1. Malware Reverse Engineering

LLMs can assist in static analysis tasks such as:

• Deobfuscating code snippets
• Explaining assembly or bytecode instructions
• Suggesting behavioral patterns based on known malware families

However, reverse engineering is highly technical and often requires dynamic analysis, sandboxing, and binary-level inspection—areas where LLMs currently lack direct capability. They can support analysts by:

• Summarizing disassembled code
• Generating hypotheses about malware behavior
• Automating documentation

But they cannot replace traditional reverse engineering tools like IDA Pro, Ghidra, or dynamic analysis environments.

2. Automated Threat Report Generation

This is where LLMs shine more reliably. They can:

• Summarize threat intelligence feeds
• Generate readable reports from structured indicators (IOCs, TTPs)
• Translate technical findings into executive summaries
• Correlate threat actor behaviors across datasets

Models like BioBERT (trained on biomedical corpora) are less suited for cyber threat tasks unless fine-tuned on cybersecurity data. In contrast, GPT models fine-tuned on threat intelligence corpora (e.g., MITRE ATT&CK, CVE databases, malware reports) can produce high-quality outputs.

3. Limitations and Risks

• Hallucinations: LLMs may generate plausible but incorrect threat details.
• Lack of real-time awareness: Without integration into live feeds, they can't detect emerging threats.
• Security risks: Improper use could expose sensitive data or generate misleading intelligence.

4. Emerging Use Cases

• SOC automation: Triage alerts and generate incident summaries.
• Threat actor profiling: Extract patterns from historical reports.
• Phishing detection: Analyze email content and metadata.