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AI Research
arXiv CS.AI·27d ago·5 min read
Large Language Models (LLMs) and Generative AI in Cybersecurity and Privacy: A Survey of Dual-Use Risks, AI-Generated Malware, Explainability, and Defensive Strategies
TL;DR
Large Language Models (LLMs) pose dual-use risks in cybersecurity, enabling both innovative defenses and the creation of AI-generated malware. This survey explores the implications of LLMs in security contexts, focusing on explainability and defensive strategies.
✦ Why It Matters
Engineers should implement explainability frameworks in AI systems to enhance transparency and mitigate cybersecurity risks.
Key Takeaways
- Adopt explainability frameworks for AI systems to improve transparency.
- Develop guidelines for ethical use of LLMs in cybersecurity.
- Monitor AI-generated threats to stay ahead of potential attacks.
- Collaborate with interdisciplinary teams to address dual-use risks.
Full Summary
The rise of Large Language Models (LLMs) has introduced significant dual-use risks in cybersecurity, where these models can be employed for both protective measures and malicious activities, such as generating malware. This survey examines the landscape of AI-generated threats, emphasizing the importance of explainability in AI systems to ensure transparency and trust.
Methodologically, the research reviews existing literature and case studies to identify patterns and potential vulnerabilities associated with LLMs. Findings indicate that while LLMs can enhance security protocols, they also facilitate sophisticated cyberattacks, necessitating the development of comprehensive defensive strategies.
The implications for engineers and researchers are profound, as they must balance innovation with ethical considerations and risk management. The study advocates for the establishment of guidelines and tools to navigate these challenges effectively.