The integration of Large Language Models (LLMs) into malware detection tools has created significant excitement.However, are they truly ready to replace traditional methods? Black Hat USA 2025 provided a clear eyed evaluation of their actual performance in real-world cybersecurity.
Here’s what the experts revealed.
Capabilities of LLMs
- Explain Behavior: Translate disassembled code into plain English summaries.
- Speed Up Triage: Help analysts prioritize threats faster.
- Assist SOC Teams: Act like copilots, suggesting responses and flagging anomalies.
- Code Classification: Classify scripts or binaries into malware families with moderate accuracy.
Where LLMs Fall Short
- Accuracy Gaps: Prone to hallucinations and overgeneralizations.
- Limited Context: Can’t understand complex execution flows or runtime behavior.
- Bypassable: Attackers are using LLMs to create stealthier malware.
- No Replacement for Sandboxing: Traditional tools still catch what LLMs miss.
Conclusion from Black Hat 2025
LLMs are powerful aids, but not replacements.
They work best when enhancing human decision-making, not standing alone.
The future of cybersecurity will be collaboration between AI and experts.
The hype is real, but caution is key.
