Thanks for sharing that.
What the article is about
This Malcat blog post discusses using large language models for two security-related tasks:
- Malware triage
- Static unpacking assistance
In general, that is an interesting direction because LLMs can help with:
- Summarizing analyst findings
- Explaining assembly or code patterns in plain language
- Speeding up repetitive documentation work
Important limitation
LLM benchmarking claims should be treated carefully.
For malware analysis, performance depends heavily on:
- The exact prompts used
- The quality and format of the input data
- Whether the model is reasoning from real artifacts or from a simplified text description
- How the results were scored
- Whether the task required factual accuracy or just plausible-sounding output
An LLM can produce useful analysis text while still being wrong on key technical details. That matters a lot in malware triage, where a confident but incorrect conclusion can waste analyst time.
Security perspective
For practical use, I would see LLMs as an assistant layer, not a decision-maker.
A cautious workflow would be:
- Use the model to summarize indicators, strings, imports, and behavioral hints
- Verify the claims manually in the sample, debugger, disassembler, or sandbox output
- Do not rely on the model alone to classify a sample as malicious or benign
That is especially true for unpacking. Static unpacking often depends on very specific packer behavior, PE structure details, stubs, section anomalies, and reconstruction steps that need direct technical validation.
Bottom line
The post is worth reading as a research and tooling discussion, but I would treat it as evidence that LLMs may assist analysts, not that they can reliably replace traditional malware analysis workflows.
Benchmarking LLMs for malware triage and static unpacking with Malcat
malcat.fr
