New framework MalEval assesses LLMs for malware auditing capabilities

A new research paper introduces MalEval, a framework designed to evaluate the capabilities of Large Language Models (LLMs) in auditing malware behavior. The framework addresses challenges such as the lack of human-written ground truth, context window limitations, and the difficulty of verifying LLM-generated claims against code evidence. MalEval uses expert-written audit reports and a context-driven intermediate representation to enable stage-wise verification of LLM outputs, revealing that current models often rely on superficial cues rather than verifiable evidence. AI

IMPACT This framework could improve the reliability and verifiability of LLM-driven security analysis tools.

RANK_REASON The cluster contains a research paper detailing a new evaluation framework for LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Large Language Models
• LLMs
• Xinran Zheng

• paper
• safety
• product

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →