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LLMs can identify flagged security issues but struggle to uncover silent intrusions and create effective remediation plans, revealing a critical gap in their utility for real-world incident response.
MASTE achieves zero-shot Aspect Sentiment Triplet Extraction with a multi-agent approach that outperforms traditional LLM methods, even without labeled data.
