arXiv:2410.15236v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have transformed artificial intelligence by advancing natural language understanding and generation, enabling applications across fields beyond healthcare, software engineering, and conversational systems. Despite these advancements in the past few years, LLMs have shown considerable vulnerabilities, particularly to prompt injection and jailbreaking attacks. This review analyzes the state of research on these vulnerabilities and presents available defense strategies. We roughly categorize attack approaches i
The rapid deployment and increasing sophistication of LLMs in critical applications necessitate immediate focus on understanding and mitigating their inherent vulnerabilities, as these models are still relatively new and evolving.
The widespread adoption of LLMs across sensitive sectors like healthcare and software engineering makes their security vulnerabilities a critical issue for data integrity, privacy, and system robustness.
This research consolidates understanding of LLM jailbreaking and mitigation, providing a clearer roadmap for developers and policymakers to build more secure AI systems, thus shifting focus to defense mechanisms.
- · AI security researchers
- · Enterprises deploying LLMs
- · Cybersecurity firms
- · Malicious actors
- · LLM providers with insecure models
- · Organizations neglecting AI security
Increased investment in AI security research and development to address identified vulnerabilities.
Development of industry standards and regulations for LLM security and ethical deployment.
A potential slowdown in LLM adoption in highly sensitive areas until robust mitigation strategies are widely implemented and proven.
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Read at arXiv cs.LG