arXiv:2606.02423v1 Announce Type: new Abstract: Large language models (LLMs) can serve as helpful assistants, yet they can equally function as harm amplifiers that enable malicious users to achieve harmful outcomes beyond their capabilities through extended interactions. This risk manifests along two axes, i.e., democratizing domain expertise that allows novices to produce specialized harmful content, and scaling harmful operations at volumes that manual effort cannot match. Existing works, however, often overlook how LLMs compound harm across multi-turn conversations. We introduce HarmAmp, a
The proliferation of advanced LLMs and their growing integration into various applications makes understanding and mitigating their harmful potential an immediate research and development priority.
A strategic reader should care about the amplified harm potential of LLMs because it represents a significant and evolving risk vector, impacting digital security, content moderation, and societal stability.
The focus expands from basic LLM safety to understanding and counteracting how these models can actively scale and democratize harmful expertize in multi-turn interactions, necessitating new defensive paradigms.
- · AI safety researchers
- · Cybersecurity firms
- · Content moderation platforms
- · Ethical AI developers
- · Malicious actors without LLM access
- · Platforms with weak moderation
- · Organizations vulnerable to misinformation at scale
- · Unsecured AI system developers
Increased focus on robust AI safety protocols and mitigation strategies for LLM interactions.
Development of regulatory frameworks specifically targeting LLM-enabled harm amplification and misuse.
Shift in AI development towards inherently safer architectures, potentially impacting speed of innovation in some areas.
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