APTInvestBench is a benchmark designed to evaluate the effectiveness of large language model (LLM) agents in investigating advanced persistent threats (APTs) across different telemetry settings. It reveals that while agents can gather evidence for 44.3% of attack actions, formal citation support is significantly lower at 25.0%, highlighting potential gaps in investigation reliability.
✦ Why It Matters
Evaluate your current APT investigation processes against APTInvestBench to identify and address evidence gaps.
Key Takeaways
- Utilize APTInvestBench to benchmark your APT investigation capabilities.
- Assess the impact of telemetry changes on evidence collection.
- Focus on improving formal citation support in investigations.
- Identify gaps in your current APT response strategies.
How It Works
APTInvestBench evaluates LLM agents by simulating various telemetry conditions that affect log collection and retention. It tracks how well agents can convert weak leads into actionable evidence, requiring them to provide formal citations for their findings.
This structured approach allows for a clear assessment of the agents' performance across different scenarios.